Update modeling_enc_t5.py

modeling_enc_t5.py

@@ -1,122 +1,13 @@
 import copy
-from typing import Any, Dict, List, Optional
 
 import torch
 from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from transformers import T5TokenizerFast, T5Config
-from transformers.modeling_outputs import SequenceClassifierOutput, TokenClassifierOutput
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import TokenClassifierOutput
 from transformers.models.t5.modeling_t5 import T5Config, T5PreTrainedModel, T5Stack
 from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
 
 
-class EncT5Tokenizer(T5TokenizerFast):
-    def __init__(
-        self,
-        vocab_file,
-        bos_token="<s>",
-        eos_token="</s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        extra_ids=100,
-        additional_special_tokens=None,
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        **kwargs,
-    ) -> None:
-        sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-
-        super().__init__(
-            vocab_file=vocab_file,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            extra_ids=extra_ids,
-            additional_special_tokens=additional_special_tokens,
-            sp_model_kwargs=sp_model_kwargs,
-            **kwargs,
-        )
-
-    def get_special_tokens_mask(
-        self,
-        token_ids_0: List[int],
-        token_ids_1: Optional[List[int]] = None,
-        already_has_special_tokens: bool = False,
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0,
-                token_ids_1=token_ids_1,
-                already_has_special_tokens=True,
-            )
-
-        # normal case: some special tokens
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
-        use of token type ids, therefore a list of zeros is returned.
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-        Returns:
-            `List[int]`: List of zeros.
-        """
-        bos = [self.bos_token_id]
-        eos = [self.eos_token_id]
-
-        if token_ids_1 is None:
-            return len(bos + token_ids_0 + eos) * [0]
-        return len(bos + token_ids_0 + eos + token_ids_1 + eos) * [0]
-
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A sequence has the following format:
-        - single sequence: `<s> X </s>`
-        - pair of sequences: `<s> A </s> B </s>`
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-        if token_ids_1 is None:
-            return [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
-        else:
-            return (
-                [self.bos_token_id]
-                + token_ids_0
-                + [self.eos_token_id]
-                + token_ids_1
-                + [self.eos_token_id]
-            )
-
-
 class EncT5ForTokenClassification(T5PreTrainedModel):
     _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
@@ -222,4 +113,3 @@ class EncT5ForTokenClassification(T5PreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
-