base changes

.gitattributes

@@ -33,4 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-*.mp4 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,6 +1 @@
-transactify_venv
-tokenizer.joblib
-label_encoder.joblib
-transactify.h5
-venv
-.venv
+venv

About.md

@@ -1,64 +0,0 @@
-Abstract for Transactify......
-
-Transactify is an LSTM-based model designed to predict the category of online payment transactions from their descriptions. 
-By analyzing textual inputs like "Live concert stream on YouTube" or "Coffee at Starbucks," it classifies transactions into categories such as "Movies & Entertainment" or "Food & Dining." 
-This model helps users track and organize their spending across various sectors, providing better financial insights and budgeting. 
-Transactify is trained on real-world transaction data for improved accuracy and generalization.
-
-Table of contents....
-
-1.Data Collection:
-The dataset consists of 5,000 transaction records generated using ChatGPT, each containing a transaction description and its corresponding category. 
-Example entries include descriptions like "Live concert stream on YouTube" (Movies & Entertainment) and "Coffee at Starbucks" (Food & Dining). 
-These records cover various spending categories such as Lifestyle, Movies & Entertainment, Food & Dining, and others.
-
-
-2.Data Preprocessing:
-The preprocessing step involves several natural language processing (NLP) tasks to clean and prepare the text data for model training. 
-These include:
-Lowercasing all text.
-Removing digits and punctuation using regular expressions (regex).
-Tokenizing the cleaned text to convert it into a sequence of tokens.
-Applying text_to_sequences to transform the tokenized words into numerical sequences.
-Using pad_sequences to ensure all sequences have the same length for input into the LSTM model.
-Label encoding the target categories to convert them into numerical labels.
-After preprocessing, the data is split into training and testing sets to build and validate the model.
-
-
-
-3.Model Building:
-Embedding Layer: Converts tokenized transaction descriptions into dense vectors, capturing word semantics and relationships.
-
-LSTM Layer: Learns sequential patterns from the embedded text, helping the model understand the context and relationships between words over time.
-
-Dropout Layer: Introduces regularization by randomly turning off neurons during training, reducing overfitting and improving the model's generalization.
-
-Dense Layer with Softmax Activation: Outputs a probability distribution across categories, allowing the model to predict the correct category for each transaction description.
-
-Model Compilation: Compiled with the Adam optimizer for efficient learning, sparse categorical cross-entropy loss for multi-class classification, and accuracy as the evaluation metric.
-
-Model Training: The model is trained for 50 epochs with a batch size of 8, using a validation set to monitor performance and adjust during training.
-
-Saving the Model and Preprocessing Objects:
-
-The trained model is saved as transactify.h5 for future use.
-The tokenizer and label encoder used during preprocessing are saved using joblib as tokenizer.joblib and label_encoder.joblib, respectively, 
-ensuring they can be reused for consistent tokenization and label encoding when making predictions on new data.
-
-
-
-4.Prediction:
-Once trained, the model is used to predict the category of new transaction descriptions. 
-The output provides the category label, enabling users to classify their spending based on transaction descriptions.
-
-
-
-5.Conclusion:
-The Transactify model effectively categorizes transaction descriptions using LSTM networks. 
-However, to improve the accuracy and reliability of predictions, a larger and more diverse dataset is necessary. 
-Expanding the dataset will help the model generalize better across various spending behaviors and conditions. 
-This enhancement will lead to more precise predictions, enabling users to gain deeper insights into their spending patterns. 
-Future work should focus on collecting additional data to refine the model's performance and applicability in real-world scenarios.
-
-
-![Excepted Output:](result.gif)

LSTM_model.py

@@ -1,62 +0,0 @@
-# LSTM_model.py
-import numpy as np
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout
-from data_preprocessing import preprocess_data, split_data
-import joblib  # To save the tokenizer and label encoder
-
-# Define the LSTM model
-def build_lstm_model(vocab_size, embedding_dim=64, max_len=10, lstm_units=128, dropout_rate=0.2, output_units=6):
-    model = Sequential()
-    model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_len))
-    model.add(LSTM(units=lstm_units, return_sequences=False))
-    model.add(Dropout(dropout_rate))
-    model.add(Dense(units=output_units, activation='softmax'))
-    
-    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-    
-    return model
-
-# Main function to execute the training process
-def main():
-    # Path to your data file
-    data_path = r"E:\transactify\transactify\transactify\transactify\transactify\data_set\transaction_data.csv"
-
-    # Preprocess the data
-    sequences, labels, tokenizer, label_encoder = preprocess_data(data_path)
-
-    # Check if preprocessing succeeded
-    if sequences is not None:
-        print("Data preprocessing successful!")
-        
-        # Split the data into training and testing sets
-        X_train, X_test, y_train, y_test = split_data(sequences, labels)
-        print(f"Training data shape: {X_train.shape}, Training labels shape: {y_train.shape}")
-        print(f"Testing data shape: {X_test.shape}, Testing labels shape: {y_test.shape}")
-
-        # Build the LSTM model
-        vocab_size = tokenizer.num_words + 1  # +1 for padding token
-        model = build_lstm_model(vocab_size, max_len=10, output_units=len(label_encoder.classes_))
-
-        # Train the model
-        model.fit(X_train, y_train, epochs=50, batch_size=8, validation_data=(X_test, y_test))
-
-        # Evaluate the model
-        loss, accuracy = model.evaluate(X_test, y_test)
-        print(f"Test Loss: {loss:.4f}, Test Accuracy: {accuracy:.4f}")
-
-        # Save the model
-        model.save('transactify.h5')
-        print("Model saved as 'transactify.h5'")
-
-        # Save the tokenizer and label encoder
-        joblib.dump(tokenizer, 'tokenizer.joblib')
-        joblib.dump(label_encoder, 'label_encoder.joblib')
-        print("Tokenizer and LabelEncoder saved as 'tokenizer.joblib' and 'label_encoder.joblib'")
-
-    else:
-        print("Data preprocessing failed.")
-
-# Execute the main function
-if __name__ == "__main__":
-    main()

README.md

@@ -1,63 +1,14 @@
----
-license: mit
-language:
-- en
----
-
-## What is Transactify?
-Transactify is an LSTM-based model designed to predict the category of online payment transactions from their descriptions. 
-By analyzing textual inputs like "Live concert stream on YouTube" or "Coffee at Starbucks," it classifies transactions into categories such as "Movies & Entertainment" or "Food & Dining." 
-This model helps users track and organize their spending across various sectors, providing better financial insights and budgeting. 
-Transactify is trained on real-world transaction data for improved accuracy and generalization.
-
-## Table of contents
-## 1. Data Collection
-The dataset consists of **5,000 transaction records** generated using ChatGPT, each containing a transaction description and its corresponding category. 
-
-Example entries include:
-- "Live concert stream on YouTube" (Movies & Entertainment)
-- "Coffee at Starbucks" (Food & Dining)
-
-These records cover various spending categories such as **Lifestyle**, **Movies & Entertainment**, **Food & Dining**, and others.
-
----
-
-## 2. Data Preprocessing
-The preprocessing step involves several natural language processing (NLP) tasks to clean and prepare the text data for model training. These include:
-
-- Lowercasing all text.
-- Removing digits and punctuation using regular expressions (regex).
-- Tokenizing the cleaned text to convert it into a sequence of tokens.
-- Applying `text_to_sequences` to transform the tokenized words into numerical sequences.
-- Using `pad_sequences` to ensure all sequences have the same length for input into the LSTM model.
-- Label encoding the target categories to convert them into numerical labels.
-
-After preprocessing, the data is split into training and testing sets to build and validate the model.
-
----
-
-## 3. Model Building
-- **Embedding Layer**: Converts tokenized transaction descriptions into dense vectors, capturing word semantics and relationships.
-  
-- **LSTM Layer**: Learns sequential patterns from the embedded text, helping the model understand the context and relationships between words over time.
-
-- **Dropout Layer**: Introduces regularization by randomly turning off neurons during training, reducing overfitting and improving the model's generalization.
-
-- **Dense Layer with Softmax Activation**: Outputs a probability distribution across categories, allowing the model to predict the correct category for each transaction description.
-
-### Model Compilation
-- Compiled with the Adam optimizer for efficient learning.
-- Sparse categorical cross-entropy loss for multi-class classification.
-- Accuracy as the evaluation metric.
-
-### Model Training
-The model is trained for **50 epochs** with a batch size of **8**, using a validation set to monitor performance and adjust during training.
-
-### Saving the Model and Preprocessing Objects
-- The trained model is saved as `transactify.h5` for future use.
-- The tokenizer and label encoder used during preprocessing are saved using joblib as `tokenizer.joblib` and `label_encoder.joblib`, respectively, ensuring they can be reused for consistent tokenization and label encoding when making predictions on new data.
-
----
-
-## 4. Prediction
-Once trained
+Transactify:
+
+To create an Virtual Environment: open command prompt  Type ---> python -m venv transactify
+
+To activate the virtual Environment  Type ---> cd transactify
+                                          ---> cd scripts
+                                          ---> activate
+
+To install Required Libaries in the virtual Environment  Type ---> pip install -r requirements.txt
+
+To run the python file  Type  ----> python main.py  
+
+
+Excepted output: Hello World from Hugging Face!

config.json

@@ -1,35 +0,0 @@
-{
-  "model_type": "custom",
-  "architectures": ["LSTM"],
-  "library_name": "tensorflow",
-  "task_specific_params": {
-    "text-classification": {
-      "vocab_size": 500,
-      "embedding_dim": 64,
-      "hidden_size": 64,
-      "num_layers": 2,
-      "dropout_rate": 0.2,
-      "max_sequence_length": 10
-    }
-  },
-  "training_params": {
-    "batch_size": 8,
-    "epochs": 50,
-    "loss_function": "sparse_categorical_crossentropy",
-    "optimizer": "adam",
-    "metrics": ["accuracy"]
-  },
-  "train_data_size": 5000,
-  "id2label": {
-    "0": "Lifestyle",
-    "1": "Movies & Entertainment",
-    "2": "Food & Dining",
-    "3": "Others"
-  },
-  "label2id": {
-    "Lifestyle": 0,
-    "Movies & Entertainment": 1,
-    "Food & Dining": 2,
-    "Others": 3
-  }
-}

data_preprocessing.py

@@ -1,83 +0,0 @@
-# data_preprocessing.py
-import numpy as np
-import pandas as pd
-import re
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import train_test_split
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-
-# Read the data
-def read_data(path):
-    try:
-        df = pd.read_csv(path)
-        if df.empty:
-            print("The file is empty.")
-            return None
-        return df
-    except FileNotFoundError:
-        print(f"File not found at: {path}")
-        return None
-    except Exception as e:
-        print(f"An error occurred: {e}")
-        return None
-
-# Cleaning the text
-def clean_text(text):
-    text = text.lower()                    # Convert uppercase to lowercase
-    text = re.sub(r"\d+", " ", text)       # Remove digits
-    text = re.sub(r"[^\w\s]", " ", text)   # Remove punctuations
-    text = text.strip()                    # Remove extra spaces
-    return text
-
-# Main preprocessing function
-def preprocess_data(file_path, max_len=10, vocab_size=250):
-    # Read the data
-    df = read_data(file_path)
-    if df is None:
-        print("Data loading failed.")
-        return None, None, None, None
-
-    # Clean the text
-    df['Transaction Description'] = df['Transaction Description'].apply(clean_text)
-    
-    # Initialize the tokenizer
-    tokenizer = Tokenizer(num_words=vocab_size, oov_token="<OOV>")
-    tokenizer.fit_on_texts(df['Transaction Description'])
-    
-    # Convert texts to sequences and pad them
-    sequences = tokenizer.texts_to_sequences(df['Transaction Description'])
-    padded_sequences = pad_sequences(sequences, maxlen=max_len, padding='post', truncating='post')
-    
-    # Initialize LabelEncoder and encode the labels
-    label_encoder = LabelEncoder()
-    labels = label_encoder.fit_transform(df['Category'])
-    
-    return padded_sequences, labels, tokenizer, label_encoder
-
-# Train-test split function
-def split_data(sequences, labels, test_size=0.2, random_state=42):
-    X_train, X_test, y_train, y_test = train_test_split(sequences, labels, test_size=test_size, random_state=random_state)
-    return X_train, X_test, y_train, y_test
-
-# Main function to execute preprocessing
-def main():
-    # Path to your data file
-    data_path = r"E:\transactify\transactify\Dataset\transaction_data.csv"
-
-    # Preprocess the data
-    sequences, labels, tokenizer, label_encoder = preprocess_data(data_path)
-
-    # Check if preprocessing succeeded
-    if sequences is not None:
-        print("Data preprocessing successful!")
-        # Split the data into training and testing sets
-        X_train, X_test, y_train, y_test = split_data(sequences, labels)
-        print(f"Training data shape: {X_train.shape}, Training labels shape: {y_train.shape}")
-        print(f"Testing data shape: {X_test.shape}, Testing labels shape: {y_test.shape}")
-    else:
-        print("Data preprocessing failed.")
-
-# Execute the main function
-if __name__ == "__main__":
-    main()

main.py

@@ -1,57 +1,6 @@
-# main.py
-import numpy as np
-import pandas as pd
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import joblib
-import re
+# hello_world.py
+def hello_world():
+    print("Hello World from Hugging Face!")
 
-# Function to clean the input text
-def clean_text(text):
-    text = text.lower()                    
-    text = re.sub(r"\d+", " ", text)      
-    text = re.sub(r"[^\w\s]", " ", text)  
-    text = text.strip()                    
-    return text
-
-# Load the model, tokenizer, and label encoder
-def load_resources(model_path, tokenizer_path, label_encoder_path):
-    model = load_model(model_path)
-    tokenizer = joblib.load(tokenizer_path)
-    label_encoder = joblib.load(label_encoder_path)
-    return model, tokenizer, label_encoder
-
-# Function to make predictions
-def predict(model, tokenizer, label_encoder, input_text, max_len=50):
-    cleaned_text = clean_text(input_text)
-    sequence = tokenizer.texts_to_sequences([cleaned_text])
-    padded_sequence = pad_sequences(sequence, maxlen=max_len, padding='post', truncating='post')
-    
-    # Make prediction
-    prediction = model.predict(padded_sequence)
-    predicted_class = np.argmax(prediction, axis=1)
-    
-    # Decode the label
-    predicted_label = label_encoder.inverse_transform(predicted_class)
-    
-    return predicted_label[0]
-
-# Main function for running predictions
-def main():
-    # Paths to your resources
-    model_path = 'transactify.h5'  # Update with the correct path if needed
-    tokenizer_path = 'tokenizer.joblib'  # Update with the correct path if needed
-    label_encoder_path = 'label_encoder.joblib'  # Update with the correct path if needed
-
-    # Load resources
-    model, tokenizer, label_encoder = load_resources(model_path, tokenizer_path, label_encoder_path)
-
-    # Input for prediction
-    input_text = input("Enter a transaction description for prediction: ")
-    predicted_category = predict(model, tokenizer, label_encoder, input_text)
-    print(f"The predicted category is: {predicted_category}")
-
-# Execute the main function
 if __name__ == "__main__":
-    main()
+    hello_world()

model.py

@@ -1,25 +0,0 @@
-from tensorflow.keras.models import load_model
-import joblib
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import numpy as np
-import re
-
-# Load the model, tokenizer, and label encoder
-model = load_model("transactify.h5")
-tokenizer = joblib.load("tokenizer.joblib")
-label_encoder = joblib.load("label_encoder.joblib")
-
-def clean_text(text):
-    text = text.lower()
-    text = re.sub(r"\d+", "", text)
-    text = re.sub(r"[^\w\s]", "", text)
-    return text.strip()
-
-def predict(text):
-    cleaned_text = clean_text(text)
-    sequence = tokenizer.texts_to_sequences([cleaned_text])
-    padded_sequence = pad_sequences(sequence, maxlen=100)
-    prediction = model.predict(padded_sequence)
-    predicted_label = np.argmax(prediction, axis=1)
-    category = label_encoder.inverse_transform(predicted_label)
-    return {"category": category[0]}

requirements.txt

@@ -1,4 +0,0 @@
-numpy
-pandas
-tensorflow
-scikit-learn

setup.md

@@ -1,53 +1,52 @@
+## Install Git LFS
+```
+brew install git-lfs
+```
+or download from https://git-lfs.github.com/
 
-# Steps to Run the Model
-
-1. **Clone the Repository**:  
-   Open your command line interface (CLI) and clone the repository using:
-   ```bash
-   git clone https://huggingface.co/webslate/transactify
-   ```
-
-2. **Create the Virtual Environment**:  
-   Navigate to the project directory and create a virtual environment:
-   ```bash
-   python -m venv transactify_venv
-   ```
-
-3. **Activate the Virtual Environment**:  
-   To activate the virtual environment, follow these steps:
-   - Open your command line interface (CLI).
-   - Type the following commands:
-     ```bash
-     cd transactify_venv
-     cd Scripts
-     activate
-     ```
-
-4. **Install Required Libraries**:  
-   After activating the virtual environment, install the necessary libraries by typing:
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-5. **Run the Data Preprocessing Code**:  
-   Execute the data preprocessing script by typing:
-   ```bash
-   python data_preprocessing.py
-   ```
-
-6. **Run the LSTM Model Code**:  
-   Train the LSTM model by executing:
-   ```bash
-   python LSTM_model.py
-   ```
-
-7. **Generate the H5 File**:  
-   After training, you can generate the model file (`transactify.h5`).
-
-8. **Run the Prediction Code**:  
-   To make predictions using the trained model, type:
-   ```bash
-   python main.py
-   ```
-
-Following these steps will set up and run the Transactify model for predicting transaction categories based on descriptions.
+## Update global git config
+```
+$ git lfs install
+```
+
+## Update system git config
+```
+$ git lfs install --system
+```
+
+## Clone the Repo
+
+### Entire Clone
+```
+git clone https://huggingface.co/webslate/transactify
+```
+
+### Light Clone
+```
+GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/webslate/transactify
+```
+
+## For Pushing the Code
+
+> Refer to https://huggingface.co/blog/password-git-deprecation
+
+### Set the Remote URL
+```
+$: git remote set-url origin https://<user_name>:<token>@huggingface.co/<repo_path>
+```
+### Token Creation
+
+> Go to Settings > Access Tokens > Create new token >
+Choose Write Tab (3rd one) / go here https://huggingface.co/settings/tokens/new?tokenType=write
+
+
+## Create Virtual Environment
+
+```
+python3 -m venv venv
+source venv/bin/activate
+```
+
+## FYI
+
+> We initially started with `pip install transformers datasets`

transactify/Lib/site-packages/pip-24.0.dist-info/AUTHORS.txt

@@ -0,0 +1,760 @@
+@Switch01
+A_Rog
+Aakanksha Agrawal
+Abhinav Sagar
+ABHYUDAY PRATAP SINGH
+abs51295
+AceGentile
+Adam Chainz
+Adam Tse
+Adam Wentz
+admin
+Adrien Morison
+ahayrapetyan
+Ahilya
+AinsworthK
+Akash Srivastava
+Alan Yee
+Albert Tugushev
+Albert-Guan
+albertg
+Alberto Sottile
+Aleks Bunin
+Ales Erjavec
+Alethea Flowers
+Alex Gaynor
+Alex Grönholm
+Alex Hedges
+Alex Loosley
+Alex Morega
+Alex Stachowiak
+Alexander Shtyrov
+Alexandre Conrad
+Alexey Popravka
+Aleš Erjavec
+Alli
+Ami Fischman
+Ananya Maiti
+Anatoly Techtonik
+Anders Kaseorg
+Andre Aguiar
+Andreas Lutro
+Andrei Geacar
+Andrew Gaul
+Andrew Shymanel
+Andrey Bienkowski
+Andrey Bulgakov
+Andrés Delfino
+Andy Freeland
+Andy Kluger
+Ani Hayrapetyan
+Aniruddha Basak
+Anish Tambe
+Anrs Hu
+Anthony Sottile
+Antoine Musso
+Anton Ovchinnikov
+Anton Patrushev
+Antonio Alvarado Hernandez
+Antony Lee
+Antti Kaihola
+Anubhav Patel
+Anudit Nagar
+Anuj Godase
+AQNOUCH Mohammed
+AraHaan
+Arindam Choudhury
+Armin Ronacher
+Artem
+Arun Babu Neelicattu
+Ashley Manton
+Ashwin Ramaswami
+atse
+Atsushi Odagiri
+Avinash Karhana
+Avner Cohen
+Awit (Ah-Wit) Ghirmai
+Baptiste Mispelon
+Barney Gale
+barneygale
+Bartek Ogryczak
+Bastian Venthur
+Ben Bodenmiller
+Ben Darnell
+Ben Hoyt
+Ben Mares
+Ben Rosser
+Bence Nagy
+Benjamin Peterson
+Benjamin VanEvery
+Benoit Pierre
+Berker Peksag
+Bernard
+Bernard Tyers
+Bernardo B. Marques
+Bernhard M. Wiedemann
+Bertil Hatt
+Bhavam Vidyarthi
+Blazej Michalik
+Bogdan Opanchuk
+BorisZZZ
+Brad Erickson
+Bradley Ayers
+Brandon L. Reiss
+Brandt Bucher
+Brett Randall
+Brett Rosen
+Brian Cristante
+Brian Rosner
+briantracy
+BrownTruck
+Bruno Oliveira
+Bruno Renié
+Bruno S
+Bstrdsmkr
+Buck Golemon
+burrows
+Bussonnier Matthias
+bwoodsend
+c22
+Caleb Martinez
+Calvin Smith
+Carl Meyer
+Carlos Liam
+Carol Willing
+Carter Thayer
+Cass
+Chandrasekhar Atina
+Chih-Hsuan Yen
+Chris Brinker
+Chris Hunt
+Chris Jerdonek
+Chris Kuehl
+Chris McDonough
+Chris Pawley
+Chris Pryer
+Chris Wolfe
+Christian Clauss
+Christian Heimes
+Christian Oudard
+Christoph Reiter
+Christopher Hunt
+Christopher Snyder
+cjc7373
+Clark Boylan
+Claudio Jolowicz
+Clay McClure
+Cody
+Cody Soyland
+Colin Watson
+Collin Anderson
+Connor Osborn
+Cooper Lees
+Cooper Ry Lees
+Cory Benfield
+Cory Wright
+Craig Kerstiens
+Cristian Sorinel
+Cristina
+Cristina Muñoz
+Curtis Doty
+cytolentino
+Daan De Meyer
+Dale
+Damian
+Damian Quiroga
+Damian Shaw
+Dan Black
+Dan Savilonis
+Dan Sully
+Dane Hillard
+daniel
+Daniel Collins
+Daniel Hahler
+Daniel Holth
+Daniel Jost
+Daniel Katz
+Daniel Shaulov
+Daniele Esposti
+Daniele Nicolodi
+Daniele Procida
+Daniil Konovalenko
+Danny Hermes
+Danny McClanahan
+Darren Kavanagh
+Dav Clark
+Dave Abrahams
+Dave Jones
+David Aguilar
+David Black
+David Bordeynik
+David Caro
+David D Lowe
+David Evans
+David Hewitt
+David Linke
+David Poggi
+David Pursehouse
+David Runge
+David Tucker
+David Wales
+Davidovich
+ddelange
+Deepak Sharma
+Deepyaman Datta
+Denise Yu
+dependabot[bot]
+derwolfe
+Desetude
+Devesh Kumar Singh
+Diego Caraballo
+Diego Ramirez
+DiegoCaraballo
+Dimitri Merejkowsky
+Dimitri Papadopoulos
+Dirk Stolle
+Dmitry Gladkov
+Dmitry Volodin
+Domen Kožar
+Dominic Davis-Foster
+Donald Stufft
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+pip

transactify/Lib/site-packages/pip-24.0.dist-info/LICENSE.txt

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+Copyright (c) 2008-present The pip developers (see AUTHORS.txt file)
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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+Metadata-Version: 2.1
+Name: pip
+Version: 24.0
+Summary: The PyPA recommended tool for installing Python packages.
+Author-email: The pip developers <[email protected]>
+License: MIT
+Project-URL: Homepage, https://pip.pypa.io/
+Project-URL: Documentation, https://pip.pypa.io
+Project-URL: Source, https://github.com/pypa/pip
+Project-URL: Changelog, https://pip.pypa.io/en/stable/news/
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Topic :: Software Development :: Build Tools
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE.txt
+License-File: AUTHORS.txt
+
+pip - The Python Package Installer
+==================================
+
+.. image:: https://img.shields.io/pypi/v/pip.svg
+   :target: https://pypi.org/project/pip/
+   :alt: PyPI
+
+.. image:: https://img.shields.io/pypi/pyversions/pip
+   :target: https://pypi.org/project/pip
+   :alt: PyPI - Python Version
+
+.. image:: https://readthedocs.org/projects/pip/badge/?version=latest
+   :target: https://pip.pypa.io/en/latest
+   :alt: Documentation
+
+pip is the `package installer`_ for Python. You can use pip to install packages from the `Python Package Index`_ and other indexes.
+
+Please take a look at our documentation for how to install and use pip:
+
+* `Installation`_
+* `Usage`_
+
+We release updates regularly, with a new version every 3 months. Find more details in our documentation:
+
+* `Release notes`_
+* `Release process`_
+
+If you find bugs, need help, or want to talk to the developers, please use our mailing lists or chat rooms:
+
+* `Issue tracking`_
+* `Discourse channel`_
+* `User IRC`_
+
+If you want to get involved head over to GitHub to get the source code, look at our development documentation and feel free to jump on the developer mailing lists and chat rooms:
+
+* `GitHub page`_
+* `Development documentation`_
+* `Development IRC`_
+
+Code of Conduct
+---------------
+
+Everyone interacting in the pip project's codebases, issue trackers, chat
+rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_.
+
+.. _package installer: https://packaging.python.org/guides/tool-recommendations/
+.. _Python Package Index: https://pypi.org
+.. _Installation: https://pip.pypa.io/en/stable/installation/
+.. _Usage: https://pip.pypa.io/en/stable/
+.. _Release notes: https://pip.pypa.io/en/stable/news.html
+.. _Release process: https://pip.pypa.io/en/latest/development/release-process/
+.. _GitHub page: https://github.com/pypa/pip
+.. _Development documentation: https://pip.pypa.io/en/latest/development
+.. _Issue tracking: https://github.com/pypa/pip/issues
+.. _Discourse channel: https://discuss.python.org/c/packaging
+.. _User IRC: https://kiwiirc.com/nextclient/#ircs://irc.libera.chat:+6697/pypa
+.. _Development IRC: https://kiwiirc.com/nextclient/#ircs://irc.libera.chat:+6697/pypa-dev
+.. _PSF Code of Conduct: https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md

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transactify/Lib/site-packages/pip-24.0.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.42.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

transactify/Lib/site-packages/pip-24.0.dist-info/entry_points.txt

@@ -0,0 +1,4 @@
+[console_scripts]
+pip = pip._internal.cli.main:main
+pip3 = pip._internal.cli.main:main
+pip3.10 = pip._internal.cli.main:main

transactify/Lib/site-packages/pip-24.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+pip

transactify/Lib/site-packages/pip/__init__.py

@@ -0,0 +1,13 @@
+from typing import List, Optional
+
+__version__ = "24.0"
+
+
+def main(args: Optional[List[str]] = None) -> int:
+    """This is an internal API only meant for use by pip's own console scripts.
+
+    For additional details, see https://github.com/pypa/pip/issues/7498.
+    """
+    from pip._internal.utils.entrypoints import _wrapper
+
+    return _wrapper(args)

transactify/Lib/site-packages/pip/__main__.py

@@ -0,0 +1,24 @@
+import os
+import sys
+
+# Remove '' and current working directory from the first entry
+# of sys.path, if present to avoid using current directory
+# in pip commands check, freeze, install, list and show,
+# when invoked as python -m pip <command>
+if sys.path[0] in ("", os.getcwd()):
+    sys.path.pop(0)
+
+# If we are running from a wheel, add the wheel to sys.path
+# This allows the usage python pip-*.whl/pip install pip-*.whl
+if __package__ == "":
+    # __file__ is pip-*.whl/pip/__main__.py
+    # first dirname call strips of '/__main__.py', second strips off '/pip'
+    # Resulting path is the name of the wheel itself
+    # Add that to sys.path so we can import pip
+    path = os.path.dirname(os.path.dirname(__file__))
+    sys.path.insert(0, path)
+
+if __name__ == "__main__":
+    from pip._internal.cli.main import main as _main
+
+    sys.exit(_main())

transactify/Lib/site-packages/pip/__pip-runner__.py

@@ -0,0 +1,50 @@
+"""Execute exactly this copy of pip, within a different environment.
+
+This file is named as it is, to ensure that this module can't be imported via
+an import statement.
+"""
+
+# /!\ This version compatibility check section must be Python 2 compatible. /!\
+
+import sys
+
+# Copied from setup.py
+PYTHON_REQUIRES = (3, 7)
+
+
+def version_str(version):  # type: ignore
+    return ".".join(str(v) for v in version)
+
+
+if sys.version_info[:2] < PYTHON_REQUIRES:
+    raise SystemExit(
+        "This version of pip does not support python {} (requires >={}).".format(
+            version_str(sys.version_info[:2]), version_str(PYTHON_REQUIRES)
+        )
+    )
+
+# From here on, we can use Python 3 features, but the syntax must remain
+# Python 2 compatible.
+
+import runpy  # noqa: E402
+from importlib.machinery import PathFinder  # noqa: E402
+from os.path import dirname  # noqa: E402
+
+PIP_SOURCES_ROOT = dirname(dirname(__file__))
+
+
+class PipImportRedirectingFinder:
+    @classmethod
+    def find_spec(self, fullname, path=None, target=None):  # type: ignore
+        if fullname != "pip":
+            return None
+
+        spec = PathFinder.find_spec(fullname, [PIP_SOURCES_ROOT], target)
+        assert spec, (PIP_SOURCES_ROOT, fullname)
+        return spec
+
+
+sys.meta_path.insert(0, PipImportRedirectingFinder())
+
+assert __name__ == "__main__", "Cannot run __pip-runner__.py as a non-main module"
+runpy.run_module("pip", run_name="__main__", alter_sys=True)

transactify/Lib/site-packages/pip/_internal/__init__.py

@@ -0,0 +1,18 @@
+from typing import List, Optional
+
+from pip._internal.utils import _log
+
+# init_logging() must be called before any call to logging.getLogger()
+# which happens at import of most modules.
+_log.init_logging()
+
+
+def main(args: (Optional[List[str]]) = None) -> int:
+    """This is preserved for old console scripts that may still be referencing
+    it.
+
+    For additional details, see https://github.com/pypa/pip/issues/7498.
+    """
+    from pip._internal.utils.entrypoints import _wrapper
+
+    return _wrapper(args)

transactify/Lib/site-packages/pip/_internal/build_env.py

@@ -0,0 +1,311 @@
+"""Build Environment used for isolation during sdist building
+"""
+
+import logging
+import os
+import pathlib
+import site
+import sys
+import textwrap
+from collections import OrderedDict
+from types import TracebackType
+from typing import TYPE_CHECKING, Iterable, List, Optional, Set, Tuple, Type, Union
+
+from pip._vendor.certifi import where
+from pip._vendor.packaging.requirements import Requirement
+from pip._vendor.packaging.version import Version
+
+from pip import __file__ as pip_location
+from pip._internal.cli.spinners import open_spinner
+from pip._internal.locations import get_platlib, get_purelib, get_scheme
+from pip._internal.metadata import get_default_environment, get_environment
+from pip._internal.utils.subprocess import call_subprocess
+from pip._internal.utils.temp_dir import TempDirectory, tempdir_kinds
+
+if TYPE_CHECKING:
+    from pip._internal.index.package_finder import PackageFinder
+
+logger = logging.getLogger(__name__)
+
+
+def _dedup(a: str, b: str) -> Union[Tuple[str], Tuple[str, str]]:
+    return (a, b) if a != b else (a,)
+
+
+class _Prefix:
+    def __init__(self, path: str) -> None:
+        self.path = path
+        self.setup = False
+        scheme = get_scheme("", prefix=path)
+        self.bin_dir = scheme.scripts
+        self.lib_dirs = _dedup(scheme.purelib, scheme.platlib)
+
+
+def get_runnable_pip() -> str:
+    """Get a file to pass to a Python executable, to run the currently-running pip.
+
+    This is used to run a pip subprocess, for installing requirements into the build
+    environment.
+    """
+    source = pathlib.Path(pip_location).resolve().parent
+
+    if not source.is_dir():
+        # This would happen if someone is using pip from inside a zip file. In that
+        # case, we can use that directly.
+        return str(source)
+
+    return os.fsdecode(source / "__pip-runner__.py")
+
+
+def _get_system_sitepackages() -> Set[str]:
+    """Get system site packages
+
+    Usually from site.getsitepackages,
+    but fallback on `get_purelib()/get_platlib()` if unavailable
+    (e.g. in a virtualenv created by virtualenv<20)
+
+    Returns normalized set of strings.
+    """
+    if hasattr(site, "getsitepackages"):
+        system_sites = site.getsitepackages()
+    else:
+        # virtualenv < 20 overwrites site.py without getsitepackages
+        # fallback on get_purelib/get_platlib.
+        # this is known to miss things, but shouldn't in the cases
+        # where getsitepackages() has been removed (inside a virtualenv)
+        system_sites = [get_purelib(), get_platlib()]
+    return {os.path.normcase(path) for path in system_sites}
+
+
+class BuildEnvironment:
+    """Creates and manages an isolated environment to install build deps"""
+
+    def __init__(self) -> None:
+        temp_dir = TempDirectory(kind=tempdir_kinds.BUILD_ENV, globally_managed=True)
+
+        self._prefixes = OrderedDict(
+            (name, _Prefix(os.path.join(temp_dir.path, name)))
+            for name in ("normal", "overlay")
+        )
+
+        self._bin_dirs: List[str] = []
+        self._lib_dirs: List[str] = []
+        for prefix in reversed(list(self._prefixes.values())):
+            self._bin_dirs.append(prefix.bin_dir)
+            self._lib_dirs.extend(prefix.lib_dirs)
+
+        # Customize site to:
+        # - ensure .pth files are honored
+        # - prevent access to system site packages
+        system_sites = _get_system_sitepackages()
+
+        self._site_dir = os.path.join(temp_dir.path, "site")
+        if not os.path.exists(self._site_dir):
+            os.mkdir(self._site_dir)
+        with open(
+            os.path.join(self._site_dir, "sitecustomize.py"), "w", encoding="utf-8"
+        ) as fp:
+            fp.write(
+                textwrap.dedent(
+                    """
+                import os, site, sys
+
+                # First, drop system-sites related paths.
+                original_sys_path = sys.path[:]
+                known_paths = set()
+                for path in {system_sites!r}:
+                    site.addsitedir(path, known_paths=known_paths)
+                system_paths = set(
+                    os.path.normcase(path)
+                    for path in sys.path[len(original_sys_path):]
+                )
+                original_sys_path = [
+                    path for path in original_sys_path
+                    if os.path.normcase(path) not in system_paths
+                ]
+                sys.path = original_sys_path
+
+                # Second, add lib directories.
+                # ensuring .pth file are processed.
+                for path in {lib_dirs!r}:
+                    assert not path in sys.path
+                    site.addsitedir(path)
+                """
+                ).format(system_sites=system_sites, lib_dirs=self._lib_dirs)
+            )
+
+    def __enter__(self) -> None:
+        self._save_env = {
+            name: os.environ.get(name, None)
+            for name in ("PATH", "PYTHONNOUSERSITE", "PYTHONPATH")
+        }
+
+        path = self._bin_dirs[:]
+        old_path = self._save_env["PATH"]
+        if old_path:
+            path.extend(old_path.split(os.pathsep))
+
+        pythonpath = [self._site_dir]
+
+        os.environ.update(
+            {
+                "PATH": os.pathsep.join(path),
+                "PYTHONNOUSERSITE": "1",
+                "PYTHONPATH": os.pathsep.join(pythonpath),
+            }
+        )
+
+    def __exit__(
+        self,
+        exc_type: Optional[Type[BaseException]],
+        exc_val: Optional[BaseException],
+        exc_tb: Optional[TracebackType],
+    ) -> None:
+        for varname, old_value in self._save_env.items():
+            if old_value is None:
+                os.environ.pop(varname, None)
+            else:
+                os.environ[varname] = old_value
+
+    def check_requirements(
+        self, reqs: Iterable[str]
+    ) -> Tuple[Set[Tuple[str, str]], Set[str]]:
+        """Return 2 sets:
+        - conflicting requirements: set of (installed, wanted) reqs tuples
+        - missing requirements: set of reqs
+        """
+        missing = set()
+        conflicting = set()
+        if reqs:
+            env = (
+                get_environment(self._lib_dirs)
+                if hasattr(self, "_lib_dirs")
+                else get_default_environment()
+            )
+            for req_str in reqs:
+                req = Requirement(req_str)
+                # We're explicitly evaluating with an empty extra value, since build
+                # environments are not provided any mechanism to select specific extras.
+                if req.marker is not None and not req.marker.evaluate({"extra": ""}):
+                    continue
+                dist = env.get_distribution(req.name)
+                if not dist:
+                    missing.add(req_str)
+                    continue
+                if isinstance(dist.version, Version):
+                    installed_req_str = f"{req.name}=={dist.version}"
+                else:
+                    installed_req_str = f"{req.name}==={dist.version}"
+                if not req.specifier.contains(dist.version, prereleases=True):
+                    conflicting.add((installed_req_str, req_str))
+                # FIXME: Consider direct URL?
+        return conflicting, missing
+
+    def install_requirements(
+        self,
+        finder: "PackageFinder",
+        requirements: Iterable[str],
+        prefix_as_string: str,
+        *,
+        kind: str,
+    ) -> None:
+        prefix = self._prefixes[prefix_as_string]
+        assert not prefix.setup
+        prefix.setup = True
+        if not requirements:
+            return
+        self._install_requirements(
+            get_runnable_pip(),
+            finder,
+            requirements,
+            prefix,
+            kind=kind,
+        )
+
+    @staticmethod
+    def _install_requirements(
+        pip_runnable: str,
+        finder: "PackageFinder",
+        requirements: Iterable[str],
+        prefix: _Prefix,
+        *,
+        kind: str,
+    ) -> None:
+        args: List[str] = [
+            sys.executable,
+            pip_runnable,
+            "install",
+            "--ignore-installed",
+            "--no-user",
+            "--prefix",
+            prefix.path,
+            "--no-warn-script-location",
+        ]
+        if logger.getEffectiveLevel() <= logging.DEBUG:
+            args.append("-v")
+        for format_control in ("no_binary", "only_binary"):
+            formats = getattr(finder.format_control, format_control)
+            args.extend(
+                (
+                    "--" + format_control.replace("_", "-"),
+                    ",".join(sorted(formats or {":none:"})),
+                )
+            )
+
+        index_urls = finder.index_urls
+        if index_urls:
+            args.extend(["-i", index_urls[0]])
+            for extra_index in index_urls[1:]:
+                args.extend(["--extra-index-url", extra_index])
+        else:
+            args.append("--no-index")
+        for link in finder.find_links:
+            args.extend(["--find-links", link])
+
+        for host in finder.trusted_hosts:
+            args.extend(["--trusted-host", host])
+        if finder.allow_all_prereleases:
+            args.append("--pre")
+        if finder.prefer_binary:
+            args.append("--prefer-binary")
+        args.append("--")
+        args.extend(requirements)
+        extra_environ = {"_PIP_STANDALONE_CERT": where()}
+        with open_spinner(f"Installing {kind}") as spinner:
+            call_subprocess(
+                args,
+                command_desc=f"pip subprocess to install {kind}",
+                spinner=spinner,
+                extra_environ=extra_environ,
+            )
+
+
+class NoOpBuildEnvironment(BuildEnvironment):
+    """A no-op drop-in replacement for BuildEnvironment"""
+
+    def __init__(self) -> None:
+        pass
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(
+        self,
+        exc_type: Optional[Type[BaseException]],
+        exc_val: Optional[BaseException],
+        exc_tb: Optional[TracebackType],
+    ) -> None:
+        pass
+
+    def cleanup(self) -> None:
+        pass
+
+    def install_requirements(
+        self,
+        finder: "PackageFinder",
+        requirements: Iterable[str],
+        prefix_as_string: str,
+        *,
+        kind: str,
+    ) -> None:
+        raise NotImplementedError()

transactify/Lib/site-packages/pip/_internal/cache.py

@@ -0,0 +1,290 @@
+"""Cache Management
+"""
+
+import hashlib
+import json
+import logging
+import os
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from pip._vendor.packaging.tags import Tag, interpreter_name, interpreter_version
+from pip._vendor.packaging.utils import canonicalize_name
+
+from pip._internal.exceptions import InvalidWheelFilename
+from pip._internal.models.direct_url import DirectUrl
+from pip._internal.models.link import Link
+from pip._internal.models.wheel import Wheel
+from pip._internal.utils.temp_dir import TempDirectory, tempdir_kinds
+from pip._internal.utils.urls import path_to_url
+
+logger = logging.getLogger(__name__)
+
+ORIGIN_JSON_NAME = "origin.json"
+
+
+def _hash_dict(d: Dict[str, str]) -> str:
+    """Return a stable sha224 of a dictionary."""
+    s = json.dumps(d, sort_keys=True, separators=(",", ":"), ensure_ascii=True)
+    return hashlib.sha224(s.encode("ascii")).hexdigest()
+
+
+class Cache:
+    """An abstract class - provides cache directories for data from links
+
+    :param cache_dir: The root of the cache.
+    """
+
+    def __init__(self, cache_dir: str) -> None:
+        super().__init__()
+        assert not cache_dir or os.path.isabs(cache_dir)
+        self.cache_dir = cache_dir or None
+
+    def _get_cache_path_parts(self, link: Link) -> List[str]:
+        """Get parts of part that must be os.path.joined with cache_dir"""
+
+        # We want to generate an url to use as our cache key, we don't want to
+        # just re-use the URL because it might have other items in the fragment
+        # and we don't care about those.
+        key_parts = {"url": link.url_without_fragment}
+        if link.hash_name is not None and link.hash is not None:
+            key_parts[link.hash_name] = link.hash
+        if link.subdirectory_fragment:
+            key_parts["subdirectory"] = link.subdirectory_fragment
+
+        # Include interpreter name, major and minor version in cache key
+        # to cope with ill-behaved sdists that build a different wheel
+        # depending on the python version their setup.py is being run on,
+        # and don't encode the difference in compatibility tags.
+        # https://github.com/pypa/pip/issues/7296
+        key_parts["interpreter_name"] = interpreter_name()
+        key_parts["interpreter_version"] = interpreter_version()
+
+        # Encode our key url with sha224, we'll use this because it has similar
+        # security properties to sha256, but with a shorter total output (and
+        # thus less secure). However the differences don't make a lot of
+        # difference for our use case here.
+        hashed = _hash_dict(key_parts)
+
+        # We want to nest the directories some to prevent having a ton of top
+        # level directories where we might run out of sub directories on some
+        # FS.
+        parts = [hashed[:2], hashed[2:4], hashed[4:6], hashed[6:]]
+
+        return parts
+
+    def _get_candidates(self, link: Link, canonical_package_name: str) -> List[Any]:
+        can_not_cache = not self.cache_dir or not canonical_package_name or not link
+        if can_not_cache:
+            return []
+
+        path = self.get_path_for_link(link)
+        if os.path.isdir(path):
+            return [(candidate, path) for candidate in os.listdir(path)]
+        return []
+
+    def get_path_for_link(self, link: Link) -> str:
+        """Return a directory to store cached items in for link."""
+        raise NotImplementedError()
+
+    def get(
+        self,
+        link: Link,
+        package_name: Optional[str],
+        supported_tags: List[Tag],
+    ) -> Link:
+        """Returns a link to a cached item if it exists, otherwise returns the
+        passed link.
+        """
+        raise NotImplementedError()
+
+
+class SimpleWheelCache(Cache):
+    """A cache of wheels for future installs."""
+
+    def __init__(self, cache_dir: str) -> None:
+        super().__init__(cache_dir)
+
+    def get_path_for_link(self, link: Link) -> str:
+        """Return a directory to store cached wheels for link
+
+        Because there are M wheels for any one sdist, we provide a directory
+        to cache them in, and then consult that directory when looking up
+        cache hits.
+
+        We only insert things into the cache if they have plausible version
+        numbers, so that we don't contaminate the cache with things that were
+        not unique. E.g. ./package might have dozens of installs done for it
+        and build a version of 0.0...and if we built and cached a wheel, we'd
+        end up using the same wheel even if the source has been edited.
+
+        :param link: The link of the sdist for which this will cache wheels.
+        """
+        parts = self._get_cache_path_parts(link)
+        assert self.cache_dir
+        # Store wheels within the root cache_dir
+        return os.path.join(self.cache_dir, "wheels", *parts)
+
+    def get(
+        self,
+        link: Link,
+        package_name: Optional[str],
+        supported_tags: List[Tag],
+    ) -> Link:
+        candidates = []
+
+        if not package_name:
+            return link
+
+        canonical_package_name = canonicalize_name(package_name)
+        for wheel_name, wheel_dir in self._get_candidates(link, canonical_package_name):
+            try:
+                wheel = Wheel(wheel_name)
+            except InvalidWheelFilename:
+                continue
+            if canonicalize_name(wheel.name) != canonical_package_name:
+                logger.debug(
+                    "Ignoring cached wheel %s for %s as it "
+                    "does not match the expected distribution name %s.",
+                    wheel_name,
+                    link,
+                    package_name,
+                )
+                continue
+            if not wheel.supported(supported_tags):
+                # Built for a different python/arch/etc
+                continue
+            candidates.append(
+                (
+                    wheel.support_index_min(supported_tags),
+                    wheel_name,
+                    wheel_dir,
+                )
+            )
+
+        if not candidates:
+            return link
+
+        _, wheel_name, wheel_dir = min(candidates)
+        return Link(path_to_url(os.path.join(wheel_dir, wheel_name)))
+
+
+class EphemWheelCache(SimpleWheelCache):
+    """A SimpleWheelCache that creates it's own temporary cache directory"""
+
+    def __init__(self) -> None:
+        self._temp_dir = TempDirectory(
+            kind=tempdir_kinds.EPHEM_WHEEL_CACHE,
+            globally_managed=True,
+        )
+
+        super().__init__(self._temp_dir.path)
+
+
+class CacheEntry:
+    def __init__(
+        self,
+        link: Link,
+        persistent: bool,
+    ):
+        self.link = link
+        self.persistent = persistent
+        self.origin: Optional[DirectUrl] = None
+        origin_direct_url_path = Path(self.link.file_path).parent / ORIGIN_JSON_NAME
+        if origin_direct_url_path.exists():
+            try:
+                self.origin = DirectUrl.from_json(
+                    origin_direct_url_path.read_text(encoding="utf-8")
+                )
+            except Exception as e:
+                logger.warning(
+                    "Ignoring invalid cache entry origin file %s for %s (%s)",
+                    origin_direct_url_path,
+                    link.filename,
+                    e,
+                )
+
+
+class WheelCache(Cache):
+    """Wraps EphemWheelCache and SimpleWheelCache into a single Cache
+
+    This Cache allows for gracefully degradation, using the ephem wheel cache
+    when a certain link is not found in the simple wheel cache first.
+    """
+
+    def __init__(self, cache_dir: str) -> None:
+        super().__init__(cache_dir)
+        self._wheel_cache = SimpleWheelCache(cache_dir)
+        self._ephem_cache = EphemWheelCache()
+
+    def get_path_for_link(self, link: Link) -> str:
+        return self._wheel_cache.get_path_for_link(link)
+
+    def get_ephem_path_for_link(self, link: Link) -> str:
+        return self._ephem_cache.get_path_for_link(link)
+
+    def get(
+        self,
+        link: Link,
+        package_name: Optional[str],
+        supported_tags: List[Tag],
+    ) -> Link:
+        cache_entry = self.get_cache_entry(link, package_name, supported_tags)
+        if cache_entry is None:
+            return link
+        return cache_entry.link
+
+    def get_cache_entry(
+        self,
+        link: Link,
+        package_name: Optional[str],
+        supported_tags: List[Tag],
+    ) -> Optional[CacheEntry]:
+        """Returns a CacheEntry with a link to a cached item if it exists or
+        None. The cache entry indicates if the item was found in the persistent
+        or ephemeral cache.
+        """
+        retval = self._wheel_cache.get(
+            link=link,
+            package_name=package_name,
+            supported_tags=supported_tags,
+        )
+        if retval is not link:
+            return CacheEntry(retval, persistent=True)
+
+        retval = self._ephem_cache.get(
+            link=link,
+            package_name=package_name,
+            supported_tags=supported_tags,
+        )
+        if retval is not link:
+            return CacheEntry(retval, persistent=False)
+
+        return None
+
+    @staticmethod
+    def record_download_origin(cache_dir: str, download_info: DirectUrl) -> None:
+        origin_path = Path(cache_dir) / ORIGIN_JSON_NAME
+        if origin_path.exists():
+            try:
+                origin = DirectUrl.from_json(origin_path.read_text(encoding="utf-8"))
+            except Exception as e:
+                logger.warning(
+                    "Could not read origin file %s in cache entry (%s). "
+                    "Will attempt to overwrite it.",
+                    origin_path,
+                    e,
+                )
+            else:
+                # TODO: use DirectUrl.equivalent when
+                # https://github.com/pypa/pip/pull/10564 is merged.
+                if origin.url != download_info.url:
+                    logger.warning(
+                        "Origin URL %s in cache entry %s does not match download URL "
+                        "%s. This is likely a pip bug or a cache corruption issue. "
+                        "Will overwrite it with the new value.",
+                        origin.url,
+                        cache_dir,
+                        download_info.url,
+                    )
+        origin_path.write_text(download_info.to_json(), encoding="utf-8")

transactify/Lib/site-packages/pip/_internal/cli/__init__.py

@@ -0,0 +1,4 @@
+"""Subpackage containing all of pip's command line interface related code
+"""
+
+# This file intentionally does not import submodules