import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor
def 			UpperCamelCase__						(    A__   )       -> Optional[int]:
				snake_case__ :     List[str] 							=				TimesformerConfig()

				if "large" in model_name:
								snake_case__ :     Dict 							=				96

				if "hr" in model_name:
								snake_case__ :     Optional[int] 							=				16
								snake_case__ :     Dict 							=				448

				snake_case__ :     Any 							=				'huggingface/label-files'
				if "k400" in model_name:
								snake_case__ :     int 							=				400
								snake_case__ :     Optional[int] 							=				'kinetics400-id2label.json'
				elif "k600" in model_name:
								snake_case__ :     Optional[Any] 							=				600
								snake_case__ :     str 							=				'kinetics600-id2label.json'
				elif "ssv2" in model_name:
								snake_case__ :     Tuple 							=				174
								snake_case__ :     Any 							=				'something-something-v2-id2label.json'
				else:
								raise ValueError('Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'   )
				snake_case__ :     int 							=				json.load(open(hf_hub_download(A__      ,			A__      ,			repo_type='dataset'   )      ,			'r'   )   )
				snake_case__ :     Tuple 							=				{int(A__   ): v for k, v in idalabel.items()}
				snake_case__ :     int 							=				idalabel
				snake_case__ :     Optional[int] 							=				{v: k for k, v in idalabel.items()}

				return config
def 			UpperCamelCase__						(    A__   )       -> List[Any]:
				if "encoder." in name:
								snake_case__ :     Optional[Any] 							=				name.replace('encoder.'      ,			''   )
				if "cls_token" in name:
								snake_case__ :     Dict 							=				name.replace('cls_token'      ,			'timesformer.embeddings.cls_token'   )
				if "pos_embed" in name:
								snake_case__ :     Optional[Any] 							=				name.replace('pos_embed'      ,			'timesformer.embeddings.position_embeddings'   )
				if "time_embed" in name:
								snake_case__ :     List[Any] 							=				name.replace('time_embed'      ,			'timesformer.embeddings.time_embeddings'   )
				if "patch_embed.proj" in name:
								snake_case__ :     Any 							=				name.replace('patch_embed.proj'      ,			'timesformer.embeddings.patch_embeddings.projection'   )
				if "patch_embed.norm" in name:
								snake_case__ :     int 							=				name.replace('patch_embed.norm'      ,			'timesformer.embeddings.norm'   )
				if "blocks" in name:
								snake_case__ :     List[Any] 							=				name.replace('blocks'      ,			'timesformer.encoder.layer'   )
				if "attn.proj" in name:
								snake_case__ :     List[Any] 							=				name.replace('attn.proj'      ,			'attention.output.dense'   )
				if "attn" in name and "bias" not in name and "temporal" not in name:
								snake_case__ :     Union[str, Any] 							=				name.replace('attn'      ,			'attention.self'   )
				if "attn" in name and "temporal" not in name:
								snake_case__ :     Dict 							=				name.replace('attn'      ,			'attention.attention'   )
				if "temporal_norm1" in name:
								snake_case__ :     Dict 							=				name.replace('temporal_norm1'      ,			'temporal_layernorm'   )
				if "temporal_attn.proj" in name:
								snake_case__ :     List[Any] 							=				name.replace('temporal_attn'      ,			'temporal_attention.output.dense'   )
				if "temporal_fc" in name:
								snake_case__ :     Optional[Any] 							=				name.replace('temporal_fc'      ,			'temporal_dense'   )
				if "norm1" in name and "temporal" not in name:
								snake_case__ :     List[Any] 							=				name.replace('norm1'      ,			'layernorm_before'   )
				if "norm2" in name:
								snake_case__ :     Dict 							=				name.replace('norm2'      ,			'layernorm_after'   )
				if "mlp.fc1" in name:
								snake_case__ :     str 							=				name.replace('mlp.fc1'      ,			'intermediate.dense'   )
				if "mlp.fc2" in name:
								snake_case__ :     List[Any] 							=				name.replace('mlp.fc2'      ,			'output.dense'   )
				if "norm.weight" in name and "fc" not in name and "temporal" not in name:
								snake_case__ :     List[str] 							=				name.replace('norm.weight'      ,			'timesformer.layernorm.weight'   )
				if "norm.bias" in name and "fc" not in name and "temporal" not in name:
								snake_case__ :     List[Any] 							=				name.replace('norm.bias'      ,			'timesformer.layernorm.bias'   )
				if "head" in name:
								snake_case__ :     int 							=				name.replace('head'      ,			'classifier'   )

				return name
def 			UpperCamelCase__						(    A__      ,			A__   )       -> List[Any]:
				for key in orig_state_dict.copy().keys():
								snake_case__ :     Union[str, Any] 							=				orig_state_dict.pop(A__   )

								if key.startswith('model.'   ):
												snake_case__ :     Any 							=				key.replace('model.'      ,			''   )

								if "qkv" in key:
												snake_case__ :     Optional[int] 							=				key.split('.'   )
												snake_case__ :     Optional[int] 							=				int(key_split[1]   )
												snake_case__ :     Optional[Any] 							=				'timesformer.encoder.layer.'
												if "temporal" in key:
																snake_case__ :     List[Any] 							=				'.temporal_attention.attention.qkv.'
												else:
																snake_case__ :     List[Any] 							=				'.attention.attention.qkv.'
												if "weight" in key:
																snake_case__ :     int 							=				val
												else:
																snake_case__ :     Union[str, Any] 							=				val
								else:
												snake_case__ :     Any 							=				val

				return orig_state_dict
def 			UpperCamelCase__						(    )       -> List[str]:
				snake_case__ :     Optional[Any] 							=				hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video'      ,			filename='eating_spaghetti.npy'      ,			repo_type='dataset'   )
				snake_case__ :     Dict 							=				np.load(A__   )
				return list(A__   )
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__   )       -> int:
				snake_case__ :     str 							=				get_timesformer_config(A__   )

				snake_case__ :     int 							=				TimesformerForVideoClassification(A__   )

				# download original checkpoint, hosted on Google Drive
				snake_case__ :     Dict 							=				'pytorch_model.bin'
				gdown.cached_download(A__      ,			A__      ,			quiet=A__   )
				snake_case__ :     Optional[Any] 							=				torch.load(A__      ,			map_location='cpu'   )
				if "model" in files:
								snake_case__ :     str 							=				files['model']
				elif "module" in files:
								snake_case__ :     Optional[Any] 							=				files['module']
				else:
								snake_case__ :     Optional[Any] 							=				files['model_state']
				snake_case__ :     List[str] 							=				convert_state_dict(A__      ,			A__   )

				model.load_state_dict(A__   )
				model.eval()

				# verify model on basic input
				snake_case__ :     List[str] 							=				VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]      ,			image_std=[0.5, 0.5, 0.5]   )
				snake_case__ :     int 							=				prepare_video()
				snake_case__ :     Optional[int] 							=				image_processor(video[:8]      ,			return_tensors='pt'   )

				snake_case__ :     Dict 							=				model(**A__   )
				snake_case__ :     Optional[Any] 							=				outputs.logits

				snake_case__ :     List[Any] 							=				[
				    # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
				    'timesformer-base-finetuned-k400',
				    'timesformer-large-finetuned-k400',
				    'timesformer-hr-finetuned-k400',
				    # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
				    'timesformer-base-finetuned-k600',
				    'timesformer-large-finetuned-k600',
				    'timesformer-hr-finetuned-k600',
				    # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
				    'timesformer-base-finetuned-ssv2',
				    'timesformer-large-finetuned-ssv2',
				    'timesformer-hr-finetuned-ssv2',
				]

				# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
				if model_name == "timesformer-base-finetuned-k400":
								snake_case__ :     int 							=				torch.Size([1, 400]   )
								snake_case__ :     List[str] 							=				torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]   )
				elif model_name == "timesformer-base-finetuned-k600":
								snake_case__ :     Tuple 							=				torch.Size([1, 600]   )
								snake_case__ :     List[str] 							=				torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]   )
				elif model_name == "timesformer-base-finetuned-ssv2":
								snake_case__ :     Tuple 							=				torch.Size([1, 174]   )
								snake_case__ :     Dict 							=				torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]   )
				elif model_name == "timesformer-large-finetuned-k400":
								snake_case__ :     int 							=				torch.Size([1, 400]   )
								snake_case__ :     List[Any] 							=				torch.tensor([0, 0, 0]   )
				elif model_name == "timesformer-large-finetuned-k600":
								snake_case__ :     str 							=				torch.Size([1, 600]   )
								snake_case__ :     Tuple 							=				torch.tensor([0, 0, 0]   )
				elif model_name == "timesformer-large-finetuned-ssv2":
								snake_case__ :     Tuple 							=				torch.Size([1, 174]   )
								snake_case__ :     int 							=				torch.tensor([0, 0, 0]   )
				elif model_name == "timesformer-hr-finetuned-k400":
								snake_case__ :     List[str] 							=				torch.Size([1, 400]   )
								snake_case__ :     Any 							=				torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]   )
				elif model_name == "timesformer-hr-finetuned-k600":
								snake_case__ :     Union[str, Any] 							=				torch.Size([1, 600]   )
								snake_case__ :     Optional[Any] 							=				torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]   )
				elif model_name == "timesformer-hr-finetuned-ssv2":
								snake_case__ :     List[Any] 							=				torch.Size([1, 174]   )
								snake_case__ :     str 							=				torch.tensor([-3.6_7_5_6, -0.7_5_1_3, 0.7_1_8_0]   )
				else:
								raise ValueError(F"""Model name not supported. Should be one of {model_names}"""   )

				# verify logits
				assert logits.shape == expected_shape
				assert torch.allclose(logits[0, :3]      ,			A__      ,			atol=1e-4   )
				print('Logits ok!'   )

				if pytorch_dump_folder_path is not None:
								print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""   )
								image_processor.save_pretrained(A__   )
								model.save_pretrained(A__   )

				if push_to_hub:
								print('Pushing to the hub...'   )
								model.push_to_hub(F"""fcakyon/{model_name}"""   )


if __name__ == "__main__":
				lowerCAmelCase__					:       Optional[Any]		=       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--checkpoint_url''',
				    default='''https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download''',
				    type=str,
				    help=(
				        '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
				        ''' download link.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''',
				    default='''''',
				    type=str,
				    help='''Path to the output PyTorch model directory.''',
				)
				parser.add_argument('''--model_name''', default='''timesformer-base-finetuned-k400''', type=str, help='''Name of the model.''')
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
				)

				lowerCAmelCase__					:       Tuple		=       parser.parse_args()
				convert_timesformer_checkpoint(
				    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
				)



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()


