



"""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        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Tuple            =TimesformerConfig()

   if "large" in model_name:
      SCREAMING_SNAKE_CASE_:	List[Any]            =96

   if "hr" in model_name:
      SCREAMING_SNAKE_CASE_:	Tuple            =16
      SCREAMING_SNAKE_CASE_:	Any            =448

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

   return config





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

   return name





def        __magic_name__			(							lowercase							,     lowercase	):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_:	str            =orig_state_dict.pop(_lowerCAmelCase	)

      if key.startswith("""model."""	):
         SCREAMING_SNAKE_CASE_:	Tuple            =key.replace("""model."""							,     """"""	)

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

   return orig_state_dict





def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	Optional[int]            =hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""							,     filename="""eating_spaghetti.npy"""							,     repo_type="""dataset"""	)
   SCREAMING_SNAKE_CASE_:	int            =np.load(_lowerCAmelCase	)
   return list(_lowerCAmelCase	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =get_timesformer_config(_lowerCAmelCase	)

   SCREAMING_SNAKE_CASE_:	int            =TimesformerForVideoClassification(_lowerCAmelCase	)

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

   model.load_state_dict(_lowerCAmelCase	)
   model.eval()

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

   SCREAMING_SNAKE_CASE_:	Optional[Any]            =model(**_lowerCAmelCase	)
   SCREAMING_SNAKE_CASE_:	List[Any]            =outputs.logits

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

   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
   )








"""simple docstring"""
from __future__ import annotations

import sys
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   int						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =letter
     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	dict[str, str]            ={}





  def __repr__(  self	:   Optional[Any]						)       ->   str:


     '''simple docstring'''




     return f'''{self.letter}:{self.freq}'''







class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Letter | TreeNode    ,						lowerCAmelCase	:   Letter | TreeNode						)       ->   List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =left
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =right







def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	dict[str, int]            ={}
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	List[Any]            =f.read(1	)
         if not c:
            break
         SCREAMING_SNAKE_CASE_:	Any            =chars[c] + 1 if c in chars else 1
   return sorted((Letter(lowercase							,     lowercase	) for c, f in chars.items())							,     key=lambda lowercase	: x.freq	)





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	list[Letter | TreeNode]            =letters  # type: ignore
   while len(lowercase	) > 1:
      SCREAMING_SNAKE_CASE_:	Any            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =left.freq + right.freq
      SCREAMING_SNAKE_CASE_:	Any            =TreeNode(lowercase							,     lowercase							,     lowercase	)
      response.append(lowercase	)
      response.sort(key=lambda lowercase	: x.freq	)
   return response[0]





def        __magic_name__			(							lowercase							,     lowercase	):
   if isinstance(lowercase							,     lowercase	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =bitstring
      return [root]
   SCREAMING_SNAKE_CASE_:	TreeNode            =root  # type: ignore
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[]
   letters += traverse_tree(treenode.left							,     bitstring + """0"""	)
   letters += traverse_tree(treenode.right							,     bitstring + """1"""	)
   return letters





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =parse_file(lowercase	)
   SCREAMING_SNAKE_CASE_:	str            =build_tree(lowercase	)
   SCREAMING_SNAKE_CASE_:	List[Any]            ={
       k: v for letter in traverse_tree(lowercase							,     """"""	) for k, v in letter.bitstring.items()
   }
   print(f'''Huffman Coding  of {file_path}: '''	)
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	Any            =f.read(1	)
         if not c:
            break
         print(letters[c]							,     end=""" """	)
   print()


if __name__ == "__main__":
   # pass the file path to the huffman function
   huffman(sys.argv[1])



