


'''simple docstring'''



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


__A	=logging.getLogger(__name__)


@dataclass
class 		_snake_case					:
     lowerCAmelCase      :str   =					42
     lowerCAmelCase      :List[str]   =					42
     lowerCAmelCase      :Optional[Any]   =					42


@dataclass
class 		_snake_case					:
     lowerCAmelCase      :List[str]   =					42
     lowerCAmelCase      :Dict   =					42
     lowerCAmelCase      :int   =					None
     lowerCAmelCase      :Tuple   =					None


class 		_snake_case					(     a__	):
     lowerCAmelCase      :str   =					'''train'''
     lowerCAmelCase      :Tuple   =					'''dev'''
     lowerCAmelCase      :Optional[int]   =					'''test'''


class 		_snake_case					:




     @staticmethod
     def 				snake_case__						(      _lowerCamelCase							,							_lowerCamelCase):
        raise NotImplementedError




     @staticmethod
     def 				snake_case__						(      _lowerCamelCase):
        raise NotImplementedError




     @staticmethod
     def 				snake_case__						(      _lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase=False							,							_lowerCamelCase="[CLS]"							,							_lowerCamelCase=1							,							_lowerCamelCase="[SEP]"							,							_lowerCamelCase=False							,							_lowerCamelCase=False							,							_lowerCamelCase=0							,							_lowerCamelCase=0							,							_lowerCamelCase=-100							,							_lowerCamelCase=0							,							_lowerCamelCase=True							,							):
        UpperCAmelCase__  :					Optional[int]        =     {label: i for i, label in enumerate(_lowerCamelCase)}

        UpperCAmelCase__  :					List[str]        =     []
        for ex_index, example in enumerate(_lowerCamelCase):
           if ex_index % 1_0000 == 0:
              logger.info("""Writing example %d of %d"""							,							_lowerCamelCase							,							len(_lowerCamelCase))

           UpperCAmelCase__  :					Tuple        =     []
           UpperCAmelCase__  :					int        =     []
           for word, label in zip(example.words							,							example.labels):
              UpperCAmelCase__  :					Optional[Any]        =     tokenizer.tokenize(_lowerCamelCase)

              # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
              if len(_lowerCamelCase) > 0:
                 tokens.extend(_lowerCamelCase)
                 # 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(_lowerCamelCase) - 1))

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
           UpperCAmelCase__  :					Tuple        =     tokenizer.num_special_tokens_to_add()
           if len(_lowerCamelCase) > max_seq_length - special_tokens_count:
              UpperCAmelCase__  :					List[str]        =     tokens[: (max_seq_length - special_tokens_count)]
              UpperCAmelCase__  :					Any        =     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]
           UpperCAmelCase__  :					str        =     [sequence_a_segment_id] * len(_lowerCamelCase)

           if cls_token_at_end:
              tokens += [cls_token]
              label_ids += [pad_token_label_id]
              segment_ids += [cls_token_segment_id]
           else:
              UpperCAmelCase__  :					str        =     [cls_token] + tokens
              UpperCAmelCase__  :					Any        =     [pad_token_label_id] + label_ids
              UpperCAmelCase__  :					Any        =     [cls_token_segment_id] + segment_ids

           UpperCAmelCase__  :					Optional[int]        =     tokenizer.convert_tokens_to_ids(_lowerCamelCase)

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

           # Zero-pad up to the sequence length.
           UpperCAmelCase__  :					List[Any]        =     max_seq_length - len(_lowerCamelCase)
           if pad_on_left:
              UpperCAmelCase__  :					List[Any]        =     ([pad_token] * padding_length) + input_ids
              UpperCAmelCase__  :					int        =     ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
              UpperCAmelCase__  :					Optional[Any]        =     ([pad_token_segment_id] * padding_length) + segment_ids
              UpperCAmelCase__  :					List[str]        =     ([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(_lowerCamelCase) == max_seq_length
           assert len(_lowerCamelCase) == max_seq_length
           assert len(_lowerCamelCase) == max_seq_length
           assert len(_lowerCamelCase) == max_seq_length

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

           if "token_type_ids" not in tokenizer.model_input_names:
              UpperCAmelCase__  :					Any        =     None

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


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


  class 		_snake_case					(     a__	):
       lowerCAmelCase      :Optional[int]   =					42
       lowerCAmelCase      :Tuple   =					nn.CrossEntropyLoss().ignore_index




       def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase=False							,							_lowerCamelCase = Split.train							,							):
          # Load data features from cache or dataset file
          UpperCAmelCase__  :					Any        =     os.path.join(
              _lowerCamelCase							,							"""cached_{}_{}_{}""".format(mode.value							,							tokenizer.__class__.__name__							,							str(_lowerCamelCase))							,							)

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          UpperCAmelCase__  :					Any        =     cached_features_file + """.lock"""
          with FileLock(_lowerCamelCase):
             if os.path.exists(_lowerCamelCase) and not overwrite_cache:
                logger.info(f'''Loading features from cached file {cached_features_file}''')
                UpperCAmelCase__  :					str        =     torch.load(_lowerCamelCase)
             else:
                logger.info(f'''Creating features from dataset file at {data_dir}''')
                UpperCAmelCase__  :					List[str]        =     token_classification_task.read_examples_from_file(_lowerCamelCase							,							_lowerCamelCase)
                # TODO clean up all this to leverage built-in features of tokenizers
                UpperCAmelCase__  :					List[Any]        =     token_classification_task.convert_examples_to_features(
                    _lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							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=_lowerCamelCase							,							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							,							_lowerCamelCase)




       def __len__(      self):
          return len(self.features)




       def __getitem__(      self							,							_lowerCamelCase):
          return self.features[i]


if is_tf_available():
  import tensorflow as tf


  class 		_snake_case					:
       lowerCAmelCase      :Optional[Any]   =					42
       lowerCAmelCase      :Tuple   =					-100




       def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase=False							,							_lowerCamelCase = Split.train							,							):
          UpperCAmelCase__  :					Dict        =     token_classification_task.read_examples_from_file(_lowerCamelCase							,							_lowerCamelCase)
          # TODO clean up all this to leverage built-in features of tokenizers
          UpperCAmelCase__  :					List[str]        =     token_classification_task.convert_examples_to_features(
              _lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							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=_lowerCamelCase							,							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:
             UpperCAmelCase__  :					Union[str, Any]        =     tf.data.Dataset.from_generator(
                 _lowerCamelCase							,							({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)							,							(
                     {"""input_ids""": tf.TensorShape([None]), """attention_mask""": tf.TensorShape([None])},
                     tf.TensorShape([None]),
                 )							,							)
          else:
             UpperCAmelCase__  :					Optional[int]        =     tf.data.Dataset.from_generator(
                 _lowerCamelCase							,							({"""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 				snake_case__						(      self):
          UpperCAmelCase__  :					int        =     self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

          return self.dataset




       def __len__(      self):
          return len(self.features)




       def __getitem__(      self							,							_lowerCamelCase):
          return self.features[i]



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A	={
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['BertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BertForMaskedLM',
		    'BertForMultipleChoice',
		    'BertForNextSentencePrediction',
		    'BertForPreTraining',
		    'BertForQuestionAnswering',
		    'BertForSequenceClassification',
		    'BertForTokenClassification',
		    'BertLayer',
		    'BertLMHeadModel',
		    'BertModel',
		    'BertPreTrainedModel',
		    'load_tf_weights_in_bert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBertEmbeddings',
		    'TFBertForMaskedLM',
		    'TFBertForMultipleChoice',
		    'TFBertForNextSentencePrediction',
		    'TFBertForPreTraining',
		    'TFBertForQuestionAnswering',
		    'TFBertForSequenceClassification',
		    'TFBertForTokenClassification',
		    'TFBertLMHeadModel',
		    'TFBertMainLayer',
		    'TFBertModel',
		    'TFBertPreTrainedModel',
		]
try:
		if not is_tensorflow_text_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['TFBertTokenizer']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'FlaxBertForCausalLM',
		    'FlaxBertForMaskedLM',
		    'FlaxBertForMultipleChoice',
		    'FlaxBertForNextSentencePrediction',
		    'FlaxBertForPreTraining',
		    'FlaxBertForQuestionAnswering',
		    'FlaxBertForSequenceClassification',
		    'FlaxBertForTokenClassification',
		    'FlaxBertModel',
		    'FlaxBertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
		from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_fast import BertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bert import (
				    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BertForMaskedLM,
				    BertForMultipleChoice,
				    BertForNextSentencePrediction,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    BertForTokenClassification,
				    BertLayer,
				    BertLMHeadModel,
				    BertModel,
				    BertPreTrainedModel,
				    load_tf_weights_in_bert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_bert import (
				    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBertEmbeddings,
				    TFBertForMaskedLM,
				    TFBertForMultipleChoice,
				    TFBertForNextSentencePrediction,
				    TFBertForPreTraining,
				    TFBertForQuestionAnswering,
				    TFBertForSequenceClassification,
				    TFBertForTokenClassification,
				    TFBertLMHeadModel,
				    TFBertMainLayer,
				    TFBertModel,
				    TFBertPreTrainedModel,
				)

		try:
				if not is_tensorflow_text_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_tf import TFBertTokenizer

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_bert import (
				    FlaxBertForCausalLM,
				    FlaxBertForMaskedLM,
				    FlaxBertForMultipleChoice,
				    FlaxBertForNextSentencePrediction,
				    FlaxBertForPreTraining,
				    FlaxBertForQuestionAnswering,
				    FlaxBertForSequenceClassification,
				    FlaxBertForTokenClassification,
				    FlaxBertModel,
				    FlaxBertPreTrainedModel,
				)

else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)