import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
				import tensorflow as tf

				tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
				import torch

if is_datasets_available():
				from datasets import load_dataset


lowerCAmelCase__					:       str		=       5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
def 			UpperCamelCase__						(    A__   )       -> Union[str, Any]:
				return PTtoTFCommand(
				    args.model_name      ,			args.local_dir      ,			args.max_error      ,			args.new_weights      ,			args.no_pr      ,			args.push      ,			args.extra_commit_description      ,			args.override_model_class      ,			)





class 			__snake_case  (      _lowerCamelCase     ):




					@staticmethod
					def       __a      (       __UpperCamelCase		)					->  int:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				parser.add_parser(
									    'pt-to-tf'   ,	help=(
									        'CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'
									        ' Can also be used to validate existing weights without opening PRs, with --no-pr.'
									    )   ,	)
									train_parser.add_argument(
									    '--model-name'   ,	type=__UpperCamelCase   ,	required=__UpperCamelCase   ,	help='The model name, including owner/organization, as seen on the hub.'   ,	)
									train_parser.add_argument(
									    '--local-dir'   ,	type=__UpperCamelCase   ,	default=''   ,	help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'   ,	)
									train_parser.add_argument(
									    '--max-error'   ,	type=__UpperCamelCase   ,	default=__UpperCamelCase   ,	help=(
									        F"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
									    )   ,	)
									train_parser.add_argument(
									    '--new-weights'   ,	action='store_true'   ,	help='Optional flag to create new TensorFlow weights, even if they already exist.'   ,	)
									train_parser.add_argument(
									    '--no-pr'   ,	action='store_true'   ,	help='Optional flag to NOT open a PR with converted weights.'		)
									train_parser.add_argument(
									    '--push'   ,	action='store_true'   ,	help='Optional flag to push the weights directly to `main` (requires permissions)'   ,	)
									train_parser.add_argument(
									    '--extra-commit-description'   ,	type=__UpperCamelCase   ,	default=''   ,	help='Optional additional commit description to use when opening a PR (e.g. to tag the owner).'   ,	)
									train_parser.add_argument(
									    '--override-model-class'   ,	type=__UpperCamelCase   ,	default=__UpperCamelCase   ,	help='If you think you know better than the auto-detector, you can specify the model class here. '
									    'Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'   ,	)
									train_parser.set_defaults(func=__UpperCamelCase		)




					@staticmethod
					def       __a      (       __UpperCamelCase   ,	__UpperCamelCase		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__ :     Optional[Any] 							=				set(pt_outputs.keys()		)
									snake_case__ :     int 							=				set(tf_outputs.keys()		)
									if pt_out_attrs != tf_out_attrs:
													raise ValueError(
													    F"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
													    F""" {tf_out_attrs})"""		)

									# 2. For each output attribute, computes the difference
									def _find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase=""		):
													# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
													# recursivelly, keeping the name of the attribute.
													if isinstance(__UpperCamelCase   ,	torch.Tensor		):
																	snake_case__ :     Any 							=				np.max(np.abs(pt_out.numpy() - tf_out.numpy()		)		)
																	snake_case__ :     Optional[Any] 							=				tensor_difference
													else:
																	snake_case__ :     Union[str, Any] 							=				attr_name
																	for i, pt_item in enumerate(__UpperCamelCase		):
																					# If it is a named attribute, we keep the name. Otherwise, just its index.
																					if isinstance(__UpperCamelCase   ,	__UpperCamelCase		):
																									snake_case__ :     List[Any] 							=				root_name + pt_item
																									snake_case__ :     Union[str, Any] 							=				tf_out[pt_item]
																									snake_case__ :     Union[str, Any] 							=				pt_out[pt_item]
																					else:
																									snake_case__ :     Optional[int] 							=				root_name + F"""[{i}]"""
																									snake_case__ :     int 							=				tf_out[i]
																					snake_case__ :     Any 							=				_find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)

													return differences

									return _find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase   ,	{}		)




					def __init__(       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	*__UpperCamelCase   ,	)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     Dict 							=				logging.get_logger('transformers-cli/pt_to_tf'		)
									snake_case__ :     Any 							=				model_name
									snake_case__ :     Tuple 							=				local_dir if local_dir else os.path.join('/tmp'   ,	__UpperCamelCase		)
									snake_case__ :     Any 							=				max_error
									snake_case__ :     Tuple 							=				new_weights
									snake_case__ :     int 							=				no_pr
									snake_case__ :     Tuple 							=				push
									snake_case__ :     List[Any] 							=				extra_commit_description
									snake_case__ :     Tuple 							=				override_model_class




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Optional[Any]:



									'''simple docstring'''







									def _get_audio_input():
													snake_case__ :     Optional[Any] 							=				load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,	'clean'   ,	split='validation'		)
													snake_case__ :     str 							=				ds.sort('id'		).select(range(2		)		)[:2]['audio']
													snake_case__ :     Union[str, Any] 							=				[x['array'] for x in speech_samples]
													return raw_samples

									snake_case__ :     Optional[Any] 							=				type(pt_model.config		)
									if model_config_class in PROCESSOR_MAPPING:
													snake_case__ :     Optional[int] 							=				AutoProcessor.from_pretrained(self._local_dir		)
													if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
																	snake_case__ :     Optional[Any] 							=				processor.tokenizer.eos_token
									elif model_config_class in IMAGE_PROCESSOR_MAPPING:
													snake_case__ :     Any 							=				AutoImageProcessor.from_pretrained(self._local_dir		)
									elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
													snake_case__ :     List[str] 							=				AutoFeatureExtractor.from_pretrained(self._local_dir		)
									elif model_config_class in TOKENIZER_MAPPING:
													snake_case__ :     Optional[Any] 							=				AutoTokenizer.from_pretrained(self._local_dir		)
													if processor.pad_token is None:
																	snake_case__ :     Optional[int] 							=				processor.eos_token
									else:
													raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})"""		)

									snake_case__ :     Dict 							=				set(inspect.signature(pt_model.forward		).parameters.keys()		)
									snake_case__ :     Tuple 							=				{}
									if "input_ids" in model_forward_signature:
													processor_inputs.update(
													    {
													        'text': ['Hi there!', 'I am a batch with more than one row and different input lengths.'],
													        'padding': True,
													        'truncation': True,
													    }		)
									if "pixel_values" in model_forward_signature:
													snake_case__ :     Dict 							=				load_dataset('cifar10'   ,	'plain_text'   ,	split='test'		)[:2]['img']
													processor_inputs.update({'images': sample_images}		)
									if "input_features" in model_forward_signature:
													snake_case__ :     List[str] 							=				inspect.signature(processor.feature_extractor		).parameters
													# Pad to the largest input length by default but take feature extractor default
													# padding value if it exists e.g. "max_length" and is not False or None
													if "padding" in feature_extractor_signature:
																	snake_case__ :     Union[str, Any] 							=				feature_extractor_signature['padding'].default
																	if default_strategy is not False and default_strategy is not None:
																					snake_case__ :     Any 							=				default_strategy
																	else:
																					snake_case__ :     int 							=				True
													else:
																	snake_case__ :     Union[str, Any] 							=				True
													processor_inputs.update({'audio': _get_audio_input(), 'padding': padding_strategy}		)
									if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
													processor_inputs.update({'audio': _get_audio_input(), 'padding': True}		)
									snake_case__ :     List[Any] 							=				processor(**__UpperCamelCase   ,	return_tensors='pt'		)
									snake_case__ :     Optional[int] 							=				processor(**__UpperCamelCase   ,	return_tensors='tf'		)

									# Extra input requirements, in addition to the input modality
									if (
									    config.is_encoder_decoder
									    or (hasattr(__UpperCamelCase   ,	'encoder'		) and hasattr(__UpperCamelCase   ,	'decoder'		))
									    or "decoder_input_ids" in tf_dummy_inputs
									):
													snake_case__ :     Tuple 							=				np.asarray([[1], [1]]   ,	dtype=__UpperCamelCase		) * (pt_model.config.decoder_start_token_id or 0)
													pt_input.update({'decoder_input_ids': torch.tensor(__UpperCamelCase		)}		)
													tf_input.update({'decoder_input_ids': tf.convert_to_tensor(__UpperCamelCase		)}		)

									return pt_input, tf_input




					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									if version.parse(huggingface_hub.__version__		) < version.parse('0.9.0'		):
													raise ImportError(
													    'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
													    ' installation.'		)
									else:
													from huggingface_hub import Repository, create_commit
													from huggingface_hub._commit_api import CommitOperationAdd

									# Fetch remote data
									snake_case__ :     Tuple 							=				Repository(local_dir=self._local_dir   ,	clone_from=self._model_name		)

									# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
									snake_case__ :     Optional[Any] 							=				AutoConfig.from_pretrained(self._local_dir		)
									snake_case__ :     Optional[Any] 							=				config.architectures
									if self._override_model_class is not None:
													if self._override_model_class.startswith('TF'		):
																	snake_case__ :     str 							=				[self._override_model_class[2:]]
													else:
																	snake_case__ :     Union[str, Any] 							=				[self._override_model_class]
													try:
																	snake_case__ :     List[str] 							=				getattr(import_module('transformers'		)   ,	architectures[0]		)
													except AttributeError:
																	raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""		)
													try:
																	snake_case__ :     int 							=				getattr(import_module('transformers'		)   ,	'TF' + architectures[0]		)
													except AttributeError:
																	raise ValueError(F"""TF model class TF{self._override_model_class} not found in transformers."""		)
									elif architectures is None:  # No architecture defined -- use auto classes
													snake_case__ :     List[str] 							=				getattr(import_module('transformers'		)   ,	'AutoModel'		)
													snake_case__ :     List[str] 							=				getattr(import_module('transformers'		)   ,	'TFAutoModel'		)
													self._logger.warning('No detected architecture, using AutoModel/TFAutoModel'		)
									else:  # Architecture defined -- use it
													if len(__UpperCamelCase		) > 1:
																	raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})"""		)
													self._logger.warning(F"""Detected architecture: {architectures[0]}"""		)
													snake_case__ :     Dict 							=				getattr(import_module('transformers'		)   ,	architectures[0]		)
													try:
																	snake_case__ :     Union[str, Any] 							=				getattr(import_module('transformers'		)   ,	'TF' + architectures[0]		)
													except AttributeError:
																	raise AttributeError(F"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""		)

        # Check the TF dummy inputs to see what keys we need in the forward pass
									snake_case__ :     Dict 							=				tf_class.from_config(__UpperCamelCase		)
									snake_case__ :     Any 							=				tf_from_pt_model.dummy_inputs

									del tf_from_pt_model  # Try to keep only one model in memory at a time

									# Load the model and get some basic inputs
									snake_case__ :     int 							=				pt_class.from_pretrained(self._local_dir		)
									pt_model.eval()

									snake_case__							,  snake_case__ :     List[str] 							=				self.get_inputs(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     str 							=				pt_model(**__UpperCamelCase   ,	output_hidden_states=__UpperCamelCase		)
									del pt_model  # will no longer be used, and may have a large memory footprint

									snake_case__ :     Dict 							=				tf_class.from_pretrained(self._local_dir   ,	from_pt=__UpperCamelCase		)
									snake_case__ :     Tuple 							=				tf_from_pt_model(**__UpperCamelCase   ,	output_hidden_states=__UpperCamelCase   ,	training=__UpperCamelCase		)

									# Confirms that cross loading PT weights into TF worked.
									snake_case__ :     int 							=				self.find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase		)
									snake_case__ :     Union[str, Any] 							=				{k: v for k, v in crossload_differences.items() if 'hidden' not in k}
									snake_case__ :     Dict 							=				{k: v for k, v in crossload_differences.items() if 'hidden' in k}
									if len(__UpperCamelCase		) == 0 and architectures is not None:
													raise ValueError(
													    F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
													    ' output was found. All outputs start with \'hidden\''		)
									snake_case__ :     Optional[int] 							=				max(output_differences.values()		) if output_differences else 0.0
									snake_case__ :     int 							=				max(hidden_differences.values()		)
									if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
													raise ValueError(
													    'The cross-loaded TensorFlow model has different outputs, something went wrong!\n'
													    + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]		)
													    + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]		)		)

									# Save the weights in a TF format (if needed) and confirms that the results are still good
									snake_case__ :     Any 							=				os.path.join(self._local_dir   ,	__UpperCamelCase		)
									snake_case__ :     Optional[Any] 							=				os.path.join(self._local_dir   ,	__UpperCamelCase		)
									if (not os.path.exists(__UpperCamelCase		) and not os.path.exists(__UpperCamelCase		)) or self._new_weights:
													tf_from_pt_model.save_pretrained(self._local_dir		)
									del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

									snake_case__ :     List[str] 							=				tf_class.from_pretrained(self._local_dir		)
									snake_case__ :     Union[str, Any] 							=				tf_model(**__UpperCamelCase   ,	output_hidden_states=__UpperCamelCase		)

									snake_case__ :     Optional[Any] 							=				self.find_pt_tf_differences(__UpperCamelCase   ,	__UpperCamelCase		)
									snake_case__ :     Any 							=				{k: v for k, v in conversion_differences.items() if 'hidden' not in k}
									snake_case__ :     Any 							=				{k: v for k, v in conversion_differences.items() if 'hidden' in k}
									if len(__UpperCamelCase		) == 0 and architectures is not None:
													raise ValueError(
													    F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
													    ' output was found. All outputs start with \'hidden\''		)
									snake_case__ :     int 							=				max(output_differences.values()		) if output_differences else 0.0
									snake_case__ :     Any 							=				max(hidden_differences.values()		)
									if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
													raise ValueError(
													    'The converted TensorFlow model has different outputs, something went wrong!\n'
													    + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]		)
													    + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]		)		)

									snake_case__ :     Any 							=				'Update TF weights' if self._new_weights else 'Add TF weights'
									if self._push:
													repo.git_add(auto_lfs_track=__UpperCamelCase		)
													repo.git_commit(__UpperCamelCase		)
													repo.git_push(blocking=__UpperCamelCase		)  # this prints a progress bar with the upload
													self._logger.warning(F"""TF weights pushed into {self._model_name}"""		)
									elif not self._no_pr:
													self._logger.warning('Uploading the weights into a new PR...'		)
													snake_case__ :     Optional[int] 							=				(
													    'Model converted by the [`transformers`\' `pt_to_tf`'
													    ' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '
													    'All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'
													    F"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
													    F"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
													    F"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
													    F"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
													)
													if self._max_error > MAX_ERROR:
																	commit_descrition += (
																	    F"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
																	)
													if self._extra_commit_description:
																	commit_descrition += "\n\n" + self._extra_commit_description

													# sharded model -> adds all related files (index and .h5 shards)
													if os.path.exists(__UpperCamelCase		):
																	snake_case__ :     str 							=				[
																	    CommitOperationAdd(path_in_repo=__UpperCamelCase   ,	path_or_fileobj=__UpperCamelCase		)
																	]
																	for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'		):
																					operations += [
																					    CommitOperationAdd(path_in_repo=os.path.basename(__UpperCamelCase		)   ,	path_or_fileobj=__UpperCamelCase		)
																					]
													else:
																	snake_case__ :     List[Any] 							=				[CommitOperationAdd(path_in_repo=__UpperCamelCase   ,	path_or_fileobj=__UpperCamelCase		)]

													snake_case__ :     Tuple 							=				create_commit(
													    repo_id=self._model_name   ,	operations=__UpperCamelCase   ,	commit_message=__UpperCamelCase   ,	commit_description=__UpperCamelCase   ,	repo_type='model'   ,	create_pr=__UpperCamelCase   ,	).pr_url
													self._logger.warning(F"""PR open in {hub_pr_url}"""		)



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,
)


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


lowerCAmelCase__					:       Union[str, Any]		=       namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def 			UpperCamelCase__						(    A__   )       -> Dict:
				snake_case__ :     List[str] 							=				BertTokenizer.from_pretrained('bert-base-uncased'      ,			do_lower_case=A__   )
				snake_case__ :     Optional[int] 							=				BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'   )
				model.to(args.device   )
				model.eval()

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

				if args.compute_rouge:
								snake_case__ :     Tuple 							=				[]
								snake_case__ :     Union[str, Any] 							=				[]

								import nltk
								import rouge

								nltk.download('punkt'   )
								snake_case__ :     Optional[Any] 							=				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.
				snake_case__ :     int 							=				''
				snake_case__ :     Any 							=				''

				snake_case__ :     Dict 							=				build_data_iterator(A__      ,			A__   )
				snake_case__ :     List[Any] 							=				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__   ):
								snake_case__ :     Dict 							=				predictor.translate_batch(A__   )
								snake_case__ :     List[str] 							=				predictor.from_batch(A__   )
								snake_case__ :     List[Any] 							=				[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:
								snake_case__ :     str 							=				rouge_evaluator.get_scores(A__      ,			A__   )
								snake_case__ :     Tuple 							=				format_rouge_scores(A__   )
								save_rouge_scores(A__   )
								print(A__   )
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__   )       -> Optional[int]:
				for summary, document_name in zip(A__      ,			A__   ):
								# Prepare the summary file's name
								if "." in document_name:
												snake_case__ :     Optional[int] 							=				'.'.join(document_name.split('.'   )[:-1]   )
												snake_case__ :     List[str] 							=				document_name.split('.'   )[-1]
												snake_case__ :     Optional[Any] 							=				bare_document_name + '_summary.' + extension
								else:
												snake_case__ :     Optional[Any] 							=				document_name + '_summary'

								snake_case__ :     Optional[int] 							=				os.path.join(A__      ,			A__   )
								with open(A__      ,			'w'   ) as output:
												output.write(A__   )
def 			UpperCamelCase__						(    A__   )       -> Dict:
				snake_case__							,  snake_case__							,  snake_case__ :     str 							=				translation
				snake_case__ :     str 							=				(
				    raw_summary.replace('[unused0]'      ,			''   )
				    .replace('[unused3]'      ,			''   )
				    .replace('[PAD]'      ,			''   )
				    .replace('[unused1]'      ,			''   )
				    .replace(r' +'      ,			' '   )
				    .replace(' [unused2] '      ,			'. '   )
				    .replace('[unused2]'      ,			''   )
				    .strip()
				)

				return summary
def 			UpperCamelCase__						(    A__   )       -> Dict:
				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__   )       -> str:
				with open('rouge_scores.txt'      ,			'w'   ) as output:
								output.write(A__   )
def 			UpperCamelCase__						(    A__      ,			A__   )       -> str:
				snake_case__ :     Optional[int] 							=				load_and_cache_examples(A__      ,			A__   )
				snake_case__ :     str 							=				SequentialSampler(A__   )

				def collate_fn(A__   ):
								return collate(A__      ,			A__      ,			block_size=512      ,			device=args.device   )

				snake_case__ :     List[str] 							=				DataLoader(
				    A__      ,			sampler=A__      ,			batch_size=args.batch_size      ,			collate_fn=A__      ,			)

				return iterator
def 			UpperCamelCase__						(    A__      ,			A__   )       -> Optional[int]:
				snake_case__ :     List[Any] 							=				CNNDMDataset(args.documents_dir   )
				return dataset
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__   )       -> List[Any]:
				snake_case__ :     List[str] 							=				[x for x in data if not len(x[1]   ) == 0]  # remove empty_files
				snake_case__ :     Tuple 							=				[name for name, _, _ in data]
				snake_case__ :     Any 							=				[' '.join(A__   ) for _, _, summary_list in data]

				snake_case__ :     int 							=				[encode_for_summarization(A__      ,			A__      ,			A__   ) for _, story, summary in data]
				snake_case__ :     List[Any] 							=				torch.tensor(
				    [truncate_or_pad(A__      ,			A__      ,			tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
				snake_case__ :     List[Any] 							=				compute_token_type_ids(A__      ,			tokenizer.cls_token_id   )
				snake_case__ :     Optional[Any] 							=				build_mask(A__      ,			tokenizer.pad_token_id   )

				snake_case__ :     Optional[Any] 							=				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__      ,			A__   )       -> List[str]:
				snake_case__ :     Optional[int] 							=				summary_tokens.to('cpu'   ).numpy()
				snake_case__ :     Dict 							=				tokenizer.decode(A__   )
				snake_case__ :     Tuple 							=				summary.split('.'   )
				snake_case__ :     int 							=				[s + '.' for s in sentences]
				return sentences
def 			UpperCamelCase__						(    )       -> Union[str, Any]:
				snake_case__ :     List[Any] 							=				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=200      ,			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=0.9_5      ,			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.'      ,			)
				snake_case__ :     int 							=				parser.parse_args()

				# Select device (distibuted not available)
				snake_case__ :     Dict 							=				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:
								snake_case__ :     List[str] 							=				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:
				if not os.path.exists(A__   ):
								return False

				snake_case__ :     List[str] 							=				os.listdir(A__   )
				if len(A__   ) == 0:
								return False

				return True


if __name__ == "__main__":
				main()


