

"""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,
)


a__			:						Optional[Any]							=			logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


a__			:						str							=			namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])





def 					__UpperCamelCase (     a__:			List[str]      )					->      Tuple:




					"""simple docstring"""



					__lowercase        =      BertTokenizer.from_pretrained('''bert-base-uncased'''			,						do_lower_case=A__      )
					__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=A__			,						length_limit=args.beam_size			,						length_limit_type='''words'''			,						apply_avg=A__			,						apply_best=A__			,						alpha=0.5			,						weight_factor=1.2			,						stemming=A__			,						)

					# 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(A__			,						A__      )
					__lowercase        =      build_predictor(A__			,						A__			,						A__			,						A__      )

					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(A__      ):
										__lowercase        =      predictor.translate_batch(A__      )
										__lowercase        =      predictor.from_batch(A__      )
										__lowercase        =      [format_summary(A__      ) for t in translations]
										save_summaries(A__			,						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(A__			,						A__      )
										__lowercase        =      format_rouge_scores(A__      )
										save_rouge_scores(A__      )
										print(A__      )





def 					__UpperCamelCase (     a__:			Union[str, Any]			,						a__:			Dict			,						a__:			Any      )					->      Optional[int]:




					"""simple docstring"""



					for summary, document_name in zip(A__			,						A__      ):
										# 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(A__			,						A__      )
										with open(A__			,						'''w'''      ) as output:
															output.write(A__      )





def 					__UpperCamelCase (     a__:			List[Any]      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      translation
					__lowercase        =      (
					    raw_summary.replace('''[unused0]'''			,						''''''      )
					    .replace('''[unused3]'''			,						''''''      )
					    .replace('''[PAD]'''			,						''''''      )
					    .replace('''[unused1]'''			,						''''''      )
					    .replace(r''' +'''			,						''' '''      )
					    .replace(''' [unused2] '''			,						'''. '''      )
					    .replace('''[unused2]'''			,						''''''      )
					    .strip()
					)

					return summary





def 					__UpperCamelCase (     a__:			int      )					->      Dict:




					"""simple docstring"""



					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 					__UpperCamelCase (     a__:			int      )					->      Union[str, Any]:




					"""simple docstring"""



					with open('''rouge_scores.txt'''			,						'''w'''      ) as output:
										output.write(A__      )





def 					__UpperCamelCase (     a__:			Union[str, Any]			,						a__:			int      )					->      Optional[int]:




					"""simple docstring"""



					__lowercase        =      load_and_cache_examples(A__			,						A__      )
					__lowercase        =      SequentialSampler(A__      )

					def collate_fn(a__:			Union[str, Any]      ):
										return collate(A__			,						A__			,						block_size=5_12			,						device=args.device      )

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

					return iterator





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			Tuple      )					->      str:




					"""simple docstring"""



					__lowercase        =      CNNDMDataset(args.documents_dir      )
					return dataset





def 					__UpperCamelCase (     a__:			Dict			,						a__:			Optional[Any]			,						a__:			Tuple			,						a__:			Optional[int]      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      [x for x in data if not len(x[1]      ) == 0]  # remove empty_files
					__lowercase        =      [name for name, _, _ in data]
					__lowercase        =      [""" """.join(A__      ) for _, _, summary_list in data]

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

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

					return batch





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			List[str]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      summary_tokens.to('''cpu'''      ).numpy()
					__lowercase        =      tokenizer.decode(A__      )
					__lowercase        =      summary.split('''.'''      )
					__lowercase        =      [s + """.""" for s in sentences]
					return sentences





def 					__UpperCamelCase (     )					->      Dict:




					"""simple docstring"""



					__lowercase        =      argparse.ArgumentParser()
					parser.add_argument(
					    '''--documents_dir'''			,						default=A__			,						type=A__			,						required=A__			,						help='''The folder where the documents to summarize are located.'''			,						)
					parser.add_argument(
					    '''--summaries_output_dir'''			,						default=A__			,						type=A__			,						required=A__			,						help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are'''			,						)
					parser.add_argument(
					    '''--compute_rouge'''			,						default=A__			,						type=A__			,						required=A__			,						help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'''			,						)
					# EVALUATION options
					parser.add_argument(
					    '''--no_cuda'''			,						default=A__			,						type=A__			,						help='''Whether to force the execution on CPU.'''			,						)
					parser.add_argument(
					    '''--batch_size'''			,						default=4			,						type=A__			,						help='''Batch size per GPU/CPU for training.'''			,						)
					# BEAM SEARCH arguments
					parser.add_argument(
					    '''--min_length'''			,						default=50			,						type=A__			,						help='''Minimum number of tokens for the summaries.'''			,						)
					parser.add_argument(
					    '''--max_length'''			,						default=2_00			,						type=A__			,						help='''Maixmum number of tokens for the summaries.'''			,						)
					parser.add_argument(
					    '''--beam_size'''			,						default=5			,						type=A__			,						help='''The number of beams to start with for each example.'''			,						)
					parser.add_argument(
					    '''--alpha'''			,						default=0.95			,						type=A__			,						help='''The value of alpha for the length penalty in the beam search.'''			,						)
					parser.add_argument(
					    '''--block_trigram'''			,						default=A__			,						type=A__			,						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=A__      )

					evaluate(A__      )





def 					__UpperCamelCase (     a__:			Dict      )					->      Dict:




					"""simple docstring"""



					if not os.path.exists(A__      ):
										return False

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

					return True


if __name__ == "__main__":
							main()







# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List
def   UpperCAmelCase							(		A__				,  A__     )							->					Any:
	if name != "training_function" and name != "main":
		raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''     )
	_snake_case     , _snake_case					:							Union[str, Any]					     =   [], False
	for line in lines:
		if not found_start and f'''def {name}''' in line:
			_snake_case					:							Tuple					     =   True
			good_lines.append(A__     )
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(A__     )
def   UpperCAmelCase							(		A__     )							->					Dict:
	return [line for line in lines if not line.lstrip().startswith("""#"""     ) and line != "\n"]


def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__ = None     )							->					str:
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Tuple					     =   f.readlines()
	with open(os.path.abspath(os.path.join("""examples"""				,  """nlp_example.py"""     )     )				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	if secondary_filename is not None:
		with open(A__				,  """r"""     ) as f:
			_snake_case					:							List[str]					     =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		_snake_case					:							Dict					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							int					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		if secondary_filename is not None:
			_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
	else:
		_snake_case					:							str					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							List[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							Union[str, Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		if secondary_filename is not None:
			_snake_case					:							List[Any]					     =   clean_lines(
			    get_function_contents_by_name(A__				,  """training_function"""     )     )

	_snake_case					:							str					     =   """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

	# Specific code in our script that differs from the full version, aka what is new
	_snake_case					:							Dict					     =   []
	_snake_case					:							str					     =   []  # We keep track of the idxs just in case it's a repeated statement
	_snake_case					:							Optional[Any]					     =   iter(A__     )
	for i in range(len(A__     ) - 1     ):
		if i not in passed_idxs:
			_snake_case					:							Any					     =   next(A__     )
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(A__     )
					passed_idxs.append(A__     )
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					_snake_case					:							Optional[int]					     =   next(A__     )

    # Extract out just the new parts from the full_file_training_func
	_snake_case					:							Any					     =   []
	_snake_case					:							Tuple					     =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(A__     ):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(A__     )
					passed_idxs.append(A__     )

    # Finally, get the overall diff
	_snake_case					:							Optional[int]					     =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		_snake_case					:							Optional[int]					     =   [line for line in full_file_contents if line not in secondary_file_func]
		_snake_case					:							int					     =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example





