# coding=utf-8
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# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import os
from typing import Union

from transformers.configuration_utils import PretrainedConfig
import transformers.models.git.configuration_git as configuration_git


GIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}


class GitVisionConfig(configuration_git.GitVisionConfig, dict):
    def __init__(self, *args, **kwargs):
        configuration_git.GitVisionConfig.__init__(
                self, *args, **kwargs)
        dict.__init__(self, **self.__dict__)

    def toJSON(self):
        return json.dumps(
            self,
            default=lambda o: o.__dict__, 
            sort_keys=True,
            indent=4)


class GitConfig(PretrainedConfig, dict):
    r"""
    This is the configuration class to store the configuration of a [`GitModel`]. It is used to instantiate a GIT model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the GIT
    [microsoft/git-base](https://huggingface.co/microsoft/git-base) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`GitVisionConfig`].
        vocab_size (`int`, *optional*, defaults to 30522):
            Vocabulary size of the GIT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GitModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 6):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        num_image_with_embedding (`int`, *optional*):
            The number of temporal embeddings to add, in case the model is used for video captioning/VQA.

    Examples:

    ```python
    >>> from transformers import GitConfig, GitModel

    >>> # Initializing a GIT microsoft/git-base style configuration
    >>> configuration = GitConfig()

    >>> # Initializing a model (with random weights) from the microsoft/git-base style configuration
    >>> model = GitModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "git"

    def __init__(
        self,
        vision_config=None,
        vocab_size=32778,
        hidden_size=768,
        num_hidden_layers=6,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=1024,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        position_embedding_type="absolute",
        use_cache=True,
        tie_word_embeddings=True,
        bos_token_id=101,
        eos_token_id=102,
        num_image_with_embedding=None,
        **kwargs,
    ):
        PretrainedConfig.__init__(
                self,
                bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs)

        if vision_config is None:
            vision_config = {}
        self.vision_config = GitVisionConfig(**vision_config)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.tie_word_embeddings = tie_word_embeddings
        self.num_image_with_embedding = num_image_with_embedding

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id

        dict.__init__(self, **self.__dict__)

    def toJSON(self):
        return json.dumps(
            self,
            default=lambda o: o.__dict__, 
            sort_keys=True,
            indent=4)