





"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_          =	logging.get_logger(__name__)


lowerCamelCase_          =	{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase_          =	{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

lowerCamelCase_          =	{
    "RUCAIBox/mvp": 1_0_2_4,
}



@lru_cache()
def   __lowerCamelCase     (							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						(
          list(range(ord('''!'''							)				,     ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,     ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,     ord('''ÿ'''							) + 1							)							)
      )
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						bs[:]
      __SCREAMING_SNAKE_CASE     :str	       =						0
      for b in range(2**8							):
            if b not in bs:
                  bs.append(a_							)
                  cs.append(2**8 + n							)
                  n += 1
      __SCREAMING_SNAKE_CASE     :int	       =						[chr(a_							) for n in cs]
      return dict(zip(a_				,     a_							)							)



def   __lowerCamelCase     (							a_  :	Dict							)    ->   List[str]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						set()
      __SCREAMING_SNAKE_CASE     :Dict	       =						word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            __SCREAMING_SNAKE_CASE     :List[str]	       =						char
      return pairs







class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   str					=						VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_     :   Tuple					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_     :   List[Any]					=						['''input_ids''', '''attention_mask''']




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__="replace"	,SCREAMING_SNAKE_CASE__="<s>"	,SCREAMING_SNAKE_CASE__="</s>"	,SCREAMING_SNAKE_CASE__="</s>"	,SCREAMING_SNAKE_CASE__="<s>"	,SCREAMING_SNAKE_CASE__="<unk>"	,SCREAMING_SNAKE_CASE__="<pad>"	,SCREAMING_SNAKE_CASE__="<mask>"	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__	,)    ->   Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else bos_token
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else eos_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else sep_token
             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else cls_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else unk_token
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else mask_token

             super().__init__(
                 errors=SCREAMING_SNAKE_CASE__	,bos_token=SCREAMING_SNAKE_CASE__	,eos_token=SCREAMING_SNAKE_CASE__	,unk_token=SCREAMING_SNAKE_CASE__	,sep_token=SCREAMING_SNAKE_CASE__	,cls_token=SCREAMING_SNAKE_CASE__	,pad_token=SCREAMING_SNAKE_CASE__	,mask_token=SCREAMING_SNAKE_CASE__	,add_prefix_space=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__	,)
             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as vocab_handle:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						json.load(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Any	       =						{v: k for k, v in self.encoder.items()}
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						errors  # how to handle errors in decoding
             __SCREAMING_SNAKE_CASE     :Dict	       =						bytes_to_unicode()
             __SCREAMING_SNAKE_CASE     :Dict	       =						{v: k for k, v in self.byte_encoder.items()}
             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as merges_handle:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						merges_handle.read().split('''\n'''							)[1:-1]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[tuple(merge.split()							) for merge in bpe_merges]
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						dict(zip(SCREAMING_SNAKE_CASE__	,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
             __SCREAMING_SNAKE_CASE     :Any	       =						{}
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						add_prefix_space

             # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
             __SCREAMING_SNAKE_CASE     :int	       =						re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''							)




       @property
       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             return len(self.encoder							)




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             return dict(self.encoder	,**self.added_tokens_encoder							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             if token in self.cache:
                   return self.cache[token]
             __SCREAMING_SNAKE_CASE     :Tuple	       =						tuple(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						get_pairs(SCREAMING_SNAKE_CASE__							)

             if not pairs:
                   return token

             while True:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						min(SCREAMING_SNAKE_CASE__	,key=lambda SCREAMING_SNAKE_CASE__							: self.bpe_ranks.get(SCREAMING_SNAKE_CASE__	,float('''inf'''							)							)							)
                   if bigram not in self.bpe_ranks:
                         break
                   __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :str	       =						bigram
                   __SCREAMING_SNAKE_CASE     :Any	       =						[]
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						0
                   while i < len(SCREAMING_SNAKE_CASE__							):
                         try:
                               __SCREAMING_SNAKE_CASE     :Dict	       =						word.index(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
                         except ValueError:
                               new_word.extend(word[i:]							)
                               break
                         else:
                               new_word.extend(word[i:j]							)
                               __SCREAMING_SNAKE_CASE     :Any	       =						j

                         if word[i] == first and i < len(SCREAMING_SNAKE_CASE__							) - 1 and word[i + 1] == second:
                               new_word.append(first + second							)
                               i += 2
                         else:
                               new_word.append(word[i]							)
                               i += 1
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						tuple(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						new_word
                   if len(SCREAMING_SNAKE_CASE__							) == 1:
                         break
                   else:
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						get_pairs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						''' '''.join(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :int	       =						word
             return word




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						[]
             for token in re.findall(self.pat	,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						''''''.join(
                       self.byte_encoder[b] for b in token.encode('''utf-8'''							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                   bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE__							).split(''' '''							)							)
             return bpe_tokens




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






             return self.encoder.get(SCREAMING_SNAKE_CASE__	,self.encoder.get(self.unk_token							)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             return self.decoder.get(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						''''''.join(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''	,errors=self.errors							)
             return text




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   Tuple[str]:





             """simple docstring"""






             if not os.path.isdir(SCREAMING_SNAKE_CASE__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             __SCREAMING_SNAKE_CASE     :str	       =						os.path.join(
                 SCREAMING_SNAKE_CASE__	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
             __SCREAMING_SNAKE_CASE     :Any	       =						os.path.join(
                 SCREAMING_SNAKE_CASE__	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']							)

             with open(SCREAMING_SNAKE_CASE__	,'''w'''	,encoding='''utf-8'''							) as f:
                   f.write(json.dumps(self.encoder	,indent=2	,sort_keys=SCREAMING_SNAKE_CASE__	,ensure_ascii=SCREAMING_SNAKE_CASE__							) + '''\n'''							)

             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						0
             with open(SCREAMING_SNAKE_CASE__	,'''w'''	,encoding='''utf-8'''							) as writer:
                   writer.write('''#version: 0.2\n'''							)
                   for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,key=lambda SCREAMING_SNAKE_CASE__							: kv[1]							):
                         if index != token_index:
                               logger.warning(
                                   f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                                   ''' Please check that the tokenizer is not corrupted!'''							)
                               __SCREAMING_SNAKE_CASE     :int	       =						token_index
                         writer.write(''' '''.join(SCREAMING_SNAKE_CASE__							) + '''\n'''							)
                         index += 1

             return vocab_file, merge_file




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[self.cls_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = False							)    ->   List[int]:





             """simple docstring"""






             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=SCREAMING_SNAKE_CASE__	,token_ids_a=SCREAMING_SNAKE_CASE__	,already_has_special_tokens=SCREAMING_SNAKE_CASE__							)

             if token_ids_a is None:
                   return [1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[Any]	       =						[self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :str	       =						[self.cls_token_id]

             if token_ids_a is None:
                   return len(cls + token_ids_a + sep							) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						kwargs.pop('''add_prefix_space'''	,self.add_prefix_space							)
             if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__							) > 0 and not text[0].isspace()):
                   __SCREAMING_SNAKE_CASE     :Any	       =						''' ''' + text
             return (text, kwargs)






"""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   __lowerCamelCase     (							a_  :	Union[str, Any]							)    ->   Tuple:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						TimesformerConfig()

      if "large" in model_name:
            __SCREAMING_SNAKE_CASE     :Dict	       =						96

      if "hr" in model_name:
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						16
            __SCREAMING_SNAKE_CASE     :int	       =						4_48

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

      return config



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

      return name



def   __lowerCamelCase     (							a_  :	Optional[Any]				,     a_  :	Tuple							)    ->   Optional[int]:
      for key in orig_state_dict.copy().keys():
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						orig_state_dict.pop(a_							)

            if key.startswith('''model.'''							):
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						key.replace('''model.'''				,     ''''''							)

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

      return orig_state_dict



def   __lowerCamelCase     (							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						hf_hub_download(
          repo_id='''hf-internal-testing/spaghetti-video'''				,     filename='''eating_spaghetti.npy'''				,     repo_type='''dataset'''							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						np.load(a_							)
      return list(a_							)



def   __lowerCamelCase     (							a_  :	List[Any]				,     a_  :	int				,     a_  :	Optional[int]				,     a_  :	Optional[Any]							)    ->   Union[str, Any]:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						get_timesformer_config(a_							)

      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						TimesformerForVideoClassification(a_							)

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

      model.load_state_dict(a_							)
      model.eval()

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

      __SCREAMING_SNAKE_CASE     :Tuple	       =						model(**a_							)
      __SCREAMING_SNAKE_CASE     :List[str]	       =						outputs.logits

      __SCREAMING_SNAKE_CASE     :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":
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.Size([1, 4_00]							)
            __SCREAMING_SNAKE_CASE     :int	       =						torch.tensor([-0.3016, -0.7713, -0.4205]							)
      elif model_name == "timesformer-base-finetuned-k600":
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.Size([1, 6_00]							)
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.tensor([-0.7267, -0.7466, 3.2404]							)
      elif model_name == "timesformer-base-finetuned-ssv2":
            __SCREAMING_SNAKE_CASE     :List[str]	       =						torch.Size([1, 1_74]							)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						torch.tensor([-0.9059, 0.6433, -3.1457]							)
      elif model_name == "timesformer-large-finetuned-k400":
            __SCREAMING_SNAKE_CASE     :Tuple	       =						torch.Size([1, 4_00]							)
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.tensor([0, 0, 0]							)
      elif model_name == "timesformer-large-finetuned-k600":
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						torch.Size([1, 6_00]							)
            __SCREAMING_SNAKE_CASE     :str	       =						torch.tensor([0, 0, 0]							)
      elif model_name == "timesformer-large-finetuned-ssv2":
            __SCREAMING_SNAKE_CASE     :int	       =						torch.Size([1, 1_74]							)
            __SCREAMING_SNAKE_CASE     :Any	       =						torch.tensor([0, 0, 0]							)
      elif model_name == "timesformer-hr-finetuned-k400":
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.Size([1, 4_00]							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						torch.tensor([-0.9617, -3.7311, -3.7708]							)
      elif model_name == "timesformer-hr-finetuned-k600":
            __SCREAMING_SNAKE_CASE     :int	       =						torch.Size([1, 6_00]							)
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.tensor([2.5273, 0.7127, 1.8848]							)
      elif model_name == "timesformer-hr-finetuned-ssv2":
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						torch.Size([1, 1_74]							)
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						torch.tensor([-3.6756, -0.7513, 0.7180]							)
      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]				,     a_				,     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(a_							)
            model.save_pretrained(a_							)

      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
  )