


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      (    __UpperCamelCase			):
     a_     :  Tuple				      =     TimesformerConfig()

     if "large" in model_name:
          a_     :  Optional[int]				      =     9_6

     if "hr" in model_name:
          a_     :  Tuple				      =     1_6
          a_     :  List[str]				      =     4_4_8

     a_     :  Tuple				      =     """huggingface/label-files"""
     if "k400" in model_name:
          a_     :  str				      =     4_0_0
          a_     :  List[Any]				      =     """kinetics400-id2label.json"""
     elif "k600" in model_name:
          a_     :  Any				      =     6_0_0
          a_     :  Any				      =     """kinetics600-id2label.json"""
     elif "ssv2" in model_name:
          a_     :  Union[str, Any]				      =     1_7_4
          a_     :  Optional[int]				      =     """something-something-v2-id2label.json"""
     else:
          raise ValueError("""Model name should either contain 'k400', 'k600' or 'ssv2'."""			)
     a_     :  str				      =     json.load(open(hf_hub_download(__UpperCamelCase	,	__UpperCamelCase	,	repo_type="""dataset"""			)	,	"""r"""			)			)
     a_     :  List[str]				      =     {int(__UpperCamelCase			): v for k, v in idalabel.items()}
     a_     :  Dict				      =     idalabel
     a_     :  Tuple				      =     {v: k for k, v in idalabel.items()}

     return config



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

     return name



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     for key in orig_state_dict.copy().keys():
          a_     :  List[Any]				      =     orig_state_dict.pop(__UpperCamelCase			)

          if key.startswith("""model."""			):
               a_     :  Dict				      =     key.replace("""model."""	,	""""""			)

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

     return orig_state_dict



def 			_a      (    ):
     a_     :  int				      =     hf_hub_download(
         repo_id="""hf-internal-testing/spaghetti-video"""	,	filename="""eating_spaghetti.npy"""	,	repo_type="""dataset"""			)
     a_     :  List[str]				      =     np.load(__UpperCamelCase			)
     return list(__UpperCamelCase			)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  List[Any]				      =     get_timesformer_config(__UpperCamelCase			)

     a_     :  Tuple				      =     TimesformerForVideoClassification(__UpperCamelCase			)

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

     model.load_state_dict(__UpperCamelCase			)
     model.eval()

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

     a_     :  Optional[Any]				      =     model(**__UpperCamelCase			)
     a_     :  Optional[Any]				      =     outputs.logits

     a_     :  Optional[int]				      =     [
         # 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":
          a_     :  List[Any]				      =     torch.Size([1, 4_0_0]			)
          a_     :  Optional[int]				      =     torch.tensor([-0.30_16, -0.77_13, -0.42_05]			)
     elif model_name == "timesformer-base-finetuned-k600":
          a_     :  Optional[Any]				      =     torch.Size([1, 6_0_0]			)
          a_     :  Dict				      =     torch.tensor([-0.72_67, -0.74_66, 3.24_04]			)
     elif model_name == "timesformer-base-finetuned-ssv2":
          a_     :  Optional[int]				      =     torch.Size([1, 1_7_4]			)
          a_     :  Optional[int]				      =     torch.tensor([-0.90_59, 0.64_33, -3.14_57]			)
     elif model_name == "timesformer-large-finetuned-k400":
          a_     :  Optional[int]				      =     torch.Size([1, 4_0_0]			)
          a_     :  Union[str, Any]				      =     torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-k600":
          a_     :  Any				      =     torch.Size([1, 6_0_0]			)
          a_     :  Optional[Any]				      =     torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-ssv2":
          a_     :  int				      =     torch.Size([1, 1_7_4]			)
          a_     :  Tuple				      =     torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-hr-finetuned-k400":
          a_     :  List[str]				      =     torch.Size([1, 4_0_0]			)
          a_     :  List[Any]				      =     torch.tensor([-0.96_17, -3.73_11, -3.77_08]			)
     elif model_name == "timesformer-hr-finetuned-k600":
          a_     :  Tuple				      =     torch.Size([1, 6_0_0]			)
          a_     :  Union[str, Any]				      =     torch.tensor([2.52_73, 0.71_27, 1.88_48]			)
     elif model_name == "timesformer-hr-finetuned-ssv2":
          a_     :  List[str]				      =     torch.Size([1, 1_7_4]			)
          a_     :  Tuple				      =     torch.tensor([-3.67_56, -0.75_13, 0.71_80]			)
     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]	,	__UpperCamelCase	,	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(__UpperCamelCase			)
          model.save_pretrained(__UpperCamelCase			)

     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
       )






import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
__UpperCAmelCase     					=  {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Dict	   =     search.split()
     _lowerCAmelCase :       int	   =     dict((g.split("="					) for g in groups)					)
     _lowerCAmelCase :       Dict	   =     list(entries.keys()					)
     _lowerCAmelCase :       Tuple	   =     [[f'''--{k} {v}''' for v in vs.split(":"					)] for k, vs in entries.items()]
     _lowerCAmelCase :       Dict	   =     [list(SCREAMING_SNAKE_CASE					) for x in itertools.product(*SCREAMING_SNAKE_CASE					)]
     return matrix, entry_names







def 						_snake_case		(     )							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     sys.argv[0]

     _lowerCAmelCase :       Any	   =     argparse.ArgumentParser(
         usage=(
             "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
             " refer to `run_eval.py -h` for the complete list."
         )					)
     parser.add_argument(
         "--search"		,				type=SCREAMING_SNAKE_CASE		,				required=SCREAMING_SNAKE_CASE		,				help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""		,				)
     parser.add_argument(
         "--bs"		,				type=SCREAMING_SNAKE_CASE		,				default=8		,				required=SCREAMING_SNAKE_CASE		,				help="initial batch size (may get reduced if it's too big)"					)
     parser.add_argument("--task"		,				type=SCREAMING_SNAKE_CASE		,				help="used for task_specific_params + metrics"					)
     parser.add_argument(
         "--info"		,				nargs="?"		,				type=SCREAMING_SNAKE_CASE		,				const=datetime_now()		,				help=(
             "add custom notes to be printed before the results table. If no value is passed, the current datetime"
             " string will be used."
         )		,				)
     _lowerCAmelCase   ,      _lowerCAmelCase :       List[str]	   =     parser.parse_known_args()
     # we share some of the args
     args_main.extend(["--task", args.task]					)
     _lowerCAmelCase :       str	   =     [prog] + args_main

     # to support variations like translation_en_to_de"
     _lowerCAmelCase :       Any	   =     "translation" if "translation" in args.task else "summarization"

     _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     parse_search_arg(args.search					)
     _lowerCAmelCase :       Optional[Any]	   =     task_score_names[task]  # score cols first
     _lowerCAmelCase :       int	   =     {col: len(str(SCREAMING_SNAKE_CASE					)					) for col in col_names}
     _lowerCAmelCase :       Optional[int]	   =     []
     for r in matrix:
          _lowerCAmelCase :       List[str]	   =     dict((x.replace("--"		,				""					).split() for x in r)					)
          _lowerCAmelCase :       Any	   =     " ".join(SCREAMING_SNAKE_CASE					).split()
          args_exp.extend(["--bs", str(args.bs					)]					)  # in case we need to reduce its size due to CUDA OOM
          _lowerCAmelCase :       List[str]	   =     args_normal + args_exp

          # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

          _lowerCAmelCase :       Any	   =     run_generate(verbose=SCREAMING_SNAKE_CASE					)
          # make sure scores are first in the table
          _lowerCAmelCase :       Any	   =     OrderedDict()
          for score in task_score_names[task]:
               _lowerCAmelCase :       Dict	   =     scores[score]
          result.update(SCREAMING_SNAKE_CASE					)
          results.append(SCREAMING_SNAKE_CASE					)

          # find widest entries
          for k, v in result.items():
               _lowerCAmelCase :       Any	   =     len(str(SCREAMING_SNAKE_CASE					)					)
               if l > col_widths[k]:
                    _lowerCAmelCase :       str	   =     l

     _lowerCAmelCase :       Optional[Any]	   =     sorted(SCREAMING_SNAKE_CASE		,				key=operator.itemgetter(*task_score_names[task]					)		,				reverse=SCREAMING_SNAKE_CASE					)
     print(" | ".join([f'''{col:{col_widths[col]}}''' for col in col_names]					)					)
     print(" | ".join([f'''{'-'*col_widths[col]}''' for col in col_names]					)					)
     for row in results_sorted:
          print(" | ".join([f'''{row[col]:{col_widths[col]}}''' for col in col_names]					)					)

     _lowerCAmelCase :       Optional[int]	   =     results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     _lowerCAmelCase :       Tuple	   =     [f'''--{k} {v}''' for k, v in best.items()]
     _lowerCAmelCase :       Union[str, Any]	   =     ["--bs", str(args.bs					)]
     if args.info:
          print(f'''\nInfo: {args.info}'''					)
     print("\nBest score args:"					)
     print(" ".join(args_main + best_args + dyn_args					)					)

     return results_sorted


if __name__ == "__main__":
  # Usage:
  # [normal-run_eval_search.py cmd plus] \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  #
  # Example:
  # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
  # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
  # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  run_search()






