


"""simple docstring"""







import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
							"""simple docstring"""


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






									"""simple docstring"""





									assert os.path.isdir(snake_case__						)

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

							def __len__(						self						):






									"""simple docstring"""





									return len(self.documents						)







							def __getitem__(						self		,					snake_case__						):






									"""simple docstring"""





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

def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


		'''simple docstring'''







		lowerCAmelCase					:					str								=							list(filter(lambda SCREAMING_SNAKE_CASE    : len(SCREAMING_SNAKE_CASE    ) != 0    ,				[line.strip() for line in raw_story.split("\n"    )]    )    )

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

		# gather article lines
		lowerCAmelCase					:					Optional[int]								=							[]
		lowerCAmelCase					:					List[Any]								=							deque(SCREAMING_SNAKE_CASE    )
		while True:
				try:
						lowerCAmelCase					:					Dict								=							lines.popleft()
						if element.startswith("@highlight"    ):
								break
						story_lines.append(SCREAMING_SNAKE_CASE    )
				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					:					Dict								=							list(filter(lambda SCREAMING_SNAKE_CASE    : not t.startswith("@highlight"    )    ,				SCREAMING_SNAKE_CASE    )    )

		return story_lines, summary_lines
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ):


		'''simple docstring'''







		lowerCAmelCase					:					Dict								=							[".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
		if line.startswith("@highlight"    ):
				return line
		if line[-1] in END_TOKENS:
				return line
		return line + "."
def  a__      (    SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   int    ):


		'''simple docstring'''







		if len(SCREAMING_SNAKE_CASE    ) > block_size:
				return sequence[:block_size]
		else:
				sequence.extend([pad_token_id] * (block_size - len(SCREAMING_SNAKE_CASE    ))    )
				return sequence
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   str    ):


		'''simple docstring'''







		lowerCAmelCase					:					str								=							torch.ones_like(SCREAMING_SNAKE_CASE    )
		lowerCAmelCase					:					Union[str, Any]								=							sequence == pad_token_id
		lowerCAmelCase					:					List[str]								=							0
		return mask
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


		'''simple docstring'''







		lowerCAmelCase					:					str								=							[tokenizer.encode(SCREAMING_SNAKE_CASE    ) for line in story_lines]
		lowerCAmelCase					:					Any								=							[token for sentence in story_lines_token_ids for token in sentence]
		lowerCAmelCase					:					Optional[int]								=							[tokenizer.encode(SCREAMING_SNAKE_CASE    ) for line in summary_lines]
		lowerCAmelCase					:					Union[str, Any]								=							[token for sentence in summary_lines_token_ids for token in sentence]

		return story_token_ids, summary_token_ids





def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ):


		'''simple docstring'''







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









"""simple docstring"""







from ...configuration_utils import PretrainedConfig


lowerCAmelCase__				    =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Any       ="tapas"

       def __init__(						self		,					snake_case__=30_522		,					snake_case__=768		,					snake_case__=12		,					snake_case__=12		,					snake_case__=3_072		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=1_024		,					snake_case__=[3, 256, 256, 2, 256, 256, 10]		,					snake_case__=0.02		,					snake_case__=1e-12		,					snake_case__=0		,					snake_case__=10.0		,					snake_case__=0		,					snake_case__=1.0		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=False		,					snake_case__="ratio"		,					snake_case__=None		,					snake_case__=None		,					snake_case__=64		,					snake_case__=32		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=None		,					snake_case__=None		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(pad_token_id=snake_case__		,					**snake_case__						)

         # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
         lowerCAmelCase					:					List[Any]								=							vocab_size
         lowerCAmelCase					:					List[str]								=							hidden_size
         lowerCAmelCase					:					Optional[Any]								=							num_hidden_layers
         lowerCAmelCase					:					List[str]								=							num_attention_heads
         lowerCAmelCase					:					Optional[Any]								=							hidden_act
         lowerCAmelCase					:					List[Any]								=							intermediate_size
         lowerCAmelCase					:					Optional[Any]								=							hidden_dropout_prob
         lowerCAmelCase					:					str								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Any								=							max_position_embeddings
         lowerCAmelCase					:					Dict								=							type_vocab_sizes
         lowerCAmelCase					:					Union[str, Any]								=							initializer_range
         lowerCAmelCase					:					str								=							layer_norm_eps

         # Fine-tuning task hyperparameters
         lowerCAmelCase					:					Dict								=							positive_label_weight
         lowerCAmelCase					:					Union[str, Any]								=							num_aggregation_labels
         lowerCAmelCase					:					Optional[Any]								=							aggregation_loss_weight
         lowerCAmelCase					:					List[Any]								=							use_answer_as_supervision
         lowerCAmelCase					:					Dict								=							answer_loss_importance
         lowerCAmelCase					:					List[Any]								=							use_normalized_answer_loss
         lowerCAmelCase					:					List[str]								=							huber_loss_delta
         lowerCAmelCase					:					Optional[int]								=							temperature
         lowerCAmelCase					:					Optional[int]								=							aggregation_temperature
         lowerCAmelCase					:					Any								=							use_gumbel_for_cells
         lowerCAmelCase					:					Union[str, Any]								=							use_gumbel_for_aggregation
         lowerCAmelCase					:					Union[str, Any]								=							average_approximation_function
         lowerCAmelCase					:					int								=							cell_selection_preference
         lowerCAmelCase					:					Dict								=							answer_loss_cutoff
         lowerCAmelCase					:					Optional[int]								=							max_num_rows
         lowerCAmelCase					:					Union[str, Any]								=							max_num_columns
         lowerCAmelCase					:					Any								=							average_logits_per_cell
         lowerCAmelCase					:					List[Any]								=							select_one_column
         lowerCAmelCase					:					Tuple								=							allow_empty_column_selection
         lowerCAmelCase					:					str								=							init_cell_selection_weights_to_zero
         lowerCAmelCase					:					List[Any]								=							reset_position_index_per_cell
         lowerCAmelCase					:					Optional[Any]								=							disable_per_token_loss

         # Aggregation hyperparameters
         lowerCAmelCase					:					List[str]								=							aggregation_labels
         lowerCAmelCase					:					List[str]								=							no_aggregation_label_index

         if isinstance(self.aggregation_labels		,					snake_case__						):
           lowerCAmelCase					:					Union[str, Any]								=							{int(snake_case__						): v for k, v in aggregation_labels.items()}





