

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 _a			(	SCREAMING_SNAKE_CASE		:      Tuple		)   ->   List[str]:

 """simple docstring"""






 __lowerCAmelCase:       List[str]            =				TimesformerConfig()

 if "large" in model_name:
  __lowerCAmelCase:       int            =				96

 if "hr" in model_name:
  __lowerCAmelCase:       Dict            =				16
  __lowerCAmelCase:       str            =				4_48

 __lowerCAmelCase:       List[Any]            =				'huggingface/label-files'
 if "k400" in model_name:
  __lowerCAmelCase:       str            =				4_00
  __lowerCAmelCase:       List[Any]            =				'kinetics400-id2label.json'
 elif "k600" in model_name:
  __lowerCAmelCase:       Optional[Any]            =				6_00
  __lowerCAmelCase:       Optional[int]            =				'kinetics600-id2label.json'
 elif "ssv2" in model_name:
  __lowerCAmelCase:       Optional[int]            =				1_74
  __lowerCAmelCase:       Optional[int]            =				'something-something-v2-id2label.json'
 else:
  raise ValueError('Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'		)
 __lowerCAmelCase:       str            =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , repo_type='dataset'		)   , 'r'		)		)
 __lowerCAmelCase:       List[Any]            =				{int(SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}
 __lowerCAmelCase:       Union[str, Any]            =				idalabel
 __lowerCAmelCase:       Union[str, Any]            =				{v: k for k, v in idalabel.items()}

 return config







def _a			(	SCREAMING_SNAKE_CASE		:      List[str]		)   ->   Optional[int]:

 """simple docstring"""






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

 return name







def _a			(	SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   Dict:

 """simple docstring"""






 for key in orig_state_dict.copy().keys():
  __lowerCAmelCase:       int            =				orig_state_dict.pop(SCREAMING_SNAKE_CASE		)

  if key.startswith('model.'		):
   __lowerCAmelCase:       Optional[Any]            =				key.replace('model.'   , ''		)

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

 return orig_state_dict







def _a			(	)   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				hf_hub_download(
     repo_id='hf-internal-testing/spaghetti-video'   , filename='eating_spaghetti.npy'   , repo_type='dataset'		)
 __lowerCAmelCase:       Optional[int]            =				np.load(SCREAMING_SNAKE_CASE		)
 return list(SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      Dict   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      Union[str, Any]		)   ->   Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:       Dict            =				get_timesformer_config(SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       int            =				TimesformerForVideoClassification(SCREAMING_SNAKE_CASE		)

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

 model.load_state_dict(SCREAMING_SNAKE_CASE		)
 model.eval()

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

 __lowerCAmelCase:       int            =				model(**SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Union[str, Any]            =				outputs.logits

 __lowerCAmelCase:       Dict            =				[
     # 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":
  __lowerCAmelCase:       Union[str, Any]            =				torch.Size([1, 4_00]		)
  __lowerCAmelCase:       Union[str, Any]            =				torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]		)
 elif model_name == "timesformer-base-finetuned-k600":
  __lowerCAmelCase:       Optional[Any]            =				torch.Size([1, 6_00]		)
  __lowerCAmelCase:       Dict            =				torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]		)
 elif model_name == "timesformer-base-finetuned-ssv2":
  __lowerCAmelCase:       Optional[Any]            =				torch.Size([1, 1_74]		)
  __lowerCAmelCase:       Optional[int]            =				torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]		)
 elif model_name == "timesformer-large-finetuned-k400":
  __lowerCAmelCase:       Union[str, Any]            =				torch.Size([1, 4_00]		)
  __lowerCAmelCase:       Dict            =				torch.tensor([0, 0, 0]		)
 elif model_name == "timesformer-large-finetuned-k600":
  __lowerCAmelCase:       str            =				torch.Size([1, 6_00]		)
  __lowerCAmelCase:       Optional[int]            =				torch.tensor([0, 0, 0]		)
 elif model_name == "timesformer-large-finetuned-ssv2":
  __lowerCAmelCase:       str            =				torch.Size([1, 1_74]		)
  __lowerCAmelCase:       List[str]            =				torch.tensor([0, 0, 0]		)
 elif model_name == "timesformer-hr-finetuned-k400":
  __lowerCAmelCase:       List[Any]            =				torch.Size([1, 4_00]		)
  __lowerCAmelCase:       Dict            =				torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]		)
 elif model_name == "timesformer-hr-finetuned-k600":
  __lowerCAmelCase:       Optional[Any]            =				torch.Size([1, 6_00]		)
  __lowerCAmelCase:       List[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":
  __lowerCAmelCase:       Optional[Any]            =				torch.Size([1, 1_74]		)
  __lowerCAmelCase:       Any            =				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]   , 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__":
  _a			=       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.'''
  )

  _a			=       parser.parse_args()
  convert_timesformer_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
  )








import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_a			=       getLogger(__name__)


_a			=       '''cuda''' if torch.cuda.is_available() else '''cpu'''







def _a			(	SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      int = 8   , SCREAMING_SNAKE_CASE		:      str = DEFAULT_DEVICE   , SCREAMING_SNAKE_CASE		:      Union[str, Any]=False   , SCREAMING_SNAKE_CASE		:      Tuple="summarization"   , SCREAMING_SNAKE_CASE		:      int=None   , **SCREAMING_SNAKE_CASE		:      Optional[Any]   , )   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				Path(SCREAMING_SNAKE_CASE		).open('w'   , encoding='utf-8'		)
 __lowerCAmelCase:       Tuple            =				str(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE		).to(SCREAMING_SNAKE_CASE		)
 if fpaa:
  __lowerCAmelCase:       List[Any]            =				model.half()

 __lowerCAmelCase:       Dict            =				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE		)
 logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''		)  # if this is wrong, check config.model_type.

 __lowerCAmelCase:       Any            =				time.time()
 # update config with task specific params
 use_task_specific_params(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 if prefix is None:
  __lowerCAmelCase:       Optional[Any]            =				prefix or getattr(model.config   , 'prefix'   , ''		) or ''
 for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)		)		):
  __lowerCAmelCase:       str            =				[prefix + text for text in examples_chunk]
  __lowerCAmelCase:       Optional[Any]            =				tokenizer(SCREAMING_SNAKE_CASE   , return_tensors='pt'   , truncation=SCREAMING_SNAKE_CASE   , padding='longest'		).to(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       List[str]            =				model.generate(
      input_ids=batch.input_ids   , attention_mask=batch.attention_mask   , **SCREAMING_SNAKE_CASE   , )
  __lowerCAmelCase:       Dict            =				tokenizer.batch_decode(SCREAMING_SNAKE_CASE   , skip_special_tokens=SCREAMING_SNAKE_CASE   , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE		)
  for hypothesis in dec:
   fout.write(hypothesis + '\n'		)
   fout.flush()
 fout.close()
 __lowerCAmelCase:       str            =				int(time.time() - start_time		)  # seconds
 __lowerCAmelCase:       Tuple            =				len(SCREAMING_SNAKE_CASE		)
 return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   , 4		)}







def _a			(	)   ->   Dict:

 """simple docstring"""






 return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'		)







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple=True		)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       str            =				argparse.ArgumentParser()
 parser.add_argument('model_name'   , type=SCREAMING_SNAKE_CASE   , help='like facebook/bart-large-cnn,t5-base, etc.'		)
 parser.add_argument('input_path'   , type=SCREAMING_SNAKE_CASE   , help='like cnn_dm/test.source'		)
 parser.add_argument('save_path'   , type=SCREAMING_SNAKE_CASE   , help='where to save summaries'		)
 parser.add_argument('--reference_path'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , help='like cnn_dm/test.target'		)
 parser.add_argument('--score_path'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default='metrics.json'   , help='where to save metrics'		)
 parser.add_argument('--device'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default=SCREAMING_SNAKE_CASE   , help='cuda, cuda:1, cpu etc.'		)
 parser.add_argument(
     '--prefix'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default=SCREAMING_SNAKE_CASE   , help='will be added to the begininng of src examples'		)
 parser.add_argument('--task'   , type=SCREAMING_SNAKE_CASE   , default='summarization'   , help='used for task_specific_params + metrics'		)
 parser.add_argument('--bs'   , type=SCREAMING_SNAKE_CASE   , default=8   , required=SCREAMING_SNAKE_CASE   , help='batch size'		)
 parser.add_argument(
     '--n_obs'   , type=SCREAMING_SNAKE_CASE   , default=-1   , required=SCREAMING_SNAKE_CASE   , help='How many observations. Defaults to all.'		)
 parser.add_argument('--fp16'   , action='store_true'		)
 parser.add_argument('--dump-args'   , action='store_true'   , help='print the custom hparams with the results'		)
 parser.add_argument(
     '--info'   , nargs='?'   , type=SCREAMING_SNAKE_CASE   , const=datetime_now()   , help=(
         'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
         ' lang=en-ru. If no value is passed, the current datetime string will be used.'
     )   , )
 # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
 __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				parser.parse_known_args()
 __lowerCAmelCase:       Union[str, Any]            =				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE		)
 if parsed_args and verbose:
  print(f'''parsed the following generate kwargs: {parsed_args}'''		)
 with open(args.input_path		) as f:
  __lowerCAmelCase:       Optional[int]            =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
 if args.n_obs > 0:
  __lowerCAmelCase:       Tuple            =				examples[: args.n_obs]
 Path(args.save_path		).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE		)
 if args.reference_path is None and Path(args.score_path		).exists():
  warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''		)
 __lowerCAmelCase:       Tuple            =				generate_summaries_or_translations(
     SCREAMING_SNAKE_CASE   , args.save_path   , args.model_name   , batch_size=args.bs   , device=args.device   , fpaa=args.fpaa   , task=args.task   , prefix=args.prefix   , **SCREAMING_SNAKE_CASE   , )

 if args.reference_path is None:
  return {}

 # Compute scores
 __lowerCAmelCase:       Dict            =				calculate_bleu if 'translation' in args.task else calculate_rouge
 __lowerCAmelCase:       Optional[Any]            =				[x.rstrip() for x in open(args.save_path		).readlines()]
 __lowerCAmelCase:       Union[str, Any]            =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(SCREAMING_SNAKE_CASE		)]
 __lowerCAmelCase:       dict            =				score_fn(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 scores.update(SCREAMING_SNAKE_CASE		)

 if args.dump_args:
  scores.update(SCREAMING_SNAKE_CASE		)
 if args.info:
  __lowerCAmelCase:       Any            =				args.info

 if verbose:
  print(SCREAMING_SNAKE_CASE		)

 if args.score_path is not None:
  json.dump(SCREAMING_SNAKE_CASE   , open(args.score_path   , 'w'		)		)

 return scores


if __name__ == "__main__":
  # Usage for MT:
  # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
  run_generate(verbose=True)





