



import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import (
	    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)

if is_tf_available() and is_tensorflow_probability_available():
	import tensorflow as tf
	import tensorflow_probability as tfp

	from ..models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)






class 		lowercase						( lowerCAmelCase__):






	"""simple docstring"""


	def __call__(	self	:						List[Any]						,  __UpperCAmelCase	:						int=None						,  __UpperCAmelCase	:						Any=None						,  **__UpperCAmelCase	:						Dict     )			-> str:
							requires_backends(self						,  """pandas"""     )
							import pandas as pd

							if table is None:
													raise ValueError("""Keyword argument `table` cannot be None."""     )
							elif query is None:
													if isinstance(_lowerCamelCase						,  _lowerCamelCase     ) and table.get("""query"""     ) is not None and table.get("""table"""     ) is not None:
																			UpperCAmelCase_=   [table]
													elif isinstance(_lowerCamelCase						,  _lowerCamelCase     ) and len(_lowerCamelCase     ) > 0:
																			if not all(isinstance(_lowerCamelCase						,  _lowerCamelCase     ) for d in table     ):
																									raise ValueError(
																									    F"""Keyword argument `table` should be a list of dict, but is {(type(_lowerCamelCase     ) for d in table)}"""     )

																			if table[0].get("""query"""     ) is not None and table[0].get("""table"""     ) is not None:
																									UpperCAmelCase_=   table
																			else:
																									raise ValueError(
																									    """If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"""
																									    F""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."""     )
													elif Dataset is not None and isinstance(_lowerCamelCase						,  _lowerCamelCase     ) or isinstance(_lowerCamelCase						,  types.GeneratorType     ):
																			return table
													else:
																			raise ValueError(
																			    """Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but """
																			    F"""is {type(_lowerCamelCase     )})"""     )
							else:
													UpperCAmelCase_=   [{"""table""": table, """query""": query}]

							for tqa_pipeline_input in tqa_pipeline_inputs:
													if not isinstance(tqa_pipeline_input["""table"""]						,  pd.DataFrame     ):
																			if tqa_pipeline_input["table"] is None:
																									raise ValueError("""Table cannot be None."""     )

																			UpperCAmelCase_=   pd.DataFrame(tqa_pipeline_input["""table"""]     )

							return tqa_pipeline_inputs






@add_end_docstrings(lowerCAmelCase__)
class 		lowercase						( lowerCAmelCase__):






	"""simple docstring"""


	a__      :					str		   =							"table,query"




	def __init__(	self	:						List[Any]						,  __UpperCAmelCase	:						str=TableQuestionAnsweringArgumentHandler()						,  *__UpperCAmelCase	:						str						,  **__UpperCAmelCase	:						List[str]     )			-> Optional[int]:
							super().__init__(*_lowerCamelCase						,  **_lowerCamelCase     )
							UpperCAmelCase_=   args_parser

							self.check_model_type(
							    dict(
							        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
							        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()     )
							    if self.framework == """tf"""
							    else dict(
							        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()     )     )

							UpperCAmelCase_=   bool(getattr(self.model.config						,  """aggregation_labels"""						,  _lowerCamelCase     )     ) and bool(
							    getattr(self.model.config						,  """num_aggregation_labels"""						,  _lowerCamelCase     )     )
							UpperCAmelCase_=   """tapas""" if hasattr(self.model.config						,  """aggregation_labels"""     ) else None




	def   _SCREAMING_SNAKE_CASE		(	self	:						int						,  **__UpperCAmelCase	:						Dict     )			-> str:
							return self.model(**_lowerCamelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  **__UpperCAmelCase	:						Optional[int]     )			-> Union[str, Any]:





							if self.framework == "pt":
													UpperCAmelCase_=   []
													UpperCAmelCase_=   []
													UpperCAmelCase_=   None
													UpperCAmelCase_=   inputs["""input_ids"""].shape[0]

													UpperCAmelCase_=   inputs["""input_ids"""].to(self.device     )
													UpperCAmelCase_=   inputs["""attention_mask"""].to(self.device     )
													UpperCAmelCase_=   inputs["""token_type_ids"""].to(self.device     )
													UpperCAmelCase_=   None

													for index in range(_lowerCamelCase     ):
																			# If sequences have already been processed, the token type IDs will be created according to the previous
																			# answer.
																			if prev_answers is not None:
																									UpperCAmelCase_=   token_type_ids_example[:, 3]  # shape (seq_len,)
																									UpperCAmelCase_=   np.zeros_like(prev_labels_example.cpu().numpy()     )  # shape (seq_len,)

																									UpperCAmelCase_=   token_type_ids[index]  # shape (seq_len, 7)
																									for i in range(model_labels.shape[0]     ):
																															UpperCAmelCase_=   token_type_ids_example[:, 0].tolist()[i]
																															UpperCAmelCase_=   token_type_ids_example[:, 1].tolist()[i] - 1
																															UpperCAmelCase_=   token_type_ids_example[:, 2].tolist()[i] - 1

																															if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																					UpperCAmelCase_=   int(prev_answers[(col_id, row_id)]     )

																									UpperCAmelCase_=   torch.from_numpy(_lowerCamelCase     ).type(torch.long     ).to(self.device     )

																			UpperCAmelCase_=   input_ids[index]
																			UpperCAmelCase_=   attention_mask[index]  # shape (seq_len,)
																			UpperCAmelCase_=   token_type_ids[index]  # shape (seq_len, 7)
																			UpperCAmelCase_=   self.model(
																			    input_ids=input_ids_example.unsqueeze(0     )						,  attention_mask=attention_mask_example.unsqueeze(0     )						,  token_type_ids=token_type_ids_example.unsqueeze(0     )						,  )
																			UpperCAmelCase_=   outputs.logits

																			if self.aggregate:
																									all_aggregations.append(outputs.logits_aggregation     )

																			all_logits.append(_lowerCamelCase     )

																			UpperCAmelCase_=   torch.distributions.Bernoulli(logits=_lowerCamelCase     )
																			UpperCAmelCase_=   dist_per_token.probs * attention_mask_example.type(torch.floataa     ).to(
																			    dist_per_token.probs.device     )

																			UpperCAmelCase_=   collections.defaultdict(_lowerCamelCase     )
																			for i, p in enumerate(probabilities.squeeze().tolist()     ):
																									UpperCAmelCase_=   token_type_ids_example[:, 0].tolist()[i]
																									UpperCAmelCase_=   token_type_ids_example[:, 1].tolist()[i] - 1
																									UpperCAmelCase_=   token_type_ids_example[:, 2].tolist()[i] - 1
																									if col >= 0 and row >= 0 and segment_id == 1:
																															coords_to_probs[(col, row)].append(_lowerCamelCase     )

																			UpperCAmelCase_=   {key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

													UpperCAmelCase_=   torch.cat(tuple(_lowerCamelCase     )						,  0     )

													return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_lowerCamelCase     )						,  0     ))
							else:
													UpperCAmelCase_=   []
													UpperCAmelCase_=   []
													UpperCAmelCase_=   None
													UpperCAmelCase_=   inputs["""input_ids"""].shape[0]

													UpperCAmelCase_=   inputs["""input_ids"""]
													UpperCAmelCase_=   inputs["""attention_mask"""]
													UpperCAmelCase_=   inputs["""token_type_ids"""].numpy()
													UpperCAmelCase_=   None

													for index in range(_lowerCamelCase     ):
																			# If sequences have already been processed, the token type IDs will be created according to the previous
																			# answer.
																			if prev_answers is not None:
																									UpperCAmelCase_=   token_type_ids_example[:, 3]  # shape (seq_len,)
																									UpperCAmelCase_=   np.zeros_like(_lowerCamelCase						,  dtype=np.intaa     )  # shape (seq_len,)

																									UpperCAmelCase_=   token_type_ids[index]  # shape (seq_len, 7)
																									for i in range(model_labels.shape[0]     ):
																															UpperCAmelCase_=   token_type_ids_example[:, 0].tolist()[i]
																															UpperCAmelCase_=   token_type_ids_example[:, 1].tolist()[i] - 1
																															UpperCAmelCase_=   token_type_ids_example[:, 2].tolist()[i] - 1

																															if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																					UpperCAmelCase_=   int(prev_answers[(col_id, row_id)]     )

																									UpperCAmelCase_=   model_labels

																			UpperCAmelCase_=   input_ids[index]
																			UpperCAmelCase_=   attention_mask[index]  # shape (seq_len,)
																			UpperCAmelCase_=   token_type_ids[index]  # shape (seq_len, 7)
																			UpperCAmelCase_=   self.model(
																			    input_ids=np.expand_dims(_lowerCamelCase						,  axis=0     )						,  attention_mask=np.expand_dims(_lowerCamelCase						,  axis=0     )						,  token_type_ids=np.expand_dims(_lowerCamelCase						,  axis=0     )						,  )
																			UpperCAmelCase_=   outputs.logits

																			if self.aggregate:
																									all_aggregations.append(outputs.logits_aggregation     )

																			all_logits.append(_lowerCamelCase     )

																			UpperCAmelCase_=   tfp.distributions.Bernoulli(logits=_lowerCamelCase     )
																			UpperCAmelCase_=   dist_per_token.probs_parameter() * tf.cast(_lowerCamelCase						,  tf.floataa     )

																			UpperCAmelCase_=   collections.defaultdict(_lowerCamelCase     )
																			UpperCAmelCase_=   token_type_ids_example
																			for i, p in enumerate(tf.squeeze(_lowerCamelCase     ).numpy().tolist()     ):
																									UpperCAmelCase_=   token_type_ids_example[:, 0].tolist()[i]
																									UpperCAmelCase_=   token_type_ids_example[:, 1].tolist()[i] - 1
																									UpperCAmelCase_=   token_type_ids_example[:, 2].tolist()[i] - 1
																									if col >= 0 and row >= 0 and segment_id == 1:
																															coords_to_probs[(col, row)].append(_lowerCamelCase     )

																			UpperCAmelCase_=   {key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

													UpperCAmelCase_=   tf.concat(tuple(_lowerCamelCase     )						,  0     )

													return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(_lowerCamelCase     )						,  0     ))




	def __call__(	self	:						Dict						,  *__UpperCAmelCase	:						Any						,  **__UpperCAmelCase	:						Union[str, Any]     )			-> Union[str, Any]:
							UpperCAmelCase_=   self._args_parser(*_lowerCamelCase						,  **_lowerCamelCase     )

							UpperCAmelCase_=   super().__call__(_lowerCamelCase						,  **_lowerCamelCase     )
							if len(_lowerCamelCase     ) == 1:
													return results[0]
							return results




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple						,  __UpperCAmelCase	:						List[Any]=None						,  __UpperCAmelCase	:						Any=None						,  __UpperCAmelCase	:						List[Any]=None						,  **__UpperCAmelCase	:						Tuple     )			-> Union[str, Any]:
							UpperCAmelCase_=   {}
							if padding is not None:
													UpperCAmelCase_=   padding
							if truncation is not None:
													UpperCAmelCase_=   truncation

							UpperCAmelCase_=   {}
							if sequential is not None:
													UpperCAmelCase_=   sequential
							return preprocess_params, forward_params, {}




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any						,  __UpperCAmelCase	:						List[Any]						,  __UpperCAmelCase	:						Tuple=None						,  __UpperCAmelCase	:						List[str]=True						,  __UpperCAmelCase	:						Optional[int]=None     )			-> Tuple:





							if truncation is None:
													if self.type == "tapas":
																			UpperCAmelCase_=   """drop_rows_to_fit"""
													else:
																			UpperCAmelCase_=   """do_not_truncate"""

							UpperCAmelCase_,   UpperCAmelCase_=   pipeline_input["""table"""], pipeline_input["""query"""]
							if table.empty:
													raise ValueError("""table is empty"""     )
							if query is None or query == "":
													raise ValueError("""query is empty"""     )
							UpperCAmelCase_=   self.tokenizer(_lowerCamelCase						,  _lowerCamelCase						,  return_tensors=self.framework						,  truncation=_lowerCamelCase						,  padding=_lowerCamelCase     )
							UpperCAmelCase_=   table
							return inputs




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						Union[str, Any]=False     )			-> Tuple:
							UpperCAmelCase_=   model_inputs.pop("""table"""     )

							if self.type == "tapas":
													if sequential:
																			UpperCAmelCase_=   self.sequential_inference(**_lowerCamelCase     )
													else:
																			UpperCAmelCase_=   self.batch_inference(**_lowerCamelCase     )
							else:
													UpperCAmelCase_=   self.model.generate(**_lowerCamelCase     )
							UpperCAmelCase_=   {"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
							return model_outputs




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]						,  __UpperCAmelCase	:						Optional[Any]     )			-> int:
							UpperCAmelCase_=   model_outputs["""model_inputs"""]
							UpperCAmelCase_=   model_outputs["""table"""]
							UpperCAmelCase_=   model_outputs["""outputs"""]
							if self.type == "tapas":
													if self.aggregate:
																			UpperCAmelCase_,   UpperCAmelCase_=   outputs[:2]
																			UpperCAmelCase_=   self.tokenizer.convert_logits_to_predictions(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase     )
																			UpperCAmelCase_,   UpperCAmelCase_=   predictions
																			UpperCAmelCase_=   {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_lowerCamelCase     )}

																			UpperCAmelCase_=   self.model.config.no_aggregation_label_index
																			UpperCAmelCase_=   {
																			    i: aggregators[i] + """ > """ for i, pred in enumerate(_lowerCamelCase     ) if pred != no_agg_label_index
																			}
													else:
																			UpperCAmelCase_=   outputs[0]
																			UpperCAmelCase_=   self.tokenizer.convert_logits_to_predictions(_lowerCamelCase						,  _lowerCamelCase     )
																			UpperCAmelCase_=   predictions[0]
																			UpperCAmelCase_=   {}
																			UpperCAmelCase_=   {}
													UpperCAmelCase_=   []
													for index, coordinates in enumerate(_lowerCamelCase     ):
																			UpperCAmelCase_=   [table.iat[coordinate] for coordinate in coordinates]
																			UpperCAmelCase_=   aggregators.get(_lowerCamelCase						,  """"""     )
																			UpperCAmelCase_=   aggregators_prefix.get(_lowerCamelCase						,  """"""     )
																			UpperCAmelCase_=   {
																			    """answer""": aggregator_prefix + """, """.join(_lowerCamelCase     ),
																			    """coordinates""": coordinates,
																			    """cells""": [table.iat[coordinate] for coordinate in coordinates],
																			}
																			if aggregator:
																									UpperCAmelCase_=   aggregator

																			answers.append(_lowerCamelCase     )
													if len(_lowerCamelCase     ) == 0:
																			raise PipelineException("""Empty answer"""     )
							else:
													UpperCAmelCase_=   [{"""answer""": answer} for answer in self.tokenizer.batch_decode(_lowerCamelCase						,  skip_special_tokens=_lowerCamelCase     )]

							return answers if len(_lowerCamelCase     ) > 1 else answers[0]











'''simple docstring'''



import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


_SCREAMING_SNAKE_CASE    =			logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


_SCREAMING_SNAKE_CASE    =			namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):
			__lowercase   =							BertTokenizer.from_pretrained('''bert-base-uncased'''						,				do_lower_case=lowerCamelCase_ )
			__lowercase   =							BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization''' )
			model.to(args.device )
			model.eval()

			__lowercase   =							{
			    '''BOS''': tokenizer.vocab['''[unused0]'''],
			    '''EOS''': tokenizer.vocab['''[unused1]'''],
			    '''PAD''': tokenizer.vocab['''[PAD]'''],
			}

			if args.compute_rouge:
						__lowercase   =							[]
						__lowercase   =							[]

						import nltk
						import rouge

						nltk.download('''punkt''' )
						__lowercase   =							rouge.Rouge(
						    metrics=['''rouge-n''', '''rouge-l''']						,				max_n=2						,				limit_length=lowerCamelCase_						,				length_limit=args.beam_size						,				length_limit_type='''words'''						,				apply_avg=lowerCamelCase_						,				apply_best=lowerCamelCase_						,				alpha=0.5						,				weight_factor=1.2						,				stemming=lowerCamelCase_						,				)

			# these (unused) arguments are defined to keep the compatibility
			# with the legacy code and will be deleted in a next iteration.
			__lowercase   =							''''''
			__lowercase   =							''''''

			__lowercase   =							build_data_iterator(lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase   =							build_predictor(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ )

			logger.info('''***** Running evaluation *****''' )
			logger.info('''  Number examples = %d'''						,				len(data_iterator.dataset ) )
			logger.info('''  Batch size = %d'''						,				args.batch_size )
			logger.info('''''' )
			logger.info('''***** Beam Search parameters *****''' )
			logger.info('''  Beam size = %d'''						,				args.beam_size )
			logger.info('''  Minimum length = %d'''						,				args.min_length )
			logger.info('''  Maximum length = %d'''						,				args.max_length )
			logger.info('''  Alpha (length penalty) = %.2f'''						,				args.alpha )
			logger.info('''  Trigrams %s be blocked'''						,				('''will''' if args.block_trigram else '''will NOT''') )

			for batch in tqdm(lowerCamelCase_ ):
						__lowercase   =							predictor.translate_batch(lowerCamelCase_ )
						__lowercase   =							predictor.from_batch(lowerCamelCase_ )
						__lowercase   =							[format_summary(lowerCamelCase_ ) for t in translations]
						save_summaries(lowerCamelCase_						,				args.summaries_output_dir						,				batch.document_names )

						if args.compute_rouge:
									reference_summaries += batch.tgt_str
									generated_summaries += summaries

			if args.compute_rouge:
						__lowercase   =							rouge_evaluator.get_scores(lowerCamelCase_						,				lowerCamelCase_ )
						__lowercase   =							format_rouge_scores(lowerCamelCase_ )
						save_rouge_scores(lowerCamelCase_ )
						print(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     str						,				lowerCamelCase_	:     Dict ):
			for summary, document_name in zip(lowerCamelCase_						,				lowerCamelCase_ ):
						# Prepare the summary file's name
						if "." in document_name:
									__lowercase   =							'''.'''.join(document_name.split('''.''' )[:-1] )
									__lowercase   =							document_name.split('''.''' )[-1]
									__lowercase   =							bare_document_name + '''_summary.''' + extension
						else:
									__lowercase   =							document_name + '''_summary'''

						__lowercase   =							os.path.join(lowerCamelCase_						,				lowerCamelCase_ )
						with open(lowerCamelCase_						,				'''w''' ) as output:
									output.write(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any] ):
			__lowercase  ,						__lowercase  ,						__lowercase   =							translation
			__lowercase   =							(
			    raw_summary.replace('''[unused0]'''						,				'''''' )
			    .replace('''[unused3]'''						,				'''''' )
			    .replace('''[PAD]'''						,				'''''' )
			    .replace('''[unused1]'''						,				'''''' )
			    .replace(r''' +'''						,				''' ''' )
			    .replace(''' [unused2] '''						,				'''. ''' )
			    .replace('''[unused2]'''						,				'''''' )
			    .strip()
			)

			return summary




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):
			return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
			    scores['''rouge-1''']['''f''']						,				scores['''rouge-1''']['''p''']						,				scores['''rouge-1''']['''r''']						,				scores['''rouge-2''']['''f''']						,				scores['''rouge-2''']['''p''']						,				scores['''rouge-2''']['''r''']						,				scores['''rouge-l''']['''f''']						,				scores['''rouge-l''']['''p''']						,				scores['''rouge-l''']['''r''']						,				)




def       _lowerCAmelCase  ( lowerCamelCase_	:     Dict ):
			with open('''rouge_scores.txt'''						,				'''w''' ) as output:
						output.write(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							load_and_cache_examples(lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase   =							SequentialSampler(lowerCamelCase_ )

			def collate_fn(lowerCamelCase_	:     List[str] ):
						return collate(lowerCamelCase_						,				lowerCamelCase_						,				block_size=5_1_2						,				device=args.device )

			__lowercase   =							DataLoader(
			    lowerCamelCase_						,				sampler=lowerCamelCase_						,				batch_size=args.batch_size						,				collate_fn=lowerCamelCase_						,				)

			return iterator




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							CNNDMDataset(args.documents_dir )
			return dataset




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Any						,				lowerCamelCase_	:     Optional[Any] ):
			__lowercase   =							[x for x in data if not len(x[1] ) == 0]  # remove empty_files
			__lowercase   =							[name for name, _, _ in data]
			__lowercase   =							[''' '''.join(lowerCamelCase_ ) for _, _, summary_list in data]

			__lowercase   =							[encode_for_summarization(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ ) for _, story, summary in data]
			__lowercase   =							torch.tensor(
			    [truncate_or_pad(lowerCamelCase_						,				lowerCamelCase_						,				tokenizer.pad_token_id ) for story, _ in encoded_text] )
			__lowercase   =							compute_token_type_ids(lowerCamelCase_						,				tokenizer.cls_token_id )
			__lowercase   =							build_mask(lowerCamelCase_						,				tokenizer.pad_token_id )

			__lowercase   =							Batch(
			    document_names=lowerCamelCase_						,				batch_size=len(lowerCamelCase_ )						,				src=encoded_stories.to(lowerCamelCase_ )						,				segs=encoder_token_type_ids.to(lowerCamelCase_ )						,				mask_src=encoder_mask.to(lowerCamelCase_ )						,				tgt_str=lowerCamelCase_						,				)

			return batch




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Union[str, Any] ):
			__lowercase   =							summary_tokens.to('''cpu''' ).numpy()
			__lowercase   =							tokenizer.decode(lowerCamelCase_ )
			__lowercase   =							summary.split('''.''' )
			__lowercase   =							[s + '''.''' for s in sentences]
			return sentences




def       _lowerCAmelCase  ( ):
			__lowercase   =							argparse.ArgumentParser()
			parser.add_argument(
			    '''--documents_dir'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				required=lowerCamelCase_						,				help='''The folder where the documents to summarize are located.'''						,				)
			parser.add_argument(
			    '''--summaries_output_dir'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				required=lowerCamelCase_						,				help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are'''						,				)
			parser.add_argument(
			    '''--compute_rouge'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				required=lowerCamelCase_						,				help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'''						,				)
			# EVALUATION options
			parser.add_argument(
			    '''--no_cuda'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				help='''Whether to force the execution on CPU.'''						,				)
			parser.add_argument(
			    '''--batch_size'''						,				default=4						,				type=lowerCamelCase_						,				help='''Batch size per GPU/CPU for training.'''						,				)
			# BEAM SEARCH arguments
			parser.add_argument(
			    '''--min_length'''						,				default=5_0						,				type=lowerCamelCase_						,				help='''Minimum number of tokens for the summaries.'''						,				)
			parser.add_argument(
			    '''--max_length'''						,				default=2_0_0						,				type=lowerCamelCase_						,				help='''Maixmum number of tokens for the summaries.'''						,				)
			parser.add_argument(
			    '''--beam_size'''						,				default=5						,				type=lowerCamelCase_						,				help='''The number of beams to start with for each example.'''						,				)
			parser.add_argument(
			    '''--alpha'''						,				default=0.95						,				type=lowerCamelCase_						,				help='''The value of alpha for the length penalty in the beam search.'''						,				)
			parser.add_argument(
			    '''--block_trigram'''						,				default=lowerCamelCase_						,				type=lowerCamelCase_						,				help='''Whether to block the existence of repeating trigrams in the text generated by beam search.'''						,				)
			__lowercase   =							parser.parse_args()

			# Select device (distibuted not available)
			__lowercase   =							torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )

			# Check the existence of directories
			if not args.summaries_output_dir:
						__lowercase   =							args.documents_dir

			if not documents_dir_is_valid(args.documents_dir ):
						raise FileNotFoundError(
						    '''We could not find the directory you specified for the documents to summarize, or it was empty. Please'''
						    ''' specify a valid path.''' )
			os.makedirs(args.summaries_output_dir						,				exist_ok=lowerCamelCase_ )

			evaluate(lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[Any] ):
			if not os.path.exists(lowerCamelCase_ ):
						return False

			__lowercase   =							os.listdir(lowerCamelCase_ )
			if len(lowerCamelCase_ ) == 0:
						return False

			return True


if __name__ == "__main__":
	main()





