
'''simple docstring'''
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    :	str			)					->							Union[str, Any]:





  """simple docstring"""

  __A		:  Optional[int]       				=		TimesformerConfig()

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

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

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

  return config







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]			)					->							Any:





  """simple docstring"""

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

  return name







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							List[Any]:





  """simple docstring"""

  for key in orig_state_dict.copy().keys():
    __A		:  str       				=		orig_state_dict.pop(__SCREAMING_SNAKE_CASE			)

    if key.startswith("""model."""			):
      __A		:  Optional[Any]       				=		key.replace("""model."""      ,  """"""			)

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

  return orig_state_dict







def 							A_  (    )					->							int:





  """simple docstring"""

  __A		:  List[Any]       				=		hf_hub_download(
      repo_id="""hf-internal-testing/spaghetti-video"""      ,  filename="""eating_spaghetti.npy"""      ,  repo_type="""dataset"""			)
  __A		:  List[Any]       				=		np.load(__SCREAMING_SNAKE_CASE			)
  return list(__SCREAMING_SNAKE_CASE			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Optional[Any]:





  """simple docstring"""

  __A		:  Dict       				=		get_timesformer_config(__SCREAMING_SNAKE_CASE			)

  __A		:  int       				=		TimesformerForVideoClassification(__SCREAMING_SNAKE_CASE			)

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

  model.load_state_dict(__SCREAMING_SNAKE_CASE			)
  model.eval()

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

  __A		:  List[Any]       				=		model(**__SCREAMING_SNAKE_CASE			)
  __A		:  str       				=		outputs.logits

  __A		:  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":
    __A		:  Dict       				=		torch.Size([1, 400]			)
    __A		:  str       				=		torch.tensor([-0.3_016, -0.7_713, -0.4_205]			)
  elif model_name == "timesformer-base-finetuned-k600":
    __A		:  Any       				=		torch.Size([1, 600]			)
    __A		:  Optional[int]       				=		torch.tensor([-0.7_267, -0.7_466, 3.2_404]			)
  elif model_name == "timesformer-base-finetuned-ssv2":
    __A		:  str       				=		torch.Size([1, 174]			)
    __A		:  List[Any]       				=		torch.tensor([-0.9_059, 0.6_433, -3.1_457]			)
  elif model_name == "timesformer-large-finetuned-k400":
    __A		:  Optional[int]       				=		torch.Size([1, 400]			)
    __A		:  List[Any]       				=		torch.tensor([0, 0, 0]			)
  elif model_name == "timesformer-large-finetuned-k600":
    __A		:  List[str]       				=		torch.Size([1, 600]			)
    __A		:  str       				=		torch.tensor([0, 0, 0]			)
  elif model_name == "timesformer-large-finetuned-ssv2":
    __A		:  int       				=		torch.Size([1, 174]			)
    __A		:  Tuple       				=		torch.tensor([0, 0, 0]			)
  elif model_name == "timesformer-hr-finetuned-k400":
    __A		:  Optional[int]       				=		torch.Size([1, 400]			)
    __A		:  List[str]       				=		torch.tensor([-0.9_617, -3.7_311, -3.7_708]			)
  elif model_name == "timesformer-hr-finetuned-k600":
    __A		:  Optional[Any]       				=		torch.Size([1, 600]			)
    __A		:  List[Any]       				=		torch.tensor([2.5_273, 0.7_127, 1.8_848]			)
  elif model_name == "timesformer-hr-finetuned-ssv2":
    __A		:  Optional[int]       				=		torch.Size([1, 174]			)
    __A		:  Optional[Any]       				=		torch.tensor([-3.6_756, -0.7_513, 0.7_180]			)
  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__ :		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.'
     )

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





'''simple docstring'''
A__ :		Optional[Any]					=[chr(i) for i in range(32, 126)]
A__ :		Tuple					=list(range(len(alphabets)))
A__ :		int					=list(range(len(alphabets)))
A__ :		Optional[Any]					=list(range(len(alphabets)))
A__ :		Any					=list(reversed(range(len(alphabets))))
A__ :		Optional[int]					=[]
A__ :		Tuple					=0







def 							A_  (    )					->							Any:





  """simple docstring"""

  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __A		:  Any       				=		gear_one[0]
  gear_one.append(__SCREAMING_SNAKE_CASE			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
    __A		:  Any       				=		gear_two[0]
    gear_two.append(__SCREAMING_SNAKE_CASE			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
      __A		:  str       				=		gear_three[0]
      gear_three.append(__SCREAMING_SNAKE_CASE			)
      del gear_three[0]
      gear_three_pos += 1







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							List[str]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		alphabets.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Dict       				=		gear_one[target]
  __A		:  int       				=		gear_two[target]
  __A		:  List[str]       				=		gear_three[target]
  __A		:  List[Any]       				=		reflector[target]
  __A		:  List[Any]       				=		gear_three.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		gear_two.index(__SCREAMING_SNAKE_CASE			)
  __A		:  List[str]       				=		gear_one.index(__SCREAMING_SNAKE_CASE			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     A__ :		int					=list(input('Type your message:\n'))
     while True:
          try:
               A__ :		Any					=int(input('Please set token:(must be only digits)\n'))
               break
          except Exception as error:
               print(error)
     for _ in range(token):
          rotator()
     for j in decode:
          engine(j)
     print('\n' + ''.join(code))
     print(
         F"\nYour Token is {token} please write it down.\nIf you want to decode "
         'this message again you should input same digits as token!'
     )



