



'''simple docstring'''





import os
from collections import deque

import torch
from torch.utils.data import Dataset






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase=""  ,__UpperCAmelCase="train"      )    ->		Optional[int]:
      assert os.path.isdir(__UpperCAmelCase      )

      lowerCAmelCase__ :  Any							      =					[]
      lowerCAmelCase__ :  List[Any]							      =					os.listdir(__UpperCAmelCase      )
      for story_filename in story_filenames_list:
          if "summary" in story_filename:
              continue
          lowerCAmelCase__ :  Union[str, Any]							      =					os.path.join(__UpperCAmelCase  ,__UpperCAmelCase      )
          if not os.path.isfile(__UpperCAmelCase      ):
              continue
          self.documents.append(__UpperCAmelCase      )




  def __len__(    self      )    ->		Optional[Any]:
      return len(self.documents      )






  def __getitem__(    self  ,__UpperCAmelCase      )    ->		Tuple:
      lowerCAmelCase__ :  Tuple							      =					self.documents[idx]
      lowerCAmelCase__ :  List[str]							      =					document_path.split("""/"""      )[-1]
      with open(__UpperCAmelCase  ,encoding="""utf-8"""      ) as source:
          lowerCAmelCase__ :  Tuple							      =					source.read()
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					process_story(__UpperCAmelCase      )
      return document_name, story_lines, summary_lines







def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					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
    lowerCAmelCase__ :  List[Any]							      =					[_add_missing_period(UpperCamelCase			) for line in nonempty_lines]

    # gather article lines
    lowerCAmelCase__ :  Optional[Any]							      =					[]
    lowerCAmelCase__ :  List[Any]							      =					deque(UpperCamelCase			)
    while True:
        try:
            lowerCAmelCase__ :  Union[str, 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
    lowerCAmelCase__ :  List[str]							      =					list(filter(lambda UpperCamelCase			: not t.startswith("""@highlight"""			)							,    UpperCamelCase			)			)

    return story_lines, summary_lines
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
    if line.startswith("""@highlight"""			):
        return line
    if line[-1] in END_TOKENS:
        return line
    return line + "."
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					torch.ones_like(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					sequence == pad_token_id
    lowerCAmelCase__ :  Dict							      =					0
    return mask
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					[tokenizer.encode(UpperCamelCase			) for line in story_lines]
    lowerCAmelCase__ :  Tuple							      =					[token for sentence in story_lines_token_ids for token in sentence]
    lowerCAmelCase__ :  Optional[int]							      =					[tokenizer.encode(UpperCamelCase			) for line in summary_lines]
    lowerCAmelCase__ :  List[str]							      =					[token for sentence in summary_lines_token_ids for token in sentence]

    return story_token_ids, summary_token_ids




def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					[]
    for sequence in batch:
        lowerCAmelCase__ :  Optional[int]							      =					-1
        lowerCAmelCase__ :  Optional[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"""


from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Any   							=							'efficientnet'


    def __init__(    self		:     Any      , __lowerCAmelCase		:     int = 3      , __lowerCAmelCase		:     int = 600      , __lowerCAmelCase		:     float = 2.0      , __lowerCAmelCase		:     float = 3.1      , __lowerCAmelCase		:     int = 8      , __lowerCAmelCase		:     List[int] = [3, 3, 5, 3, 5, 5, 3]      , __lowerCAmelCase		:     List[int] = [32, 16, 24, 40, 80, 112, 192]      , __lowerCAmelCase		:     List[int] = [16, 24, 40, 80, 112, 192, 320]      , __lowerCAmelCase		:     List[int] = []      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 2, 1, 2, 1]      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 3, 3, 4, 1]      , __lowerCAmelCase		:     List[int] = [1, 6, 6, 6, 6, 6, 6]      , __lowerCAmelCase		:     float = 0.25      , __lowerCAmelCase		:     str = "swish"      , __lowerCAmelCase		:     int = 2560      , __lowerCAmelCase		:     str = "mean"      , __lowerCAmelCase		:     float = 0.02      , __lowerCAmelCase		:     float = 0.001      , __lowerCAmelCase		:     float = 0.99      , __lowerCAmelCase		:     float = 0.5      , __lowerCAmelCase		:     float = 0.2      , **__lowerCAmelCase		:     List[Any]      , ):
      super().__init__(**__lowerCAmelCase    )

      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					width_coefficient
      _UpperCAmelCase         =					depth_coefficient
      _UpperCAmelCase         =					depth_divisor
      _UpperCAmelCase         =					kernel_sizes
      _UpperCAmelCase         =					in_channels
      _UpperCAmelCase         =					out_channels
      _UpperCAmelCase         =					depthwise_padding
      _UpperCAmelCase         =					strides
      _UpperCAmelCase         =					num_block_repeats
      _UpperCAmelCase         =					expand_ratios
      _UpperCAmelCase         =					squeeze_expansion_ratio
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					hidden_dim
      _UpperCAmelCase         =					pooling_type
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					batch_norm_eps
      _UpperCAmelCase         =					batch_norm_momentum
      _UpperCAmelCase         =					dropout_rate
      _UpperCAmelCase         =					drop_connect_rate
      _UpperCAmelCase         =					sum(__lowerCAmelCase    ) * 4



class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Dict   							=							version.parse('1.11'      )


    @property
    def    lowerCAmelCase_     (    self		:     Any    ):
      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]    )


    @property
    def    lowerCAmelCase_     (    self		:     int    ):
      return 1e-5
