
'''simple docstring'''



import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__      :		Tuple       							=  logging.get_logger(__name__)

UpperCamelCase__      :		Tuple       							=  '▁'

UpperCamelCase__      :		Any       							=  {'vocab_file': 'prophetnet.tokenizer'}

UpperCamelCase__      :		List[str]       							=  {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

UpperCamelCase__      :		str       							=  {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

UpperCamelCase__      :		Any       							=  {
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				collections.OrderedDict()
			with open(A_    ,      """r"""    ,      encoding="""utf-8"""						) as reader:
						_SCREAMING_SNAKE_CASE      =				reader.readlines()
			for index, token in enumerate(A_						):
						_SCREAMING_SNAKE_CASE      =				token.rstrip("""\n"""						)
						_SCREAMING_SNAKE_CASE      =				index
			return vocab







class    _a				(_A):





							"""simple docstring"""




							SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE      =  PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE      =  ['input_ids', 'attention_mask']






							def __init__(							self       ,							A__       ,							A__="[SEP]"       ,							A__="[SEP]"       ,							A__="[SEP]"       ,							A__="[UNK]"       ,							A__="[PAD]"       ,							A__="[CLS]"       ,							A__="[MASK]"       ,							A__ = None       ,							**A__       ,							)    ->			None:
										_SCREAMING_SNAKE_CASE      =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=UpperCamelCase__       ,							eos_token=UpperCamelCase__       ,							sep_token=UpperCamelCase__       ,							unk_token=UpperCamelCase__       ,							pad_token=UpperCamelCase__       ,							cls_token=UpperCamelCase__       ,							mask_token=UpperCamelCase__       ,							sp_model_kwargs=self.sp_model_kwargs       ,							**UpperCamelCase__       ,							)

										try:
													import sentencepiece as spm
										except ImportError:
													logger.warning(
													    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
													    """ pip install sentencepiece"""      )
													raise

										_SCREAMING_SNAKE_CASE      =				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(str(UpperCamelCase__      )      )
										_SCREAMING_SNAKE_CASE      =				vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# put special tokens and [unused] tokens into the vocab
										_SCREAMING_SNAKE_CASE      =				{"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

										for i in range(10      ):
													_SCREAMING_SNAKE_CASE      =				F"[unused{i}]"
													_SCREAMING_SNAKE_CASE      =				5 + i

										# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
										_SCREAMING_SNAKE_CASE      =				12
										_SCREAMING_SNAKE_CASE      =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
										for k in self.fairseq_tokens_to_ids.keys():
													self.unique_no_split_tokens.append(UpperCamelCase__      )






							def __getstate__(							self      )    ->			Optional[int]:
										_SCREAMING_SNAKE_CASE      =				self.__dict__.copy()
										_SCREAMING_SNAKE_CASE      =				None
										return state






							def __setstate__(							self       ,							A__      )    ->			Optional[int]:
										_SCREAMING_SNAKE_CASE      =				d
										try:
													import sentencepiece as spm
										except ImportError:
													logger.warning(
													    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
													    """ pip install sentencepiece"""      )
													raise

										# for backward compatibility
										if not hasattr(self       ,							"""sp_model_kwargs"""      ):
													_SCREAMING_SNAKE_CASE      =				{}

										_SCREAMING_SNAKE_CASE      =				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(self.vocab_file      )






							def        UpperCamelCase				(							self       ,							A__       ,							A__ = None       ,							A__ = False      )    ->			List[int]:






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

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






							def        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			List[int]:
										_SCREAMING_SNAKE_CASE      =				[self.sep_token_id]

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






							@property
							def        UpperCamelCase				(							self      )    ->			str:
										return len(self.sp_model      ) + self.fairseq_offset






							def        UpperCamelCase				(							self      )    ->			List[Any]:
										_SCREAMING_SNAKE_CASE      =				{self.convert_ids_to_tokens(UpperCamelCase__      ): i for i in range(self.vocab_size      )}
										vocab.update(self.added_tokens_encoder      )
										return vocab






							def        UpperCamelCase				(							self       ,							A__      )    ->			str:
										return self.sp_model.encode(UpperCamelCase__       ,							out_type=UpperCamelCase__      )






							def        UpperCamelCase				(							self       ,							A__      )    ->			Dict:






										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										_SCREAMING_SNAKE_CASE      =				self.sp_model.PieceToId(UpperCamelCase__      )

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






							def        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:






										if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset      )






							def        UpperCamelCase				(							self       ,							A__      )    ->			Optional[Any]:
										_SCREAMING_SNAKE_CASE      =				"""""".join(UpperCamelCase__      ).replace(UpperCamelCase__       ,							""" """      ).strip()
										return out_string






							def        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			Tuple[str]:






										if not os.path.isdir(UpperCamelCase__      ):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
													return
										_SCREAMING_SNAKE_CASE      =				os.path.join(
										    UpperCamelCase__       ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

										if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase__      ) and os.path.isfile(self.vocab_file      ):
													copyfile(self.vocab_file       ,							UpperCamelCase__      )
										elif not os.path.isfile(self.vocab_file      ):
													with open(UpperCamelCase__       ,							"""wb"""      ) as fi:
																_SCREAMING_SNAKE_CASE      =				self.sp_model.serialized_model_proto()
																fi.write(UpperCamelCase__      )

										return (out_vocab_file,)






							def        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			List[int]:






										if token_ids_a is None:
													return token_ids_a + [self.sep_token_id]
										_SCREAMING_SNAKE_CASE      =				[self.sep_token_id]
										return token_ids_a + sep + token_ids_a + sep








'''simple docstring'''



import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				XCLIPTextConfig()

   # derive patch size from model name
   _SCREAMING_SNAKE_CASE      =				model_name.find("""patch"""						)
   _SCREAMING_SNAKE_CASE      =				int(model_name[start_idx + len("""patch"""						) : start_idx + len("""patch"""						) + 2]						)
   _SCREAMING_SNAKE_CASE      =				XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE_    ,      num_frames=SCREAMING_SNAKE_CASE_						)

   if "large" in model_name:
      _SCREAMING_SNAKE_CASE      =				7_68
      _SCREAMING_SNAKE_CASE      =				30_72
      _SCREAMING_SNAKE_CASE      =				12

      _SCREAMING_SNAKE_CASE      =				10_24
      _SCREAMING_SNAKE_CASE      =				40_96
      _SCREAMING_SNAKE_CASE      =				16
      _SCREAMING_SNAKE_CASE      =				24
      _SCREAMING_SNAKE_CASE      =				7_68
      _SCREAMING_SNAKE_CASE      =				30_72

   if model_name == "xclip-large-patch14-16-frames":
      _SCREAMING_SNAKE_CASE      =				3_36

   _SCREAMING_SNAKE_CASE      =				XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   if "large" in model_name:
      _SCREAMING_SNAKE_CASE      =				7_68

   return config
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   # text encoder
   if name == "token_embedding.weight":
      _SCREAMING_SNAKE_CASE      =				name.replace("""token_embedding.weight"""    ,      """text_model.embeddings.token_embedding.weight"""						)
   if name == "positional_embedding":
      _SCREAMING_SNAKE_CASE      =				name.replace("""positional_embedding"""    ,      """text_model.embeddings.position_embedding.weight"""						)
   if "ln_1" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""ln_1"""    ,      """layer_norm1"""						)
   if "ln_2" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""ln_2"""    ,      """layer_norm2"""						)
   if "c_fc" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""c_fc"""    ,      """fc1"""						)
   if "c_proj" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""c_proj"""    ,      """fc2"""						)
   if name.startswith("""transformer.resblocks"""						):
      _SCREAMING_SNAKE_CASE      =				name.replace("""transformer.resblocks"""    ,      """text_model.encoder.layers"""						)
   if "attn.out_proj" in name and "message" not in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""attn.out_proj"""    ,      """self_attn.out_proj"""						)
   if "ln_final" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""ln_final"""    ,      """text_model.final_layer_norm"""						)
   # visual encoder
   if name == "visual.class_embedding":
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.class_embedding"""    ,      """vision_model.embeddings.class_embedding"""						)
   if name == "visual.positional_embedding":
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.positional_embedding"""    ,      """vision_model.embeddings.position_embedding.weight"""						)
   if name.startswith("""visual.transformer.resblocks"""						):
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.transformer.resblocks"""    ,      """vision_model.encoder.layers"""						)
   if "visual.conv1" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.conv1"""    ,      """vision_model.embeddings.patch_embedding"""						)
   if "visual.ln_pre" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.ln_pre"""    ,      """vision_model.pre_layernorm"""						)
   if "visual.ln_post" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.ln_post"""    ,      """vision_model.post_layernorm"""						)
   if "visual.proj" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""visual.proj"""    ,      """visual_projection.weight"""						)
   if "text_projection" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""text_projection"""    ,      """text_projection.weight"""						)
   # things on top
   if "prompts_visual_proj" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""prompts_visual_proj"""    ,      """prompts_visual_projection"""						)
   if "prompts_visual_ln" in name:
      _SCREAMING_SNAKE_CASE      =				name.replace("""prompts_visual_ln"""    ,      """prompts_visual_layernorm"""						)
   # mit
   if name == "mit.positional_embedding":
      _SCREAMING_SNAKE_CASE      =				name.replace("""positional"""    ,      """position"""						)
   if name.startswith("""mit.resblocks"""						):
      _SCREAMING_SNAKE_CASE      =				name.replace("""mit.resblocks"""    ,      """mit.encoder.layers"""						)
   # prompts generator
   if name.startswith("""prompts_generator.norm"""						):
      _SCREAMING_SNAKE_CASE      =				name.replace("""prompts_generator.norm"""    ,      """prompts_generator.layernorm"""						)

   return name
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      _SCREAMING_SNAKE_CASE      =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_						)

      if "attn.in_proj" in key:
         _SCREAMING_SNAKE_CASE      =				key.split("""."""						)
         if key.startswith("""visual"""						):
            _SCREAMING_SNAKE_CASE      =				key_split[3]
            _SCREAMING_SNAKE_CASE      =				config.vision_config.hidden_size
            if "message_attn" in key:
               if "weight" in key:
                  _SCREAMING_SNAKE_CASE      =				val[
                      :dim, :
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      dim : dim * 2, :
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      -dim:, :
                  ]
               else:
                  _SCREAMING_SNAKE_CASE      =				val[
                      :dim
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      dim : dim * 2
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      -dim:
                  ]
            else:
               if "weight" in key:
                  _SCREAMING_SNAKE_CASE      =				val[
                      :dim, :
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      dim : dim * 2, :
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[
                      -dim:, :
                  ]
               else:
                  _SCREAMING_SNAKE_CASE      =				val[:dim]
                  _SCREAMING_SNAKE_CASE      =				val[
                      dim : dim * 2
                  ]
                  _SCREAMING_SNAKE_CASE      =				val[-dim:]
         elif key.startswith("""mit"""						):
            _SCREAMING_SNAKE_CASE      =				key_split[2]
            _SCREAMING_SNAKE_CASE      =				config.vision_config.mit_hidden_size
            if "weight" in key:
               _SCREAMING_SNAKE_CASE      =				val[:dim, :]
               _SCREAMING_SNAKE_CASE      =				val[dim : dim * 2, :]
               _SCREAMING_SNAKE_CASE      =				val[-dim:, :]
            else:
               _SCREAMING_SNAKE_CASE      =				val[:dim]
               _SCREAMING_SNAKE_CASE      =				val[dim : dim * 2]
               _SCREAMING_SNAKE_CASE      =				val[-dim:]
         else:
            _SCREAMING_SNAKE_CASE      =				key_split[2]
            _SCREAMING_SNAKE_CASE      =				config.text_config.hidden_size
            if "weight" in key:
               _SCREAMING_SNAKE_CASE      =				val[:dim, :]
               _SCREAMING_SNAKE_CASE      =				val[
                   dim : dim * 2, :
               ]
               _SCREAMING_SNAKE_CASE      =				val[-dim:, :]
            else:
               _SCREAMING_SNAKE_CASE      =				val[:dim]
               _SCREAMING_SNAKE_CASE      =				val[
                   dim : dim * 2
               ]
               _SCREAMING_SNAKE_CASE      =				val[-dim:]
      else:
         _SCREAMING_SNAKE_CASE      =				rename_key(SCREAMING_SNAKE_CASE_						)
         if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
            _SCREAMING_SNAKE_CASE      =				val.T
         _SCREAMING_SNAKE_CASE      =				val

   return orig_state_dict
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if num_frames == 8:
      _SCREAMING_SNAKE_CASE      =				"""eating_spaghetti_8_frames.npy"""
   elif num_frames == 16:
      _SCREAMING_SNAKE_CASE      =				"""eating_spaghetti.npy"""
   elif num_frames == 32:
      _SCREAMING_SNAKE_CASE      =				"""eating_spaghetti_32_frames.npy"""
   _SCREAMING_SNAKE_CASE      =				hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""    ,      filename=SCREAMING_SNAKE_CASE_    ,      repo_type="""dataset"""    ,      )
   _SCREAMING_SNAKE_CASE      =				np.load(SCREAMING_SNAKE_CASE_						)
   return list(SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				{
       # fully supervised kinetics-400 checkpoints
       """xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
       """xclip-base-patch32-16-frames""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
       ),
       """xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
       """xclip-base-patch16-16-frames""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
       ),
       """xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
       """xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f""",
       # fully supervised kinetics-600 checkpoints
       """xclip-base-patch16-kinetics-600""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
       ),
       """xclip-base-patch16-kinetics-600-16-frames""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
       ),
       """xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
       # few shot
       """xclip-base-patch16-hmdb-2-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
       ),
       """xclip-base-patch16-hmdb-4-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
       ),
       """xclip-base-patch16-hmdb-8-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
       ),
       """xclip-base-patch16-hmdb-16-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
       ),
       """xclip-base-patch16-ucf-2-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
       ),
       """xclip-base-patch16-ucf-4-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
       ),
       """xclip-base-patch16-ucf-8-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
       ),
       """xclip-base-patch16-ucf-16-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
       ),
       # zero shot
       """xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
   }

   _SCREAMING_SNAKE_CASE      =				model_to_url[model_name]
   _SCREAMING_SNAKE_CASE      =				8
   if "16-frames" in model_name:
      _SCREAMING_SNAKE_CASE      =				16
   elif "shot" in model_name:
      _SCREAMING_SNAKE_CASE      =				32

   _SCREAMING_SNAKE_CASE      =				get_xclip_config(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				XCLIPModel(SCREAMING_SNAKE_CASE_						)
   model.eval()

   if "drive" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				"""pytorch_model.bin"""
      gdown.cached_download(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      quiet=SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)["""model"""]
   else:
      _SCREAMING_SNAKE_CASE      =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_						)["""model"""]

   _SCREAMING_SNAKE_CASE      =				convert_state_dict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				XCLIPModel(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)
   assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
   model.eval()

   _SCREAMING_SNAKE_CASE      =				3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
   _SCREAMING_SNAKE_CASE      =				VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""						)
   _SCREAMING_SNAKE_CASE      =				CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""						)
   _SCREAMING_SNAKE_CASE      =				XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_    ,      tokenizer=SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				prepare_video(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				processor(
       text=["""playing sports""", """eating spaghetti""", """go shopping"""]    ,      videos=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""    ,      padding=SCREAMING_SNAKE_CASE_						)

   print("""Shape of pixel values:"""    ,      inputs.pixel_values.shape						)

   with torch.no_grad():
      _SCREAMING_SNAKE_CASE      =				model(**SCREAMING_SNAKE_CASE_						)

   # Verify outputs
   _SCREAMING_SNAKE_CASE      =				outputs.logits_per_video
   _SCREAMING_SNAKE_CASE      =				logits_per_video.softmax(dim=1						)
   print("""Probs:"""    ,      SCREAMING_SNAKE_CASE_						)
   # kinetics-400
   if model_name == "xclip-base-patch32":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0019, 0.9951, 0.0030]]						)
   elif model_name == "xclip-base-patch32-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]						)
   elif model_name == "xclip-base-patch16":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0083, 0.9681, 0.0236]]						)
   elif model_name == "xclip-base-patch16-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]						)
   elif model_name == "xclip-large-patch14":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0062, 0.9864, 0.0075]]						)
   elif model_name == "xclip-large-patch14-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]						)
   # kinetics-600
   elif model_name == "xclip-base-patch16-kinetics-600":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0555, 0.8914, 0.0531]]						)
   elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]						)
   elif model_name == "xclip-large-patch14-kinetics-600":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0036, 0.9920, 0.0045]]						)
   # few shot
   elif model_name == "xclip-base-patch16-hmdb-2-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]						)
   elif model_name == "xclip-base-patch16-hmdb-4-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]						)
   elif model_name == "xclip-base-patch16-hmdb-8-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]						)
   elif model_name == "xclip-base-patch16-hmdb-16-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]						)
   elif model_name == "xclip-base-patch16-ucf-2-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
   elif model_name == "xclip-base-patch16-ucf-4-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
   elif model_name == "xclip-base-patch16-ucf-8-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[0.0027, 0.9904, 0.0070]]						)
   elif model_name == "xclip-base-patch16-ucf-16-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]						)
   # zero shot
   elif model_name == "xclip-base-patch16-zero-shot":
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]						)
   else:
      raise ValueError(F"Model name {model_name} not supported"						)
   assert torch.allclose(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      atol=1e-3						)
   print("""Looks ok!"""						)

   if pytorch_dump_folder_path is not None:
      print(F"Saving model {model_name} to {pytorch_dump_folder_path}"						)
      model.save_pretrained(SCREAMING_SNAKE_CASE_						)

   if push_to_hub:
      print("""Pushing model, processor and slow tokenizer files to the hub..."""						)
      model.push_to_hub(SCREAMING_SNAKE_CASE_    ,      organization="""nielsr"""						)
      processor.push_to_hub(SCREAMING_SNAKE_CASE_    ,      organization="""nielsr"""						)
      slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_    ,      organization="""nielsr"""						)


if __name__ == "__main__":
     UpperCamelCase__      :		Union[str, Any]       							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default="xclip-base-patch32",
         type=str,
         help="Name of the model.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )
     parser.add_argument(
         "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
     )

     UpperCamelCase__      :		str       							=  parser.parse_args()
     convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






