

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


lowercase_             =     logging.getLogger(__name__)





@dataclass
class 				__lowerCAmelCase  :
  _a		    =						42
  _a		    =						42
  _a		    =						42





@dataclass
class 				__lowerCAmelCase  :
  _a		    =						42
  _a		    =						42
  _a		    =						None
  _a		    =						None





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						"""train"""
  _a		    =						"""dev"""
  _a		    =						"""test"""





class 				__lowerCAmelCase  :




  @staticmethod
  def 						A__  (       lowerCAmelCase							,     lowerCAmelCase      )						->				List[InputExample]:
        '''simple docstring'''






        raise NotImplementedError




  @staticmethod
  def 						A__  (       lowerCAmelCase      )						->				List[str]:
        '''simple docstring'''






        raise NotImplementedError




  @staticmethod
  def 						A__  (       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							,     )						->				List[InputFeatures]:
        '''simple docstring'''






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

        _lowercase			 =[]
        for ex_index, example in enumerate(lowerCAmelCase      ):
              if ex_index % 10_000 == 0:
                    logger.info('Writing example %d of %d'							,     lowerCAmelCase							,     len(lowerCAmelCase      )      )

              _lowercase			 =[]
              _lowercase			 =[]
              for word, label in zip(example.words							,     example.labels      ):
                    _lowercase			 =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.
              _lowercase			 =tokenizer.num_special_tokens_to_add()
              if len(lowerCAmelCase      ) > 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(lowerCAmelCase      )

              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(lowerCAmelCase      )

              # 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(lowerCAmelCase      )

              # Zero-pad up to the sequence length.
              _lowercase			 =max_seq_length - len(lowerCAmelCase      )
              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(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:
                    _lowercase			 =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 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
    _a		    =						42
    _a		    =						nn.CrossEntropyLoss().ignore_index




    def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = None							,     lowerCAmelCase=False							,     lowerCAmelCase = Split.train							,     )						->				str:
          '''simple docstring'''






          _lowercase			 =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.
          _lowercase			 =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}'''      )
                      _lowercase			 =torch.load(lowerCAmelCase      )
                else:
                      logger.info(F'''Creating features from dataset file at {data_dir}'''      )
                      _lowercase			 =token_classification_task.read_examples_from_file(lowerCAmelCase							,     lowerCAmelCase      )
                      # TODO clean up all this to leverage built-in features of tokenizers
                      _lowercase			 =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      )						->				str:
          '''simple docstring'''






          return len(self.features      )




    def __getitem__(       self							,     lowerCAmelCase      )						->				InputFeatures:
          '''simple docstring'''






          return self.features[i]


if is_tf_available():
  import tensorflow as tf





  class 				__lowerCAmelCase  :
    _a		    =						42
    _a		    =						-100




    def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = None							,     lowerCAmelCase=False							,     lowerCAmelCase = Split.train							,     )						->				str:
          '''simple docstring'''






          _lowercase			 =token_classification_task.read_examples_from_file(lowerCAmelCase							,     lowerCAmelCase      )
          # TODO clean up all this to leverage built-in features of tokenizers
          _lowercase			 =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:
                _lowercase			 =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:
                _lowercase			 =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 						A__  (       self      )						->				Union[str, Any]:
          '''simple docstring'''






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

          return self.dataset




    def __len__(       self      )						->				Optional[int]:
          '''simple docstring'''






          return len(self.features      )




    def __getitem__(       self							,     lowerCAmelCase      )						->				InputFeatures:
          '''simple docstring'''






          return self.features[i]





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowercase_             =     {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     ['BeitFeatureExtractor']
  lowercase_             =     ['BeitImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BeitForImageClassification',
      'BeitForMaskedImageModeling',
      'BeitForSemanticSegmentation',
      'BeitModel',
      'BeitPreTrainedModel',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'FlaxBeitForImageClassification',
      'FlaxBeitForMaskedImageModeling',
      'FlaxBeitModel',
      'FlaxBeitPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_beit import BeitFeatureExtractor
    from .image_processing_beit import BeitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_beit import (
        BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BeitForImageClassification,
        BeitForMaskedImageModeling,
        BeitForSemanticSegmentation,
        BeitModel,
        BeitPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_beit import (
        FlaxBeitForImageClassification,
        FlaxBeitForMaskedImageModeling,
        FlaxBeitModel,
        FlaxBeitPreTrainedModel,
    )


else:
  import sys

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


