'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_=""					,lowerCamelCase_="train"							)			->			List[Any]:



         '''simple docstring'''



         assert os.path.isdir(lowerCamelCase_							)

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




      def __len__(				self							)			->			List[Any]:



         '''simple docstring'''



         return len(self.documents							)




      def __getitem__(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self.documents[idx]
         UpperCAmelCase__							:				Optional[Any]   				=  document_path.split('''/'''							)[-1]
         with open(lowerCamelCase_					,encoding='''utf-8'''							) as source:
            UpperCAmelCase__							:				Tuple   				=  source.read()
            UpperCAmelCase__				,      UpperCAmelCase__							:				str   				=  process_story(lowerCamelCase_							)
         return document_name, story_lines, summary_lines







def __UpperCamelCase(   _A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  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
   UpperCAmelCase__							:				Any   				=  [_add_missing_period(_A    ) for line in nonempty_lines]

   # gather article lines
   UpperCAmelCase__							:				List[str]   				=  []
   UpperCAmelCase__							:				Tuple   				=  deque(_A    )
   while True:
      try:
         UpperCAmelCase__							:				Tuple   				=  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
   UpperCAmelCase__							:				Dict   				=  list(filter(lambda _A    : not t.startswith('''@highlight'''    )  ,			_A    )    )

   return story_lines, summary_lines







def __UpperCamelCase(   _A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  ['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
   if line.startswith('''@highlight'''    ):
      return line
   if line[-1] in END_TOKENS:
      return line
   return line + "."







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				List[Any]  ,			_A					:				Optional[Any]    ):
   '''simple docstring'''
   if len(_A    ) > block_size:
      return sequence[:block_size]
   else:
      sequence.extend([pad_token_id] * (block_size - len(_A    ))    )
      return sequence







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Dict    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  torch.ones_like(_A    )
   UpperCAmelCase__							:				Tuple   				=  sequence == pad_token_id
   UpperCAmelCase__							:				Any   				=  0
   return mask







def __UpperCamelCase(   _A					:				int  ,			_A					:				Optional[Any]  ,			_A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				str   				=  [tokenizer.encode(_A    ) for line in story_lines]
   UpperCAmelCase__							:				Optional[int]   				=  [token for sentence in story_lines_token_ids for token in sentence]
   UpperCAmelCase__							:				Dict   				=  [tokenizer.encode(_A    ) for line in summary_lines]
   UpperCAmelCase__							:				List[Any]   				=  [token for sentence in summary_lines_token_ids for token in sentence]

   return story_token_ids, summary_token_ids







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Dict    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  []
   for sequence in batch:
      UpperCAmelCase__							:				Any   				=  -1
      UpperCAmelCase__							:				Optional[Any]   				=  []
      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'''

from typing import List, Optional, Union

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


UpperCamelCase__     :	List[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	Optional[Any]				=			{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   '''time_series_transformer'''
      UpperCAmelCase_      :      List[Any]      							=   {
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
          '''num_hidden_layers''': '''encoder_layers''',
      }




      def __init__(				self					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = "student_t"					,lowerCamelCase_ = "nll"					,lowerCamelCase_ = 1					,lowerCamelCase_ = [1, 2, 3, 4, 5, 6, 7]					,lowerCamelCase_ = "mean"					,lowerCamelCase_ = 0					,lowerCamelCase_ = 0					,lowerCamelCase_ = 0					,lowerCamelCase_ = 0					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = 32					,lowerCamelCase_ = 32					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = True					,lowerCamelCase_ = "gelu"					,lowerCamelCase_ = 64					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 100					,lowerCamelCase_ = 0.02					,lowerCamelCase_=True					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  prediction_length
         UpperCAmelCase__							:				Optional[Any]   				=  context_length or prediction_length
         UpperCAmelCase__							:				Union[str, Any]   				=  distribution_output
         UpperCAmelCase__							:				List[Any]   				=  loss
         UpperCAmelCase__							:				Optional[int]   				=  input_size
         UpperCAmelCase__							:				Tuple   				=  num_time_features
         UpperCAmelCase__							:				str   				=  lags_sequence
         UpperCAmelCase__							:				Union[str, Any]   				=  scaling
         UpperCAmelCase__							:				Optional[int]   				=  num_dynamic_real_features
         UpperCAmelCase__							:				Optional[Any]   				=  num_static_real_features
         UpperCAmelCase__							:				str   				=  num_static_categorical_features
         if cardinality and num_static_categorical_features > 0:
            if len(lowerCamelCase_							) != num_static_categorical_features:
               raise ValueError(
                   '''The cardinality should be a list of the same length as `num_static_categorical_features`'''							)
            UpperCAmelCase__							:				Union[str, Any]   				=  cardinality
         else:
            UpperCAmelCase__							:				List[str]   				=  [0]
         if embedding_dimension and num_static_categorical_features > 0:
            if len(lowerCamelCase_							) != num_static_categorical_features:
               raise ValueError(
                   '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''							)
            UpperCAmelCase__							:				int   				=  embedding_dimension
         else:
            UpperCAmelCase__							:				Union[str, Any]   				=  [min(50					,(cat + 1) // 2							) for cat in self.cardinality]
         UpperCAmelCase__							:				Dict   				=  num_parallel_samples

         # Transformer architecture configuration
         UpperCAmelCase__							:				int   				=  input_size * len(lowerCamelCase_							) + self._number_of_features
         UpperCAmelCase__							:				List[Any]   				=  d_model
         UpperCAmelCase__							:				Optional[Any]   				=  encoder_attention_heads
         UpperCAmelCase__							:				Optional[int]   				=  decoder_attention_heads
         UpperCAmelCase__							:				Union[str, Any]   				=  encoder_ffn_dim
         UpperCAmelCase__							:				Union[str, Any]   				=  decoder_ffn_dim
         UpperCAmelCase__							:				Tuple   				=  encoder_layers
         UpperCAmelCase__							:				Optional[Any]   				=  decoder_layers

         UpperCAmelCase__							:				Optional[Any]   				=  dropout
         UpperCAmelCase__							:				Any   				=  attention_dropout
         UpperCAmelCase__							:				Optional[int]   				=  activation_dropout
         UpperCAmelCase__							:				List[Any]   				=  encoder_layerdrop
         UpperCAmelCase__							:				Optional[int]   				=  decoder_layerdrop

         UpperCAmelCase__							:				List[Any]   				=  activation_function
         UpperCAmelCase__							:				List[Any]   				=  init_std

         UpperCAmelCase__							:				Optional[Any]   				=  use_cache

         super().__init__(is_encoder_decoder=lowerCamelCase_					,**lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         return (
             sum(self.embedding_dimension							)
             + self.num_dynamic_real_features
             + self.num_time_features
             + self.num_static_real_features
             + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
         )




