


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


UpperCAmelCase_				:			Optional[Any]   =	logging.getLogger(__name__)
@dataclass
class 			SCREAMING_SNAKE_CASE__  :
      snake_case__    :					str
      snake_case__    :					List[str]
      snake_case__    :					Optional[List[str]]
@dataclass
class 			SCREAMING_SNAKE_CASE__  :
      snake_case__    :					List[int]
      snake_case__    :					List[int]
      snake_case__    :					Optional[List[int]]					       =						None
      snake_case__    :					Optional[List[int]]					       =						None
class 			SCREAMING_SNAKE_CASE__  (    lowercase_      ):
      snake_case__    :					str					       =						"train"
      snake_case__    :					Optional[int]					       =						"dev"
      snake_case__    :					Union[str, Any]					       =						"test"
class 			SCREAMING_SNAKE_CASE__  :
      @staticmethod
      def 	SCREAMING_SNAKE_CASE							(	SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      List[InputExample]:
             raise NotImplementedError
      @staticmethod
      def 	SCREAMING_SNAKE_CASE							(	SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      List[str]:
             raise NotImplementedError

      @staticmethod
      def 	SCREAMING_SNAKE_CASE							(	SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Tuple=False					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]="[CLS]"					,							SCREAMING_SNAKE_CASE__    :	Dict=1					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="[SEP]"					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=False					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=False					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=0					,							SCREAMING_SNAKE_CASE__    :	List[str]=-1_0_0					,							SCREAMING_SNAKE_CASE__    :	str=0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=True					,							)		->      List[InputFeatures]:
             a_ :		Dict							=  {label: i for i, label in enumerate(a__					)}

             a_ :		str							=  []
             for ex_index, example in enumerate(a__					):
                    if ex_index % 1_0_0_0_0 == 0:
                           logger.info('Writing example %d of %d'					,							a__					,							len(a__					)					)

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

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                    a_ :		Union[str, Any]							=  tokenizer.num_special_tokens_to_add()
                    if len(a__					) > max_seq_length - special_tokens_count:
                           a_ :		List[Any]							=  tokens[: (max_seq_length - special_tokens_count)]
                           a_ :		Optional[int]							=  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]
                    a_ :		List[Any]							=  [sequence_a_segment_id] * len(a__					)

                    if cls_token_at_end:
                           tokens += [cls_token]
                           label_ids += [pad_token_label_id]
                           segment_ids += [cls_token_segment_id]
                    else:
                           a_ :		Union[str, Any]							=  [cls_token] + tokens
                           a_ :		Tuple							=  [pad_token_label_id] + label_ids
                           a_ :		Tuple							=  [cls_token_segment_id] + segment_ids

                    a_ :		Optional[int]							=  tokenizer.convert_tokens_to_ids(a__					)

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

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

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

                    if "token_type_ids" not in tokenizer.model_input_names:
                           a_ :		List[Any]							=  None

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


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset
  class 			SCREAMING_SNAKE_CASE__  (    lowercase_      ):
        snake_case__    :					List[InputFeatures]
        snake_case__    :					int					       =						nn.CrossEntropyLoss().ignore_index
        def __init__(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	List[str] = None					,							SCREAMING_SNAKE_CASE__    :	str=False					,							SCREAMING_SNAKE_CASE__    :	List[str] = Split.train					,							)		->      str:
               a_ :		int							=  os.path.join(
                   a__					,							'cached_{}_{}_{}'.format(mode.value					,							tokenizer.__class__.__name__					,							str(a__					)					)					,							)

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

        def __getitem__(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Dict					)		->      InputFeatures:
               return self.features[i]


if is_tf_available():
  import tensorflow as tf
  class 			SCREAMING_SNAKE_CASE__  :
        snake_case__    :					List[InputFeatures]
        snake_case__    :					int					       =						-100
        def __init__(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=False					,							SCREAMING_SNAKE_CASE__    :	List[Any] = Split.train					,							)		->      Optional[int]:
               a_ :		Optional[Any]							=  token_classification_task.read_examples_from_file(a__					,							a__					)
               # TODO clean up all this to leverage built-in features of tokenizers
               a_ :		Dict							=  token_classification_task.convert_examples_to_features(
                   a__					,							a__					,							a__					,							a__					,							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=a__					,							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:
                      a_ :		List[str]							=  tf.data.Dataset.from_generator(
                          a__					,							({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)					,							(
                              {'input_ids': tf.TensorShape([None]					), 'attention_mask': tf.TensorShape([None]					)},
                              tf.TensorShape([None]					),
                          )					,							)
               else:
                      a_ :		Optional[Any]							=  tf.data.Dataset.from_generator(
                          a__					,							({'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 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      str:
               a_ :		Dict							=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

               return self.dataset
        def __len__(	self    :	List[Any]					)		->      List[Any]:
               return len(self.features					)

        def __getitem__(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					)		->      InputFeatures:
               return self.features[i]






from typing import TYPE_CHECKING

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


UpperCAmelCase_				:			Union[str, Any]   =	{
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Optional[Any]   =	['WhisperTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			int   =	[
       'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'WhisperForConditionalGeneration',
       'WhisperModel',
       'WhisperPreTrainedModel',
       'WhisperForAudioClassification',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			int   =	[
       'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFWhisperForConditionalGeneration',
       'TFWhisperModel',
       'TFWhisperPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Any   =	[
       'FlaxWhisperForConditionalGeneration',
       'FlaxWhisperModel',
       'FlaxWhisperPreTrainedModel',
       'FlaxWhisperForAudioClassification',
   ]


if TYPE_CHECKING:
   from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
   from .feature_extraction_whisper import WhisperFeatureExtractor
   from .processing_whisper import WhisperProcessor
   from .tokenization_whisper import WhisperTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_whisper_fast import WhisperTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_whisper import (
          WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WhisperForAudioClassification,
          WhisperForConditionalGeneration,
          WhisperModel,
          WhisperPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_whisper import (
          TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWhisperForConditionalGeneration,
          TFWhisperModel,
          TFWhisperPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_whisper import (
          FlaxWhisperForAudioClassification,
          FlaxWhisperForConditionalGeneration,
          FlaxWhisperModel,
          FlaxWhisperPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase_				:			int   =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


