





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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  lowercase__           =  XCLIPTextConfig()

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

  if "large" in model_name:
    lowercase__           =  768
    lowercase__           =  3072
    lowercase__           =  12

    lowercase__           =  1024
    lowercase__           =  4096
    lowercase__           =  16
    lowercase__           =  24
    lowercase__           =  768
    lowercase__           =  3072

  if model_name == "xclip-large-patch14-16-frames":
    lowercase__           =  336

  lowercase__           =  XCLIPConfig.from_text_vision_configs(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  if "large" in model_name:
    lowercase__           =  768

  return config




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

  return name




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Optional[int]:
  for key in orig_state_dict.copy().keys():
    lowercase__           =  orig_state_dict.pop(_SCREAMING_SNAKE_CASE		)

    if "attn.in_proj" in key:
      lowercase__           =  key.split('.'		)
      if key.startswith('visual'		):
        lowercase__           =  key_split[3]
        lowercase__           =  config.vision_config.hidden_size
        if "message_attn" in key:
          if "weight" in key:
            lowercase__           =  val[
                :dim, :
            ]
            lowercase__           =  val[
                dim : dim * 2, :
            ]
            lowercase__           =  val[
                -dim:, :
            ]
          else:
            lowercase__           =  val[
                :dim
            ]
            lowercase__           =  val[
                dim : dim * 2
            ]
            lowercase__           =  val[
                -dim:
            ]
        else:
          if "weight" in key:
            lowercase__           =  val[
                :dim, :
            ]
            lowercase__           =  val[
                dim : dim * 2, :
            ]
            lowercase__           =  val[
                -dim:, :
            ]
          else:
            lowercase__           =  val[:dim]
            lowercase__           =  val[
                dim : dim * 2
            ]
            lowercase__           =  val[-dim:]
      elif key.startswith('mit'		):
        lowercase__           =  key_split[2]
        lowercase__           =  config.vision_config.mit_hidden_size
        if "weight" in key:
          lowercase__           =  val[:dim, :]
          lowercase__           =  val[dim : dim * 2, :]
          lowercase__           =  val[-dim:, :]
        else:
          lowercase__           =  val[:dim]
          lowercase__           =  val[dim : dim * 2]
          lowercase__           =  val[-dim:]
      else:
        lowercase__           =  key_split[2]
        lowercase__           =  config.text_config.hidden_size
        if "weight" in key:
          lowercase__           =  val[:dim, :]
          lowercase__           =  val[
              dim : dim * 2, :
          ]
          lowercase__           =  val[-dim:, :]
        else:
          lowercase__           =  val[:dim]
          lowercase__           =  val[
              dim : dim * 2
          ]
          lowercase__           =  val[-dim:]
    else:
      lowercase__           =  rename_key(_SCREAMING_SNAKE_CASE		)
      if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
        lowercase__           =  val.T
      lowercase__           =  val

  return orig_state_dict




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       int:
  if num_frames == 8:
    lowercase__           =  'eating_spaghetti_8_frames.npy'
  elif num_frames == 16:
    lowercase__           =  'eating_spaghetti.npy'
  elif num_frames == 32:
    lowercase__           =  'eating_spaghetti_32_frames.npy'
  lowercase__           =  hf_hub_download(
      repo_id='hf-internal-testing/spaghetti-video'		,						filename=_SCREAMING_SNAKE_CASE		,						repo_type='dataset'		,						)
  lowercase__           =  np.load(_SCREAMING_SNAKE_CASE		)
  return list(_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=False		)    ->       Union[str, Any]:
  lowercase__           =  {
      # 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',
  }

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

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

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

  lowercase__           =  convert_state_dict(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  lowercase__           =  XCLIPModel(_SCREAMING_SNAKE_CASE		)
  lowercase__      ,   lowercase__           =  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()

  lowercase__           =  336 if model_name == 'xclip-large-patch14-16-frames' else 224
  lowercase__           =  VideoMAEImageProcessor(size=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'		)
  lowercase__           =  CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'		)
  lowercase__           =  XCLIPProcessor(image_processor=_SCREAMING_SNAKE_CASE		,						tokenizer=_SCREAMING_SNAKE_CASE		)

  lowercase__           =  prepare_video(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  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():
    lowercase__           =  model(**_SCREAMING_SNAKE_CASE		)

  # Verify outputs
  lowercase__           =  outputs.logits_per_video
  lowercase__           =  logits_per_video.softmax(dim=1		)
  print('Probs:'		,						_SCREAMING_SNAKE_CASE		)
  # kinetics-400
  if model_name == "xclip-base-patch32":
    lowercase__           =  torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]		)
  elif model_name == "xclip-base-patch32-16-frames":
    lowercase__           =  torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]]		)
  elif model_name == "xclip-base-patch16":
    lowercase__           =  torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]		)
  elif model_name == "xclip-base-patch16-16-frames":
    lowercase__           =  torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]]		)
  elif model_name == "xclip-large-patch14":
    lowercase__           =  torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]		)
  elif model_name == "xclip-large-patch14-16-frames":
    lowercase__           =  torch.tensor([[3.3_877E-04, 9.9_937E-01, 2.8_888E-04]]		)
  # kinetics-600
  elif model_name == "xclip-base-patch16-kinetics-600":
    lowercase__           =  torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]		)
  elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
    lowercase__           =  torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]]		)
  elif model_name == "xclip-large-patch14-kinetics-600":
    lowercase__           =  torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]		)
  # few shot
  elif model_name == "xclip-base-patch16-hmdb-2-shot":
    lowercase__           =  torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]]		)
  elif model_name == "xclip-base-patch16-hmdb-4-shot":
    lowercase__           =  torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]]		)
  elif model_name == "xclip-base-patch16-hmdb-8-shot":
    lowercase__           =  torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]]		)
  elif model_name == "xclip-base-patch16-hmdb-16-shot":
    lowercase__           =  torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]]		)
  elif model_name == "xclip-base-patch16-ucf-2-shot":
    lowercase__           =  torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]		)
  elif model_name == "xclip-base-patch16-ucf-4-shot":
    lowercase__           =  torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]		)
  elif model_name == "xclip-base-patch16-ucf-8-shot":
    lowercase__           =  torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]		)
  elif model_name == "xclip-base-patch16-ucf-16-shot":
    lowercase__           =  torch.tensor([[9.8_219E-04, 9.9_593E-01, 3.0_863E-03]]		)
  # zero shot
  elif model_name == "xclip-base-patch16-zero-shot":
    lowercase__           =  torch.tensor([[3.5_082E-04, 9.9_785E-01, 1.7_966E-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__":
      lowercase_          =		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."""
      )

      lowercase_          =		parser.parse_args()
      convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowercase_          =		TypeVar("""KEY""")
lowercase_          =		TypeVar("""VAL""")
@dataclass(frozen=UpperCAmelCase      ,     slots=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (Generic[KEY, VAL]				):
   _UpperCamelCase   :		KEY
   _UpperCamelCase   :		VAL






class     SCREAMING_SNAKE_CASE       (_Item				):


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





     """simple docstring"""







     super().__init__(a      ,					a	)


   def __bool__(   self      :    str	)->       bool:





     """simple docstring"""







     return False






lowercase_          =		_DeletedItem()
class     SCREAMING_SNAKE_CASE       (MutableMapping[KEY, VAL]				):


   def __init__(   self      :    Tuple      ,					a      :    int = 8      ,					a      :    float = 0.75	)->       None:





     """simple docstring"""







     lowercase__           =  initial_block_size
     lowercase__           =  [None] * initial_block_size
     assert 0.0 < capacity_factor < 1.0
     lowercase__           =  capacity_factor
     lowercase__           =  0


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    KEY	)->       int:





     """simple docstring"""







     return hash(a	) % len(self._buckets	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int	)->       int:





     """simple docstring"""







     return (ind + 1) % len(self._buckets	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    int      ,					a      :    KEY      ,					a      :    VAL	)->       bool:





     """simple docstring"""







     lowercase__           =  self._buckets[ind]
     if not stored:
       lowercase__           =  _Item(a      ,					a	)
       self._len += 1
       return True
     elif stored.key == key:
       lowercase__           =  _Item(a      ,					a	)
       return True
     else:
       return False


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       bool:





     """simple docstring"""







     lowercase__           =  len(self._buckets	) * self._capacity_factor
     return len(self	) >= int(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       bool:





     """simple docstring"""







     if len(self._buckets	) <= self._initial_block_size:
       return False
     lowercase__           =  len(self._buckets	) * self._capacity_factor / 2
     return len(self	) < limit


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    int	)->       None:





     """simple docstring"""







     lowercase__           =  self._buckets
     lowercase__           =  [None] * new_size
     lowercase__           =  0
     for item in old_buckets:
       if item:
         self._add_item(item.key      ,					item.val	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     self._resize(len(self._buckets	) * 2	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     self._resize(len(self._buckets	) // 2	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    KEY	)->       Iterator[int]:





     """simple docstring"""







     lowercase__           =  self._get_bucket_index(a	)
     for _ in range(len(self._buckets	)	):
       yield ind
       lowercase__           =  self._get_next_ind(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    KEY      ,					a      :    VAL	)->       None:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       if self._try_set(a      ,					a      ,					a	):
         break


   def __setitem__(   self      :    List[Any]      ,					a      :    KEY      ,					a      :    VAL	)->       None:





     """simple docstring"""







     if self._is_full():
       self._size_up()

     self._add_item(a      ,					a	)


   def __delitem__(   self      :    str      ,					a      :    KEY	)->       None:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       lowercase__           =  self._buckets[ind]
       if item is None:
         raise KeyError(a	)
       if item is _deleted:
         continue
       if item.key == key:
         lowercase__           =  _deleted
         self._len -= 1
         break
     if self._is_sparse():
       self._size_down()


   def __getitem__(   self      :    List[str]      ,					a      :    KEY	)->       VAL:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       lowercase__           =  self._buckets[ind]
       if item is None:
         break
       if item is _deleted:
         continue
       if item.key == key:
         return item.val
     raise KeyError(a	)


   def __len__(   self      :    Tuple	)->       int:





     """simple docstring"""







     return self._len


   def __iter__(   self      :    int	)->       Iterator[KEY]:





     """simple docstring"""







     yield from (item.key for item in self._buckets if item)


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





     """simple docstring"""







     lowercase__           =  ' ,'.join(
         f"""{item.key}: {item.val}""" for item in self._buckets if item	)
     return f"""HashMap({val_string})"""


