




'''simple docstring'''



import os
from collections import deque

import torch
from torch.utils.data import Dataset






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A="",							A="train"  ):







             '''simple docstring'''







             assert os.path.isdir(A  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[]
             SCREAMING_SNAKE_CASE   :      str    					=						os.listdir(A  )
             for story_filename in story_filenames_list:
                   if "summary" in story_filename:
                         continue
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						os.path.join(A,							A  )
                   if not os.path.isfile(A  ):
                         continue
                   self.documents.append(A  )







       def __len__(      self  ):







             '''simple docstring'''







             return len(self.documents  )







       def __getitem__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.documents[idx]
             SCREAMING_SNAKE_CASE   :      List[str]    					=						document_path.split('/'  )[-1]
             with open(A,							encoding='utf-8'  ) as source:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						source.read()
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						process_story(A  )
             return document_name, story_lines, summary_lines




def   lowercase__(			__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						list(filter(lambda __UpperCamelCase    : len(__UpperCamelCase    ) != 0      ,[line.strip() for line in raw_story.split('\n'    )]    )    )

      # for some unknown reason some lines miss a period, add it
      SCREAMING_SNAKE_CASE   :      List[str]    					=						[_add_missing_period(__UpperCamelCase    ) for line in nonempty_lines]

      # gather article lines
      SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
      SCREAMING_SNAKE_CASE   :      int    					=						deque(__UpperCamelCase    )
      while True:
            try:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						lines.popleft()
                  if element.startswith('@highlight'    ):
                        break
                  story_lines.append(__UpperCamelCase    )
            except IndexError:
                  # if "@highlight" is absent from the file we pop
                  # all elements until there is None, raising an exception.
                  return story_lines, []

    # gather summary lines
      SCREAMING_SNAKE_CASE   :      int    					=						list(filter(lambda __UpperCamelCase    : not t.startswith('@highlight'    )      ,__UpperCamelCase    )    )

      return story_lines, summary_lines




def   lowercase__(			__UpperCamelCase:					Dict    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
      if line.startswith('@highlight'    ):
            return line
      if line[-1] in END_TOKENS:
            return line
      return line + "."




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






      if len(__UpperCamelCase    ) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(__UpperCamelCase    ))    )
            return sequence




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						torch.ones_like(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						sequence == pad_token_id
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
      return mask




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						[tokenizer.encode(__UpperCamelCase    ) for line in story_lines]
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[token for sentence in story_lines_token_ids for token in sentence]
      SCREAMING_SNAKE_CASE   :      str    					=						[tokenizer.encode(__UpperCamelCase    ) for line in summary_lines]
      SCREAMING_SNAKE_CASE   :      Dict    					=						[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids





def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[]
      for sequence in batch:
            SCREAMING_SNAKE_CASE   :      Dict    					=						-1
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
            for s in sequence:
                  if s == separator_token_id:
                        sentence_num += 1
                  embeddings.append(sentence_num % 2    )
            batch_embeddings.append(__UpperCamelCase    )
      return torch.tensor(__UpperCamelCase    )








'''simple docstring'''





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_		:     Optional[Any]          =		logging.get_logger(__name__)

UpperCamelCase_		:     Any          =		{
    '''microsoft/unispeech-sat-base-100h-libri-ft''': (
        '''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       str       							=		"""unispeech-sat"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=768   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=3_072   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=1e-5   ,_SCREAMING_SNAKE_CASE="group"   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 512, 512, 512)   ,_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)   ,_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2)   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=128   ,_SCREAMING_SNAKE_CASE=16   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=0.0_5   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=320   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=100   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE="mean"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1_500)   ,_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)   ,_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=1   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=504   ,**_SCREAMING_SNAKE_CASE   ,)				->     Any:
          super().__init__(**_SCREAMING_SNAKE_CASE   ,pad_token_id=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE       )
          _snake_case				= hidden_size
          _snake_case				= feat_extract_norm
          _snake_case				= feat_extract_activation
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= conv_bias
          _snake_case				= num_conv_pos_embeddings
          _snake_case				= num_conv_pos_embedding_groups
          _snake_case				= len(self.conv_dim       )
          _snake_case				= num_hidden_layers
          _snake_case				= intermediate_size
          _snake_case				= hidden_act
          _snake_case				= num_attention_heads
          _snake_case				= hidden_dropout
          _snake_case				= attention_dropout
          _snake_case				= activation_dropout
          _snake_case				= feat_proj_dropout
          _snake_case				= final_dropout
          _snake_case				= layerdrop
          _snake_case				= layer_norm_eps
          _snake_case				= initializer_range
          _snake_case				= vocab_size
          _snake_case				= num_clusters
          _snake_case				= do_stable_layer_norm
          _snake_case				= use_weighted_layer_sum

          if (
              (len(self.conv_stride       ) != self.num_feat_extract_layers)
              or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
              or (len(self.conv_dim       ) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
                   " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
                   f""" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"""
                   f""" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."""       )

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          _snake_case				= apply_spec_augment
          _snake_case				= mask_time_prob
          _snake_case				= mask_time_length
          _snake_case				= mask_time_min_masks
          _snake_case				= mask_feature_prob
          _snake_case				= mask_feature_length
          _snake_case				= mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          _snake_case				= num_codevectors_per_group
          _snake_case				= num_codevector_groups
          _snake_case				= contrastive_logits_temperature
          _snake_case				= feat_quantizer_dropout
          _snake_case				= num_negatives
          _snake_case				= codevector_dim
          _snake_case				= proj_codevector_dim
          _snake_case				= diversity_loss_weight

          # ctc loss
          _snake_case				= ctc_loss_reduction
          _snake_case				= ctc_zero_infinity

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          _snake_case				= classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= xvector_output_dim






     @property
     def 							_lowercase      (					self       )				->     List[str]:
          return functools.reduce(operator.mul   ,self.conv_stride   ,1       )


