


'''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 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       str      =							XCLIPTextConfig()

     # derive patch size from model name
     __A	:       Dict      =							model_name.find("patch"							)
     __A	:       List[str]      =							int(model_name[start_idx + len("patch"							) : start_idx + len("patch"							) + 2]							)
     __A	:       Dict      =							XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE       ,					num_frames=SCREAMING_SNAKE_CASE							)

     if "large" in model_name:
          __A	:       Optional[Any]      =							768
          __A	:       List[str]      =							3072
          __A	:       Union[str, Any]      =							12

          __A	:       List[str]      =							1024
          __A	:       Tuple      =							4096
          __A	:       Union[str, Any]      =							16
          __A	:       Dict      =							24
          __A	:       Tuple      =							768
          __A	:       Any      =							3072

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

     __A	:       Union[str, Any]      =							XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)

     if "large" in model_name:
          __A	:       Dict      =							768

     return config


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





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

     return name


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __A	:       Optional[Any]      =							orig_state_dict.pop(SCREAMING_SNAKE_CASE							)

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

     return orig_state_dict


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if num_frames == 8:
          __A	:       List[Any]      =							"eating_spaghetti_8_frames.npy"
     elif num_frames == 16:
          __A	:       List[Any]      =							"eating_spaghetti.npy"
     elif num_frames == 32:
          __A	:       Dict      =							"eating_spaghetti_32_frames.npy"
     __A	:       List[str]      =							hf_hub_download(
         repo_id="hf-internal-testing/spaghetti-video"       ,					filename=SCREAMING_SNAKE_CASE       ,					repo_type="dataset"       ,					)
     __A	:       str      =							np.load(SCREAMING_SNAKE_CASE							)
     return list(SCREAMING_SNAKE_CASE							)


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE=None       ,					SCREAMING_SNAKE_CASE=False							):




     '''simple docstring'''





     __A	:       Optional[int]      =							{
         # 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",
     }

     __A	:       List[str]      =							model_to_url[model_name]
     __A	:       Optional[Any]      =							8
     if "16-frames" in model_name:
          __A	:       Optional[int]      =							16
     elif "shot" in model_name:
          __A	:       Optional[int]      =							32

     __A	:       Tuple      =							get_xclip_config(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     __A	:       Optional[Any]      =							XCLIPModel(SCREAMING_SNAKE_CASE							)
     model.eval()

     if "drive" in checkpoint_url:
          __A	:       List[str]      =							"pytorch_model.bin"
          gdown.cached_download(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					quiet=SCREAMING_SNAKE_CASE							)
          __A	:       Tuple      =							torch.load(SCREAMING_SNAKE_CASE       ,					map_location="cpu"							)["model"]
     else:
          __A	:       int      =							torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE							)["model"]

     __A	:       List[Any]      =							convert_state_dict(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)

     __A	:       Tuple      =							XCLIPModel(SCREAMING_SNAKE_CASE							)
     __A  ,__A	:       Optional[Any]      =							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()

     __A	:       str      =							336 if model_name == "xclip-large-patch14-16-frames" else 224
     __A	:       Dict      =							VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE							)
     __A	:       Optional[int]      =							CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"							)
     __A	:       List[Any]      =							CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"							)
     __A	:       int      =							XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE       ,					tokenizer=SCREAMING_SNAKE_CASE							)

     __A	:       Optional[Any]      =							prepare_video(SCREAMING_SNAKE_CASE							)
     __A	:       List[str]      =							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():
          __A	:       str      =							model(**SCREAMING_SNAKE_CASE							)

     # Verify outputs
     __A	:       str      =							outputs.logits_per_video
     __A	:       List[Any]      =							logits_per_video.softmax(dim=1							)
     print("Probs:"       ,					SCREAMING_SNAKE_CASE							)
     # kinetics-400
     if model_name == "xclip-base-patch32":
          __A	:       Optional[Any]      =							torch.tensor([[0.0019, 0.9951, 0.0030]]							)
     elif model_name == "xclip-base-patch32-16-frames":
          __A	:       Dict      =							torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]							)
     elif model_name == "xclip-base-patch16":
          __A	:       Optional[int]      =							torch.tensor([[0.0083, 0.9681, 0.0236]]							)
     elif model_name == "xclip-base-patch16-16-frames":
          __A	:       List[str]      =							torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]							)
     elif model_name == "xclip-large-patch14":
          __A	:       Optional[int]      =							torch.tensor([[0.0062, 0.9864, 0.0075]]							)
     elif model_name == "xclip-large-patch14-16-frames":
          __A	:       int      =							torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]							)
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          __A	:       str      =							torch.tensor([[0.0555, 0.8914, 0.0531]]							)
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          __A	:       Tuple      =							torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]							)
     elif model_name == "xclip-large-patch14-kinetics-600":
          __A	:       Optional[int]      =							torch.tensor([[0.0036, 0.9920, 0.0045]]							)
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          __A	:       Optional[int]      =							torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]							)
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          __A	:       Any      =							torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]							)
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          __A	:       List[Any]      =							torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]							)
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          __A	:       Dict      =							torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]							)
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          __A	:       Optional[int]      =							torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          __A	:       Dict      =							torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          __A	:       Union[str, Any]      =							torch.tensor([[0.0027, 0.9904, 0.0070]]							)
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          __A	:       List[Any]      =							torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]							)
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          __A	:       int      =							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 							=  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 							=  parser.parse_args()
      convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							1
     __A	:       Union[str, Any]      =							2
     while i * i <= n:
          __A	:       List[Any]      =							0
          while n % i == 0:
               n //= i
               multiplicity += 1
          n_divisors *= multiplicity + 1
          i += 1
     if n > 1:
          n_divisors *= 2
     return n_divisors


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							1
     __A	:       Dict      =							1

     while True:
          i += 1
          t_num += i

          if count_divisors(SCREAMING_SNAKE_CASE							) > 500:
               break

     return t_num


if __name__ == "__main__":
      print(solution())

