




"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset






class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase=""  ,						lowerCamelCase="train"		):
        assert os.path.isdir(lowerCamelCase		)

        __a     				=  []
        __a     				=  os.listdir(lowerCamelCase		)
        for story_filename in story_filenames_list:
               if "summary" in story_filename:
                      continue
               __a     				=  os.path.join(lowerCamelCase  ,						lowerCamelCase		)
               if not os.path.isfile(lowerCamelCase		):
                      continue
               self.documents.append(lowerCamelCase		)


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






 def __getitem__(				self  ,						lowerCamelCase		):
        __a     				=  self.documents[idx]
        __a     				=  document_path.split("/"		)[-1]
        with open(lowerCamelCase  ,						encoding="utf-8"		) as source:
               __a     				=  source.read()
               __a				, __a     				=  process_story(lowerCamelCase		)
        return document_name, story_lines, summary_lines



def 					_lowerCamelCase(					a							):
       __a     				=  list(filter(lambda a							: len(a							) != 0 ,  [line.strip() for line in raw_story.split("\n"							)]							)							)

       # for some unknown reason some lines miss a period, add it
       __a     				=  [_add_missing_period(a							) for line in nonempty_lines]

       # gather article lines
       __a     				=  []
       __a     				=  deque(a							)
       while True:
              try:
                     __a     				=  lines.popleft()
                     if element.startswith("@highlight"							):
                            break
                     story_lines.append(a							)
              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
       __a     				=  list(filter(lambda a							: not t.startswith("@highlight"							) ,  a							)							)

       return story_lines, summary_lines



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



def 					_lowerCamelCase(					a ,  a ,  a							):
       if len(a							) > block_size:
              return sequence[:block_size]
       else:
              sequence.extend([pad_token_id] * (block_size - len(a							))							)
              return sequence



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  torch.ones_like(a							)
       __a     				=  sequence == pad_token_id
       __a     				=  0
       return mask



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  [tokenizer.encode(a							) for line in story_lines]
       __a     				=  [token for sentence in story_lines_token_ids for token in sentence]
       __a     				=  [tokenizer.encode(a							) for line in summary_lines]
       __a     				=  [token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids




def 					_lowerCamelCase(					a ,  a							):
       __a     				=  []
       for sequence in batch:
              __a     				=  -1
              __a     				=  []
              for s in sequence:
                     if s == separator_token_id:
                            sentence_num += 1
                     embeddings.append(sentence_num % 2							)
              batch_embeddings.append(a							)
       return torch.tensor(a							)

"""simple docstring"""



import functools
import operator

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


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

SCREAMING_SNAKE_CASE__:str			=	{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		"""sew-d"""


 def __init__(				self  ,						lowerCamelCase=32  ,						lowerCamelCase=768  ,						lowerCamelCase=12  ,						lowerCamelCase=12  ,						lowerCamelCase=3072  ,						lowerCamelCase=2  ,						lowerCamelCase=512  ,						lowerCamelCase=256  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=("p2c", "c2p")  ,						lowerCamelCase="layer_norm"  ,						lowerCamelCase="gelu_python"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.02  ,						lowerCamelCase=1E-7  ,						lowerCamelCase=1E-5  ,						lowerCamelCase="group"  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,						lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,						lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,						lowerCamelCase=False  ,						lowerCamelCase=128  ,						lowerCamelCase=16  ,						lowerCamelCase=True  ,						lowerCamelCase=0.05  ,						lowerCamelCase=10  ,						lowerCamelCase=2  ,						lowerCamelCase=0.0  ,						lowerCamelCase=10  ,						lowerCamelCase=0  ,						lowerCamelCase="mean"  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=256  ,						lowerCamelCase=0  ,						lowerCamelCase=1  ,						lowerCamelCase=2  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase  ,						pad_token_id=lowerCamelCase  ,						bos_token_id=lowerCamelCase  ,						eos_token_id=lowerCamelCase		)
        __a     				=  hidden_size
        __a     				=  feat_extract_norm
        __a     				=  feat_extract_activation
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  conv_bias
        __a     				=  num_conv_pos_embeddings
        __a     				=  num_conv_pos_embedding_groups
        __a     				=  len(self.conv_dim		)
        __a     				=  num_hidden_layers
        __a     				=  intermediate_size
        __a     				=  squeeze_factor
        __a     				=  max_position_embeddings
        __a     				=  position_buckets
        __a     				=  share_att_key
        __a     				=  relative_attention
        __a     				=  norm_rel_ebd
        __a     				=  list(lowerCamelCase		)
        __a     				=  hidden_act
        __a     				=  num_attention_heads
        __a     				=  hidden_dropout
        __a     				=  attention_dropout
        __a     				=  activation_dropout
        __a     				=  feat_proj_dropout
        __a     				=  final_dropout
        __a     				=  layer_norm_eps
        __a     				=  feature_layer_norm_eps
        __a     				=  initializer_range
        __a     				=  vocab_size

        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)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."		)

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

        # ctc loss
        __a     				=  ctc_loss_reduction
        __a     				=  ctc_zero_infinity

        # sequence classification
        __a     				=  use_weighted_layer_sum
        __a     				=  classifier_proj_size






 @property
 def 		a__ (				self		):
        return functools.reduce(operator.mul  ,						self.conv_stride  ,						1		)
