from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					:       List[str]		=       {
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       List[str]		=       ['''GPT2TokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Union[str, Any]		=       [
				    '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''GPT2DoubleHeadsModel''',
				    '''GPT2ForQuestionAnswering''',
				    '''GPT2ForSequenceClassification''',
				    '''GPT2ForTokenClassification''',
				    '''GPT2LMHeadModel''',
				    '''GPT2Model''',
				    '''GPT2PreTrainedModel''',
				    '''load_tf_weights_in_gpt2''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Optional[int]		=       [
				    '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFGPT2DoubleHeadsModel''',
				    '''TFGPT2ForSequenceClassification''',
				    '''TFGPT2LMHeadModel''',
				    '''TFGPT2MainLayer''',
				    '''TFGPT2Model''',
				    '''TFGPT2PreTrainedModel''',
				]

try:
				if not is_keras_nlp_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       int		=       ['''TFGPT2Tokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Optional[int]		=       ['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
				from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
				from .tokenization_gpta import GPTaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_gpta_fast import GPTaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_gpta import (
								    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GPTaDoubleHeadsModel,
								    GPTaForQuestionAnswering,
								    GPTaForSequenceClassification,
								    GPTaForTokenClassification,
								    GPTaLMHeadModel,
								    GPTaModel,
								    GPTaPreTrainedModel,
								    load_tf_weights_in_gpta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_gpta import (
								    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFGPTaDoubleHeadsModel,
								    TFGPTaForSequenceClassification,
								    TFGPTaLMHeadModel,
								    TFGPTaMainLayer,
								    TFGPTaModel,
								    TFGPTaPreTrainedModel,
								)

				try:
								if not is_keras_nlp_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_gpta_tf import TFGPTaTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
				import sys

				lowerCAmelCase__					:       Optional[Any]		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



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()


