



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    _snake_case (       __snake_case					,  __snake_case    )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		XCLIPTextConfig()

      # derive patch size from model name
      UpperCAmelCase_  :	Optional[int]				      =		model_name.find("patch"    )
      UpperCAmelCase_  :	Optional[Any]				      =		int(model_name[start_idx + len("patch"    ) : start_idx + len("patch"    ) + 2]    )
      UpperCAmelCase_  :	List[Any]				      =		XCLIPVisionConfig(patch_size=_snake_case					,  num_frames=_snake_case    )

      if "large" in model_name:
            UpperCAmelCase_  :	Union[str, Any]				      =		7_6_8
            UpperCAmelCase_  :	Tuple				      =		3_0_7_2
            UpperCAmelCase_  :	Optional[int]				      =		1_2

            UpperCAmelCase_  :	Dict				      =		1_0_2_4
            UpperCAmelCase_  :	Dict				      =		4_0_9_6
            UpperCAmelCase_  :	Any				      =		1_6
            UpperCAmelCase_  :	List[Any]				      =		2_4
            UpperCAmelCase_  :	str				      =		7_6_8
            UpperCAmelCase_  :	Union[str, Any]				      =		3_0_7_2

      if model_name == "xclip-large-patch14-16-frames":
            UpperCAmelCase_  :	Dict				      =		3_3_6

      UpperCAmelCase_  :	List[Any]				      =		XCLIPConfig.from_text_vision_configs(_snake_case					,  _snake_case    )

      if "large" in model_name:
            UpperCAmelCase_  :	Optional[Any]				      =		7_6_8

      return config







def    _snake_case (       __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







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

      return name







def    _snake_case (       __snake_case					,  __snake_case    )     ->     Tuple:






      '''simple docstring'''







      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_  :	Any				      =		orig_state_dict.pop(_snake_case    )

            if "attn.in_proj" in key:
                  UpperCAmelCase_  :	List[Any]				      =		key.split("."    )
                  if key.startswith("visual"    ):
                        UpperCAmelCase_  :	Optional[Any]				      =		key_split[3]
                        UpperCAmelCase_  :	List[Any]				      =		config.vision_config.hidden_size
                        if "message_attn" in key:
                              if "weight" in key:
                                    UpperCAmelCase_  :	Any				      =		val[
                                        :dim, :
                                    ]
                                    UpperCAmelCase_  :	Tuple				      =		val[
                                        dim : dim * 2, :
                                    ]
                                    UpperCAmelCase_  :	List[str]				      =		val[
                                        -dim:, :
                                    ]
                              else:
                                    UpperCAmelCase_  :	Dict				      =		val[
                                        :dim
                                    ]
                                    UpperCAmelCase_  :	Union[str, Any]				      =		val[
                                        dim : dim * 2
                                    ]
                                    UpperCAmelCase_  :	Optional[Any]				      =		val[
                                        -dim:
                                    ]
                        else:
                              if "weight" in key:
                                    UpperCAmelCase_  :	str				      =		val[
                                        :dim, :
                                    ]
                                    UpperCAmelCase_  :	Optional[int]				      =		val[
                                        dim : dim * 2, :
                                    ]
                                    UpperCAmelCase_  :	Optional[Any]				      =		val[
                                        -dim:, :
                                    ]
                              else:
                                    UpperCAmelCase_  :	Tuple				      =		val[:dim]
                                    UpperCAmelCase_  :	Any				      =		val[
                                        dim : dim * 2
                                    ]
                                    UpperCAmelCase_  :	Dict				      =		val[-dim:]
                  elif key.startswith("mit"    ):
                        UpperCAmelCase_  :	str				      =		key_split[2]
                        UpperCAmelCase_  :	List[Any]				      =		config.vision_config.mit_hidden_size
                        if "weight" in key:
                              UpperCAmelCase_  :	List[str]				      =		val[:dim, :]
                              UpperCAmelCase_  :	List[str]				      =		val[dim : dim * 2, :]
                              UpperCAmelCase_  :	Dict				      =		val[-dim:, :]
                        else:
                              UpperCAmelCase_  :	int				      =		val[:dim]
                              UpperCAmelCase_  :	Any				      =		val[dim : dim * 2]
                              UpperCAmelCase_  :	Any				      =		val[-dim:]
                  else:
                        UpperCAmelCase_  :	Optional[int]				      =		key_split[2]
                        UpperCAmelCase_  :	Dict				      =		config.text_config.hidden_size
                        if "weight" in key:
                              UpperCAmelCase_  :	List[str]				      =		val[:dim, :]
                              UpperCAmelCase_  :	Dict				      =		val[
                                  dim : dim * 2, :
                              ]
                              UpperCAmelCase_  :	int				      =		val[-dim:, :]
                        else:
                              UpperCAmelCase_  :	List[str]				      =		val[:dim]
                              UpperCAmelCase_  :	List[str]				      =		val[
                                  dim : dim * 2
                              ]
                              UpperCAmelCase_  :	int				      =		val[-dim:]
            else:
                  UpperCAmelCase_  :	str				      =		rename_key(_snake_case    )
                  if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                        UpperCAmelCase_  :	Optional[Any]				      =		val.T
                  UpperCAmelCase_  :	List[Any]				      =		val

      return orig_state_dict







def    _snake_case (       __snake_case    )     ->     List[Any]:






      '''simple docstring'''







      if num_frames == 8:
            UpperCAmelCase_  :	Tuple				      =		"eating_spaghetti_8_frames.npy"
      elif num_frames == 1_6:
            UpperCAmelCase_  :	Any				      =		"eating_spaghetti.npy"
      elif num_frames == 3_2:
            UpperCAmelCase_  :	Optional[int]				      =		"eating_spaghetti_32_frames.npy"
      UpperCAmelCase_  :	Union[str, Any]				      =		hf_hub_download(
          repo_id="hf-internal-testing/spaghetti-video"					,  filename=_snake_case					,  repo_type="dataset"					,  )
      UpperCAmelCase_  :	List[Any]				      =		np.load(_snake_case    )
      return list(_snake_case    )







def    _snake_case (       __snake_case					,  __snake_case=None					,  __snake_case=False    )     ->     List[str]:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[Any]				      =		{
          # 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",
      }

      UpperCAmelCase_  :	List[str]				      =		model_to_url[model_name]
      UpperCAmelCase_  :	Union[str, Any]				      =		8
      if "16-frames" in model_name:
            UpperCAmelCase_  :	Union[str, Any]				      =		1_6
      elif "shot" in model_name:
            UpperCAmelCase_  :	Optional[int]				      =		3_2

      UpperCAmelCase_  :	List[str]				      =		get_xclip_config(_snake_case					,  _snake_case    )
      UpperCAmelCase_  :	Dict				      =		XCLIPModel(_snake_case    )
      model.eval()

      if "drive" in checkpoint_url:
            UpperCAmelCase_  :	Optional[int]				      =		"pytorch_model.bin"
            gdown.cached_download(_snake_case					,  _snake_case					,  quiet=_snake_case    )
            UpperCAmelCase_  :	Tuple				      =		torch.load(_snake_case					,  map_location="cpu"    )["model"]
      else:
            UpperCAmelCase_  :	Union[str, Any]				      =		torch.hub.load_state_dict_from_url(_snake_case    )["model"]

      UpperCAmelCase_  :	Optional[int]				      =		convert_state_dict(_snake_case					,  _snake_case    )

      UpperCAmelCase_  :	Optional[int]				      =		XCLIPModel(_snake_case    )
      UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[Any]				      =		model.load_state_dict(_snake_case					,  strict=_snake_case    )
      assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
      model.eval()

      UpperCAmelCase_  :	Tuple				      =		3_3_6 if model_name == "xclip-large-patch14-16-frames" else 2_2_4
      UpperCAmelCase_  :	Any				      =		VideoMAEImageProcessor(size=_snake_case    )
      UpperCAmelCase_  :	Any				      =		CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"    )
      UpperCAmelCase_  :	Any				      =		CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"    )
      UpperCAmelCase_  :	Optional[Any]				      =		XCLIPProcessor(image_processor=_snake_case					,  tokenizer=_snake_case    )

      UpperCAmelCase_  :	Tuple				      =		prepare_video(_snake_case    )
      UpperCAmelCase_  :	Any				      =		processor(
          text=["playing sports", "eating spaghetti", "go shopping"]					,  videos=_snake_case					,  return_tensors="pt"					,  padding=_snake_case    )

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

      with torch.no_grad():
            UpperCAmelCase_  :	Union[str, Any]				      =		model(**_snake_case    )

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

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


if __name__ == "__main__":
   __lowerCamelCase			       =			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.'''
   )

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





import operator







def    _snake_case (       __snake_case					,  __snake_case = False					,  __snake_case = None    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		operator.lt if reverse else operator.gt
      UpperCAmelCase_  :	int				      =		solution or []

      if not arr:
            return solution

      UpperCAmelCase_  :	Union[str, Any]				      =		[arr.pop(0    )]
      for i, item in enumerate(__snake_case    ):
            if _operator(__snake_case					,  sublist[-1]    ):
                  sublist.append(__snake_case    )
                  arr.pop(__snake_case    )

    #  merging sublist into solution list
      if not solution:
            solution.extend(__snake_case    )
      else:
            while sublist:
                  UpperCAmelCase_  :	Optional[int]				      =		sublist.pop(0    )
                  for i, xx in enumerate(__snake_case    ):
                        if not _operator(__snake_case					,  __snake_case    ):
                              solution.insert(__snake_case					,  __snake_case    )
                              break
                  else:
                        solution.append(__snake_case    )

      strand_sort(__snake_case					,  __snake_case					,  __snake_case    )
      return solution


if __name__ == "__main__":
   assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
   assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
