



"""simple docstring"""





import os
from collections import deque

import torch
from torch.utils.data import Dataset
class    lowercase__     (					lowerCamelCase__   ):



       def __init__(							self    :						Union[str, Any]	,       _UpperCAmelCase    :						Any=""	,       _UpperCAmelCase    :						Any="train"					)				->		str:


             '''simple docstring'''




             assert os.path.isdir(__lowerCamelCase					)

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



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


             '''simple docstring'''




             return len(self.documents					)







       def __getitem__(							self    :						List[str]	,       _UpperCAmelCase    :						Optional[int]					)				->		List[str]:


             '''simple docstring'''




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



def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							list(filter(lambda lowerCAmelCase__   : len(lowerCamelCase_   ) != 0			,    [line.strip() for line in raw_story.split("\n"   )]   )   )

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

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

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

      if len(lowerCamelCase_   ) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(lowerCamelCase_   ))   )
            return sequence
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							torch.ones_like(lowerCamelCase_   )
      UpperCAmelCase_    				=							sequence == pad_token_id
      UpperCAmelCase_    				=							0
      return mask
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[tokenizer.encode(lowerCamelCase_   ) for line in story_lines]
      UpperCAmelCase_    				=							[token for sentence in story_lines_token_ids for token in sentence]
      UpperCAmelCase_    				=							[tokenizer.encode(lowerCamelCase_   ) for line in summary_lines]
      UpperCAmelCase_    				=							[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids


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









"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''mobilenet_v1'''



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						int=3	,       _UpperCAmelCase    :						Union[str, Any]=224	,       _UpperCAmelCase    :						Any=1.0	,       _UpperCAmelCase    :						Any=8	,       _UpperCAmelCase    :						List[Any]="relu6"	,       _UpperCAmelCase    :						List[str]=True	,       _UpperCAmelCase    :						Dict=0.999	,       _UpperCAmelCase    :						List[Any]=0.02	,       _UpperCAmelCase    :						List[Any]=0.001	,       **_UpperCAmelCase    :						str	,       )				->		Optional[int]:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)

             if depth_multiplier <= 0:
                   raise ValueError("depth_multiplier must be greater than zero."					)

             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							image_size
             UpperCAmelCase_    				=							depth_multiplier
             UpperCAmelCase_    				=							min_depth
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							tf_padding
             UpperCAmelCase_    				=							classifier_dropout_prob
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  version.parse('''1.11'''   )



       @property
       def   lowercase__       (							self    :						List[Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             return OrderedDict([("pixel_values", {0: "batch"})]					)



       @property
       def   lowercase__       (							self    :						List[Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "image-classification":
                   return OrderedDict([("logits", {0: "batch"})]					)
             else:
                   return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]					)







       @property
       def   lowercase__       (							self    :						Tuple					)				->		float:


             '''simple docstring'''




             return 1e-4




