

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_	     =     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


lowerCAmelCase_	     =     namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
      snake_case_       =     BertTokenizer.from_pretrained('''bert-base-uncased'''  ,		do_lower_case=_UpperCAmelCase	)
      snake_case_       =     BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''	)
      model.to(args.device	)
      model.eval()

      snake_case_       =     {
          "BOS": tokenizer.vocab["[unused0]"],
          "EOS": tokenizer.vocab["[unused1]"],
          "PAD": tokenizer.vocab["[PAD]"],
      }

      if args.compute_rouge:
            snake_case_       =     []
            snake_case_       =     []

            import nltk
            import rouge

            nltk.download('''punkt'''	)
            snake_case_       =     rouge.Rouge(
                metrics=['''rouge-n''', '''rouge-l''']  ,		max_n=2  ,		limit_length=_UpperCAmelCase  ,		length_limit=args.beam_size  ,		length_limit_type='''words'''  ,		apply_avg=_UpperCAmelCase  ,		apply_best=_UpperCAmelCase  ,		alpha=0.5  ,		weight_factor=1.2  ,		stemming=_UpperCAmelCase  ,		)

      # these (unused) arguments are defined to keep the compatibility
      # with the legacy code and will be deleted in a next iteration.
      snake_case_       =     ""
      snake_case_       =     ""

      snake_case_       =     build_data_iterator(_UpperCAmelCase  ,		_UpperCAmelCase	)
      snake_case_       =     build_predictor(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase	)

      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(_UpperCAmelCase	):
            snake_case_       =     predictor.translate_batch(_UpperCAmelCase	)
            snake_case_       =     predictor.from_batch(_UpperCAmelCase	)
            snake_case_       =     [format_summary(_UpperCAmelCase	) for t in translations]
            save_summaries(_UpperCAmelCase  ,		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_       =     rouge_evaluator.get_scores(_UpperCAmelCase  ,		_UpperCAmelCase	)
            snake_case_       =     format_rouge_scores(_UpperCAmelCase	)
            save_rouge_scores(_UpperCAmelCase	)
            print(_UpperCAmelCase	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):







      for summary, document_name in zip(_UpperCAmelCase  ,		_UpperCAmelCase	):
            # Prepare the summary file's name
            if "." in document_name:
                  snake_case_       =     ".".join(document_name.split('''.'''	)[:-1]	)
                  snake_case_       =     document_name.split('''.'''	)[-1]
                  snake_case_       =     bare_document_name + "_summary." + extension
            else:
                  snake_case_       =     document_name + "_summary"

            snake_case_       =     os.path.join(_UpperCAmelCase  ,		_UpperCAmelCase	)
            with open(_UpperCAmelCase  ,		'''w'''	) as output:
                  output.write(_UpperCAmelCase	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
      snake_case_       =     translation
      snake_case_       =     (
          raw_summary.replace('''[unused0]'''  ,		''''''	)
          .replace('''[unused3]'''  ,		''''''	)
          .replace('''[PAD]'''  ,		''''''	)
          .replace('''[unused1]'''  ,		''''''	)
          .replace(R''' +'''  ,		''' '''	)
          .replace(''' [unused2] '''  ,		'''. '''	)
          .replace('''[unused2]'''  ,		''''''	)
          .strip()
      )

      return summary


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
      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   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):







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


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
      snake_case_       =     load_and_cache_examples(_UpperCAmelCase  ,		_UpperCAmelCase	)
      snake_case_       =     SequentialSampler(_UpperCAmelCase	)

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

      snake_case_       =     DataLoader(
          _UpperCAmelCase  ,		sampler=_UpperCAmelCase  ,		batch_size=args.batch_size  ,		collate_fn=_UpperCAmelCase  ,		)

      return iterator


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
      snake_case_       =     CNNDMDataset(args.documents_dir	)
      return dataset


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
      snake_case_       =     [x for x in data if not len(x[1]	) == 0]  # remove empty_files
      snake_case_       =     [name for name, _, _ in data]
      snake_case_       =     [" ".join(_UpperCAmelCase	) for _, _, summary_list in data]

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

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

      return batch


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
      snake_case_       =     summary_tokens.to('''cpu'''	).numpy()
      snake_case_       =     tokenizer.decode(_UpperCAmelCase	)
      snake_case_       =     summary.split('''.'''	)
      snake_case_       =     [s + "." for s in sentences]
      return sentences


def   __SCREAMING_SNAKE_CASE ():
      snake_case_       =     argparse.ArgumentParser()
      parser.add_argument(
          '''--documents_dir'''  ,		default=_UpperCAmelCase  ,		type=_UpperCAmelCase  ,		required=_UpperCAmelCase  ,		help='''The folder where the documents to summarize are located.'''  ,		)
      parser.add_argument(
          '''--summaries_output_dir'''  ,		default=_UpperCAmelCase  ,		type=_UpperCAmelCase  ,		required=_UpperCAmelCase  ,		help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are'''  ,		)
      parser.add_argument(
          '''--compute_rouge'''  ,		default=_UpperCAmelCase  ,		type=_UpperCAmelCase  ,		required=_UpperCAmelCase  ,		help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'''  ,		)
      # EVALUATION options
      parser.add_argument(
          '''--no_cuda'''  ,		default=_UpperCAmelCase  ,		type=_UpperCAmelCase  ,		help='''Whether to force the execution on CPU.'''  ,		)
      parser.add_argument(
          '''--batch_size'''  ,		default=4  ,		type=_UpperCAmelCase  ,		help='''Batch size per GPU/CPU for training.'''  ,		)
      # BEAM SEARCH arguments
      parser.add_argument(
          '''--min_length'''  ,		default=50  ,		type=_UpperCAmelCase  ,		help='''Minimum number of tokens for the summaries.'''  ,		)
      parser.add_argument(
          '''--max_length'''  ,		default=200  ,		type=_UpperCAmelCase  ,		help='''Maixmum number of tokens for the summaries.'''  ,		)
      parser.add_argument(
          '''--beam_size'''  ,		default=5  ,		type=_UpperCAmelCase  ,		help='''The number of beams to start with for each example.'''  ,		)
      parser.add_argument(
          '''--alpha'''  ,		default=0.95  ,		type=_UpperCAmelCase  ,		help='''The value of alpha for the length penalty in the beam search.'''  ,		)
      parser.add_argument(
          '''--block_trigram'''  ,		default=_UpperCAmelCase  ,		type=_UpperCAmelCase  ,		help='''Whether to block the existence of repeating trigrams in the text generated by beam search.'''  ,		)
      snake_case_       =     parser.parse_args()

      # Select device (distibuted not available)
      snake_case_       =     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_       =     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=_UpperCAmelCase	)

      evaluate(_UpperCAmelCase	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):







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

      snake_case_       =     os.listdir(_UpperCAmelCase	)
      if len(_UpperCAmelCase	) == 0:
            return False

      return True


if __name__ == "__main__":
     main()


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   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     TimesformerConfig()

						if "large" in model_name:
												snake_case_       =     96

						if "hr" in model_name:
												snake_case_       =     16
												snake_case_       =     448

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

						return config


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						if "encoder." in name:
												snake_case_       =     name.replace('''encoder.'''  ,		''''''	)
						if "cls_token" in name:
												snake_case_       =     name.replace('''cls_token'''  ,		'''timesformer.embeddings.cls_token'''	)
						if "pos_embed" in name:
												snake_case_       =     name.replace('''pos_embed'''  ,		'''timesformer.embeddings.position_embeddings'''	)
						if "time_embed" in name:
												snake_case_       =     name.replace('''time_embed'''  ,		'''timesformer.embeddings.time_embeddings'''	)
						if "patch_embed.proj" in name:
												snake_case_       =     name.replace('''patch_embed.proj'''  ,		'''timesformer.embeddings.patch_embeddings.projection'''	)
						if "patch_embed.norm" in name:
												snake_case_       =     name.replace('''patch_embed.norm'''  ,		'''timesformer.embeddings.norm'''	)
						if "blocks" in name:
												snake_case_       =     name.replace('''blocks'''  ,		'''timesformer.encoder.layer'''	)
						if "attn.proj" in name:
												snake_case_       =     name.replace('''attn.proj'''  ,		'''attention.output.dense'''	)
						if "attn" in name and "bias" not in name and "temporal" not in name:
												snake_case_       =     name.replace('''attn'''  ,		'''attention.self'''	)
						if "attn" in name and "temporal" not in name:
												snake_case_       =     name.replace('''attn'''  ,		'''attention.attention'''	)
						if "temporal_norm1" in name:
												snake_case_       =     name.replace('''temporal_norm1'''  ,		'''temporal_layernorm'''	)
						if "temporal_attn.proj" in name:
												snake_case_       =     name.replace('''temporal_attn'''  ,		'''temporal_attention.output.dense'''	)
						if "temporal_fc" in name:
												snake_case_       =     name.replace('''temporal_fc'''  ,		'''temporal_dense'''	)
						if "norm1" in name and "temporal" not in name:
												snake_case_       =     name.replace('''norm1'''  ,		'''layernorm_before'''	)
						if "norm2" in name:
												snake_case_       =     name.replace('''norm2'''  ,		'''layernorm_after'''	)
						if "mlp.fc1" in name:
												snake_case_       =     name.replace('''mlp.fc1'''  ,		'''intermediate.dense'''	)
						if "mlp.fc2" in name:
												snake_case_       =     name.replace('''mlp.fc2'''  ,		'''output.dense'''	)
						if "norm.weight" in name and "fc" not in name and "temporal" not in name:
												snake_case_       =     name.replace('''norm.weight'''  ,		'''timesformer.layernorm.weight'''	)
						if "norm.bias" in name and "fc" not in name and "temporal" not in name:
												snake_case_       =     name.replace('''norm.bias'''  ,		'''timesformer.layernorm.bias'''	)
						if "head" in name:
												snake_case_       =     name.replace('''head'''  ,		'''classifier'''	)

						return name


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						for key in orig_state_dict.copy().keys():
												snake_case_       =     orig_state_dict.pop(SCREAMING_SNAKE_CASE__	)

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

												if "qkv" in key:
																		snake_case_       =     key.split('''.'''	)
																		snake_case_       =     int(key_split[1]	)
																		snake_case_       =     '''timesformer.encoder.layer.'''
																		if "temporal" in key:
																								snake_case_       =     '''.temporal_attention.attention.qkv.'''
																		else:
																								snake_case_       =     '''.attention.attention.qkv.'''
																		if "weight" in key:
																								snake_case_       =     val
																		else:
																								snake_case_       =     val
												else:
																		snake_case_       =     val

						return orig_state_dict


def   __SCREAMING_SNAKE_CASE ():
						snake_case_       =     hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video'''  ,		filename='''eating_spaghetti.npy'''  ,		repo_type='''dataset'''	)
						snake_case_       =     np.load(SCREAMING_SNAKE_CASE__	)
						return list(SCREAMING_SNAKE_CASE__	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     get_timesformer_config(SCREAMING_SNAKE_CASE__	)

						snake_case_       =     TimesformerForVideoClassification(SCREAMING_SNAKE_CASE__	)

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

						model.load_state_dict(SCREAMING_SNAKE_CASE__	)
						model.eval()

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

						snake_case_       =     model(**SCREAMING_SNAKE_CASE__	)
						snake_case_       =     outputs.logits

						snake_case_       =     [
						    # 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_       =     torch.Size([1, 400]	)
												snake_case_       =     torch.tensor([-0.3016, -0.7713, -0.4205]	)
						elif model_name == "timesformer-base-finetuned-k600":
												snake_case_       =     torch.Size([1, 600]	)
												snake_case_       =     torch.tensor([-0.7267, -0.7466, 3.2404]	)
						elif model_name == "timesformer-base-finetuned-ssv2":
												snake_case_       =     torch.Size([1, 174]	)
												snake_case_       =     torch.tensor([-0.9059, 0.6433, -3.1457]	)
						elif model_name == "timesformer-large-finetuned-k400":
												snake_case_       =     torch.Size([1, 400]	)
												snake_case_       =     torch.tensor([0, 0, 0]	)
						elif model_name == "timesformer-large-finetuned-k600":
												snake_case_       =     torch.Size([1, 600]	)
												snake_case_       =     torch.tensor([0, 0, 0]	)
						elif model_name == "timesformer-large-finetuned-ssv2":
												snake_case_       =     torch.Size([1, 174]	)
												snake_case_       =     torch.tensor([0, 0, 0]	)
						elif model_name == "timesformer-hr-finetuned-k400":
												snake_case_       =     torch.Size([1, 400]	)
												snake_case_       =     torch.tensor([-0.9617, -3.7311, -3.7708]	)
						elif model_name == "timesformer-hr-finetuned-k600":
												snake_case_       =     torch.Size([1, 600]	)
												snake_case_       =     torch.tensor([2.5273, 0.7127, 1.8848]	)
						elif model_name == "timesformer-hr-finetuned-ssv2":
												snake_case_       =     torch.Size([1, 174]	)
												snake_case_       =     torch.tensor([-3.6756, -0.7513, 0.7180]	)
						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]  ,		SCREAMING_SNAKE_CASE__  ,		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(SCREAMING_SNAKE_CASE__	)
												model.save_pretrained(SCREAMING_SNAKE_CASE__	)

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


if __name__ == "__main__":
					lowerCAmelCase_	     =     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_	     =     parser.parse_args()
					convert_timesformer_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
					)