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__ :     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()



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase__					:       Any		=       logging.get_logger(__name__)

lowerCAmelCase__					:       int		=       {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__      ,			A__   )       -> Optional[int]:
				for attribute in key.split('.'   ):
								snake_case__ :     Optional[Any] 							=				getattr(A__      ,			A__   )

				if weight_type is not None:
								snake_case__ :     Any 							=				getattr(A__      ,			A__   ).shape
				else:
								snake_case__ :     str 							=				hf_pointer.shape

				assert hf_shape == value.shape, (
				    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
				    F""" {value.shape} for {full_name}"""
				)

				if weight_type == "weight":
								snake_case__ :     List[str] 							=				value
				elif weight_type == "weight_g":
								snake_case__ :     str 							=				value
				elif weight_type == "weight_v":
								snake_case__ :     List[str] 							=				value
				elif weight_type == "bias":
								snake_case__ :     List[Any] 							=				value
				else:
								snake_case__ :     Union[str, Any] 							=				value

				logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )
def 			UpperCamelCase__						(    A__      ,			A__   )       -> List[str]:
				snake_case__ :     Tuple 							=				[]
				snake_case__ :     int 							=				fairseq_model.state_dict()

				snake_case__ :     int 							=				hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								snake_case__ :     Union[str, Any] 							=				False
								if "conv_layers" in name:
												load_conv_layer(
												    A__      ,			A__      ,			A__      ,			A__      ,			hf_model.config.feat_extract_norm == 'group'      ,			)
												snake_case__ :     Tuple 							=				True
								else:
												for key, mapped_key in MAPPING.items():
																snake_case__ :     Dict 							=				mapped_key

																if key in name:
																				snake_case__ :     int 							=				True
																				if "*" in mapped_key:
																								snake_case__ :     List[str] 							=				name.split(A__   )[0].split('.'   )[-2]
																								snake_case__ :     Dict 							=				mapped_key.replace('*'      ,			A__   )
																				if "weight_g" in name:
																								snake_case__ :     int 							=				'weight_g'
																				elif "weight_v" in name:
																								snake_case__ :     int 							=				'weight_v'
																				elif "weight" in name:
																								snake_case__ :     Any 							=				'weight'
																				elif "bias" in name:
																								snake_case__ :     Tuple 							=				'bias'
																				else:
																								snake_case__ :     int 							=				None
																				set_recursively(A__      ,			A__      ,			A__      ,			A__      ,			A__   )
																continue
								if not is_used:
												unused_weights.append(A__   )

				logger.warning(F"""Unused weights: {unused_weights}"""   )
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__      ,			A__   )       -> Tuple:
				snake_case__ :     Optional[int] 							=				full_name.split('conv_layers.'   )[-1]
				snake_case__ :     Dict 							=				name.split('.'   )
				snake_case__ :     str 							=				int(items[0]   )
				snake_case__ :     Optional[Any] 							=				int(items[1]   )

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
												)
												snake_case__ :     List[Any] 							=				value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												snake_case__ :     Optional[int] 							=				value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
												    " found."
												)
												snake_case__ :     Optional[int] 							=				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
												)
												snake_case__ :     Union[str, Any] 							=				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
				else:
								unused_weights.append(A__   )
def 			UpperCamelCase__						(    A__   )       -> Union[str, Any]:
				snake_case__ :     Optional[Any] 							=				HubertConfig()
				snake_case__ :     Dict 							=				model.config

				snake_case__ :     Optional[Any] 							=				fs_config.activation_dropout
				snake_case__ :     Union[str, Any] 							=				False
				snake_case__ :     Tuple 							=				fs_config.attention_dropout
				snake_case__ :     List[str] 							=				False
				snake_case__ :     Optional[int] 							=				eval(fs_config.extractor_conv_feature_layers   )
				snake_case__ :     Optional[int] 							=				[x[0] for x in conv_layers]
				snake_case__ :     Union[str, Any] 							=				[x[1] for x in conv_layers]
				snake_case__ :     Dict 							=				[x[2] for x in conv_layers]
				snake_case__ :     int 							=				'gelu'
				snake_case__ :     Dict 							=				'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
				snake_case__ :     Any 							=				False
				snake_case__ :     List[Any] 							=				0.0
				snake_case__ :     Any 							=				0.0
				snake_case__ :     Union[str, Any] 							=				fs_config.activation_fn
				snake_case__ :     List[str] 							=				fs_config.dropout
				snake_case__ :     Dict 							=				fs_config.encoder_embed_dim
				snake_case__ :     Any 							=				0.0_2
				snake_case__ :     Union[str, Any] 							=				fs_config.encoder_ffn_embed_dim
				snake_case__ :     List[Any] 							=				1e-5
				snake_case__ :     Union[str, Any] 							=				0.0
				snake_case__ :     int 							=				fs_config.encoder_attention_heads
				snake_case__ :     Any 							=				fs_config.conv_pos_groups
				snake_case__ :     int 							=				fs_config.conv_pos
				snake_case__ :     Any 							=				len(A__   )
				snake_case__ :     str 							=				fs_config.encoder_layers

				return config
@torch.no_grad()
def 			UpperCamelCase__						(    A__      ,			A__=None   )       -> str:
				snake_case__ :     int 							=				distilhubert().model.model

				if config_path is not None:
								snake_case__ :     Optional[int] 							=				HubertConfig.from_pretrained(A__   )
				else:
								snake_case__ :     int 							=				convert_config(A__   )
				snake_case__ :     Dict 							=				model.eval()

				snake_case__ :     int 							=				WavaVecaFeatureExtractor(
				    feature_size=1      ,			sampling_rate=1_6000      ,			padding_value=0      ,			do_normalize=A__      ,			return_attention_mask=A__      ,			)
				snake_case__ :     Optional[int] 							=				HubertModel(A__   )

				recursively_load_weights(A__      ,			A__   )

				feature_extractor.save_pretrained(A__   )
				hf_model.save_pretrained(A__   )


if __name__ == "__main__":
				lowerCAmelCase__					:       Union[str, Any]		=       argparse.ArgumentParser()
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
				lowerCAmelCase__					:       List[Any]		=       parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


