





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_snake_case           =			logging.getLogger(__name__)





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      42
  __lowerCamelCase		=      42
  __lowerCamelCase		=      42





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      42
  __lowerCamelCase		=      42
  __lowerCamelCase		=      None
  __lowerCamelCase		=      None





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      "train"
  __lowerCamelCase		=      "dev"
  __lowerCamelCase		=      "test"





class  lowerCAmelCase							:







  @staticmethod
  def   UpperCAmelCase					(      _lowercase  :Tuple   ,							_lowercase  :Union[Split, str] ):






     '''simple docstring'''





     raise NotImplementedError







  @staticmethod
  def   UpperCAmelCase					(      _lowercase  :str ):






     '''simple docstring'''





     raise NotImplementedError







  @staticmethod
  def   UpperCAmelCase					(      _lowercase  :List[InputExample]   ,							_lowercase  :List[str]   ,							_lowercase  :int   ,							_lowercase  :PreTrainedTokenizer   ,							_lowercase  :Optional[int]=False   ,							_lowercase  :List[str]="[CLS]"   ,							_lowercase  :Optional[int]=1   ,							_lowercase  :Union[str, Any]="[SEP]"   ,							_lowercase  :str=False   ,							_lowercase  :Any=False   ,							_lowercase  :List[str]=0   ,							_lowercase  :int=0   ,							_lowercase  :List[str]=-1_00   ,							_lowercase  :List[str]=0   ,							_lowercase  :Any=True   ,							):






     '''simple docstring'''





     lowercase__			       =  {label: i for i, label in enumerate(_lowercase )}

     lowercase__			       =  []
     for ex_index, example in enumerate(_lowercase ):
        if ex_index % 1_00_00 == 0:
           logger.info("Writing example %d of %d"   ,							_lowercase   ,							len(_lowercase ) )

        lowercase__			       =  []
        lowercase__			       =  []
        for word, label in zip(example.words   ,							example.labels ):
           lowercase__			       =  tokenizer.tokenize(_lowercase )

           # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
           if len(_lowercase ) > 0:
              tokens.extend(_lowercase )
              # Use the real label id for the first token of the word, and padding ids for the remaining tokens
              label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_lowercase ) - 1) )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        lowercase__			       =  tokenizer.num_special_tokens_to_add()
        if len(_lowercase ) > max_seq_length - special_tokens_count:
           lowercase__			       =  tokens[: (max_seq_length - special_tokens_count)]
           lowercase__			       =  label_ids[: (max_seq_length - special_tokens_count)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids:   0   0   0   0  0     0   0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
        if sep_token_extra:
           # roberta uses an extra separator b/w pairs of sentences
           tokens += [sep_token]
           label_ids += [pad_token_label_id]
        lowercase__			       =  [sequence_a_segment_id] * len(_lowercase )

        if cls_token_at_end:
           tokens += [cls_token]
           label_ids += [pad_token_label_id]
           segment_ids += [cls_token_segment_id]
        else:
           lowercase__			       =  [cls_token] + tokens
           lowercase__			       =  [pad_token_label_id] + label_ids
           lowercase__			       =  [cls_token_segment_id] + segment_ids

        lowercase__			       =  tokenizer.convert_tokens_to_ids(_lowercase )

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        lowercase__			       =  [1 if mask_padding_with_zero else 0] * len(_lowercase )

        # Zero-pad up to the sequence length.
        lowercase__			       =  max_seq_length - len(_lowercase )
        if pad_on_left:
           lowercase__			       =  ([pad_token] * padding_length) + input_ids
           lowercase__			       =  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
           lowercase__			       =  ([pad_token_segment_id] * padding_length) + segment_ids
           lowercase__			       =  ([pad_token_label_id] * padding_length) + label_ids
        else:
           input_ids += [pad_token] * padding_length
           input_mask += [0 if mask_padding_with_zero else 1] * padding_length
           segment_ids += [pad_token_segment_id] * padding_length
           label_ids += [pad_token_label_id] * padding_length

        assert len(_lowercase ) == max_seq_length
        assert len(_lowercase ) == max_seq_length
        assert len(_lowercase ) == max_seq_length
        assert len(_lowercase ) == max_seq_length

        if ex_index < 5:
           logger.info("*** Example ***" )
           logger.info("guid: %s"   ,							example.guid )
           logger.info("tokens: %s"   ,							" ".join([str(_lowercase ) for x in tokens] ) )
           logger.info("input_ids: %s"   ,							" ".join([str(_lowercase ) for x in input_ids] ) )
           logger.info("input_mask: %s"   ,							" ".join([str(_lowercase ) for x in input_mask] ) )
           logger.info("segment_ids: %s"   ,							" ".join([str(_lowercase ) for x in segment_ids] ) )
           logger.info("label_ids: %s"   ,							" ".join([str(_lowercase ) for x in label_ids] ) )

        if "token_type_ids" not in tokenizer.model_input_names:
           lowercase__			       =  None

        features.append(
            InputFeatures(
                input_ids=_lowercase   ,							attention_mask=_lowercase   ,							token_type_ids=_lowercase   ,							label_ids=_lowercase ) )
     return features


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset





  class  lowerCAmelCase							(       lowercase_ ):
    __lowerCamelCase		=      42
    __lowerCamelCase		=      nn.CrossEntropyLoss().ignore_index







    def __init__(      self  :Tuple   ,							_lowercase  :TokenClassificationTask   ,							_lowercase  :str   ,							_lowercase  :PreTrainedTokenizer   ,							_lowercase  :List[str]   ,							_lowercase  :str   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :Optional[Any]=False   ,							_lowercase  :Split = Split.train   ,							):






       '''simple docstring'''





       lowercase__			       =  os.path.join(
           _lowercase   ,							"cached_{}_{}_{}".format(mode.value   ,							tokenizer.__class__.__name__   ,							str(_lowercase ) )   ,							)

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       lowercase__			       =  cached_features_file + ".lock"
       with FileLock(_lowercase ):
          if os.path.exists(_lowercase ) and not overwrite_cache:
             logger.info(f'''Loading features from cached file {cached_features_file}''' )
             lowercase__			       =  torch.load(_lowercase )
          else:
             logger.info(f'''Creating features from dataset file at {data_dir}''' )
             lowercase__			       =  token_classification_task.read_examples_from_file(_lowercase   ,							_lowercase )
             # TODO clean up all this to leverage built-in features of tokenizers
             lowercase__			       =  token_classification_task.convert_examples_to_features(
                 _lowercase   ,							_lowercase   ,							_lowercase   ,							_lowercase   ,							cls_token_at_end=bool(model_type in ["xlnet"] )   ,							cls_token=tokenizer.cls_token   ,							cls_token_segment_id=2 if model_type in ["xlnet"] else 0   ,							sep_token=tokenizer.sep_token   ,							sep_token_extra=_lowercase   ,							pad_on_left=bool(tokenizer.padding_side == "left" )   ,							pad_token=tokenizer.pad_token_id   ,							pad_token_segment_id=tokenizer.pad_token_type_id   ,							pad_token_label_id=self.pad_token_label_id   ,							)
             logger.info(f'''Saving features into cached file {cached_features_file}''' )
             torch.save(self.features   ,							_lowercase )







    def __len__(      self  :int ):






       '''simple docstring'''





       return len(self.features )







    def __getitem__(      self  :Optional[Any]   ,							_lowercase  :List[Any] ):






       '''simple docstring'''





       return self.features[i]


if is_tf_available():
  import tensorflow as tf





  class  lowerCAmelCase							:
    __lowerCamelCase		=      42
    __lowerCamelCase		=      -100







    def __init__(      self  :Optional[int]   ,							_lowercase  :TokenClassificationTask   ,							_lowercase  :str   ,							_lowercase  :PreTrainedTokenizer   ,							_lowercase  :List[str]   ,							_lowercase  :str   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :Any=False   ,							_lowercase  :Split = Split.train   ,							):






       '''simple docstring'''





       lowercase__			       =  token_classification_task.read_examples_from_file(_lowercase   ,							_lowercase )
       # TODO clean up all this to leverage built-in features of tokenizers
       lowercase__			       =  token_classification_task.convert_examples_to_features(
           _lowercase   ,							_lowercase   ,							_lowercase   ,							_lowercase   ,							cls_token_at_end=bool(model_type in ["xlnet"] )   ,							cls_token=tokenizer.cls_token   ,							cls_token_segment_id=2 if model_type in ["xlnet"] else 0   ,							sep_token=tokenizer.sep_token   ,							sep_token_extra=_lowercase   ,							pad_on_left=bool(tokenizer.padding_side == "left" )   ,							pad_token=tokenizer.pad_token_id   ,							pad_token_segment_id=tokenizer.pad_token_type_id   ,							pad_token_label_id=self.pad_token_label_id   ,							)

       def gen():
          for ex in self.features:
             if ex.token_type_ids is None:
                yield (
                    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                    ex.label_ids,
                )
             else:
                yield (
                    {
                        "input_ids": ex.input_ids,
                        "attention_mask": ex.attention_mask,
                        "token_type_ids": ex.token_type_ids,
                    },
                    ex.label_ids,
                )

       if "token_type_ids" not in tokenizer.model_input_names:
          lowercase__			       =  tf.data.Dataset.from_generator(
              _lowercase   ,							({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)   ,							(
                  {"input_ids": tf.TensorShape([None] ), "attention_mask": tf.TensorShape([None] )},
                  tf.TensorShape([None] ),
              )   ,							)
       else:
          lowercase__			       =  tf.data.Dataset.from_generator(
              _lowercase   ,							({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)   ,							(
                  {
                      "input_ids": tf.TensorShape([None] ),
                      "attention_mask": tf.TensorShape([None] ),
                      "token_type_ids": tf.TensorShape([None] ),
                  },
                  tf.TensorShape([None] ),
              )   ,							)







    def   UpperCAmelCase					(      self  :List[Any] ):






       '''simple docstring'''





       lowercase__			       =  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

       return self.dataset







    def __len__(      self  :Optional[Any] ):






       '''simple docstring'''





       return len(self.features )







    def __getitem__(      self  :List[str]   ,							_lowercase  :Union[str, Any] ):






       '''simple docstring'''





       return self.features[i]













from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def _A       (   ):
   lowercase__			       =  HfArgumentParser(__magic_name__	)
   lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   lowercase__			       =  TensorFlowBenchmark(args=__magic_name__	)
   try:
      lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      lowercase__			       =  "Arg --no_{0} is no longer used, please use --no-{0} instead."
      lowercase__			       =  " ".join(str(__magic_name__	).split(" "	)[:-1]	)
      lowercase__			       =  ""
      lowercase__			       =  eval(str(__magic_name__	).split(" "	)[-1]	)
      lowercase__			       =  []
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]	)
         else:
            wrong_args.append(__magic_name__	)
      if len(__magic_name__	) > 0:
         lowercase__			       =  full_error_msg + begin_error_msg + str(__magic_name__	)
      raise ValueError(__magic_name__	)
   benchmark.run()


if __name__ == "__main__":
  main()






