






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					(   a				:Optional[int]				)							->    Optional[int]:
 a							=   TimesformerConfig()

 if "large" in model_name:
  a							=   96

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

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

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

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

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

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

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

 a							=   TimesformerForVideoClassification(UpperCamelCase__				)

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

 model.load_state_dict(UpperCamelCase__				)
 model.eval()

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

 a							=   model(**UpperCamelCase__				)
 a							=   outputs.logits

 a							=   [
     # 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							=   torch.Size([1, 400]				)
  a							=   torch.tensor([-0.3_016, -0.7_713, -0.4_205]				)
 elif model_name == "timesformer-base-finetuned-k600":
  a							=   torch.Size([1, 600]				)
  a							=   torch.tensor([-0.7_267, -0.7_466, 3.2_404]				)
 elif model_name == "timesformer-base-finetuned-ssv2":
  a							=   torch.Size([1, 174]				)
  a							=   torch.tensor([-0.9_059, 0.6_433, -3.1_457]				)
 elif model_name == "timesformer-large-finetuned-k400":
  a							=   torch.Size([1, 400]				)
  a							=   torch.tensor([0, 0, 0]				)
 elif model_name == "timesformer-large-finetuned-k600":
  a							=   torch.Size([1, 600]				)
  a							=   torch.tensor([0, 0, 0]				)
 elif model_name == "timesformer-large-finetuned-ssv2":
  a							=   torch.Size([1, 174]				)
  a							=   torch.tensor([0, 0, 0]				)
 elif model_name == "timesformer-hr-finetuned-k400":
  a							=   torch.Size([1, 400]				)
  a							=   torch.tensor([-0.9_617, -3.7_311, -3.7_708]				)
 elif model_name == "timesformer-hr-finetuned-k600":
  a							=   torch.Size([1, 600]				)
  a							=   torch.tensor([2.5_273, 0.7_127, 1.8_848]				)
 elif model_name == "timesformer-hr-finetuned-ssv2":
  a							=   torch.Size([1, 174]				)
  a							=   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]			,							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__":
     UpperCAmelCase__            =	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."
     )

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














import numpy as np

UpperCAmelCase__            =	[
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]






class 	lowercase_      :






      '''simple docstring'''




      def __init__(   self   :		Optional[Any]						)					->None:




       """simple docstring"""


       a							=   np.array(__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		str						)					->np.ndarray:




       """simple docstring"""


       a						,  a							=   np.where(letter == self.SQUARE						)
       a							=   np.concatenate([indexa + 1, indexa + 1]						)
       return indexes







      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		int				,      __UpperCAmelCase   :		int						)					->str:




       """simple docstring"""


       return self.SQUARE[indexa - 1, indexa - 1]







      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


       a							=   message.lower()
       a							=   message.replace('''j'''				,      '''i'''						)

       a							=   ''''''
       for letter_index in range(len(__UpperCAmelCase						)						):
        if message[letter_index] != " ":
         a							=   self.letter_to_numbers(message[letter_index]						)
         a							=   encoded_message + str(numbers[0]						) + str(numbers[1]						)
        elif message[letter_index] == " ":
         a							=   encoded_message + ''' '''

       return encoded_message







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


       a							=   message.replace(''' '''				,      '''  '''						)
       a							=   ''''''
       for numbers_index in range(int(len(__UpperCAmelCase						) / 2						)						):
        if message[numbers_index * 2] != " ":
         a							=   message[numbers_index * 2]
         a							=   message[numbers_index * 2 + 1]

         a							=   self.numbers_to_letter(int(__UpperCAmelCase						)				,      int(__UpperCAmelCase						)						)
         a							=   decoded_message + letter
        elif message[numbers_index * 2] == " ":
         a							=   decoded_message + ''' '''

       return decoded_message


