




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    A						(__A   :    str   ,				__A   :    Any       )   ->      Optional[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							XCLIPTextConfig()

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

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

          UpperCAmelCase_          =							1024
          UpperCAmelCase_          =							4096
          UpperCAmelCase_          =							16
          UpperCAmelCase_          =							24
          UpperCAmelCase_          =							768
          UpperCAmelCase_          =							3072

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

     UpperCAmelCase_          =							XCLIPConfig.from_text_vision_configs(__snake_case   ,				__snake_case       )

     if "large" in model_name:
          UpperCAmelCase_          =							768

     return config


def    A						(__A   :    List[str]       )   ->      Optional[Any]:







     """simple docstring"""


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

     return name


def    A						(__A   :    Optional[Any]   ,				__A   :    int       )   ->      List[str]:







     """simple docstring"""


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

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

     return orig_state_dict


def    A						(__A   :    int       )   ->      List[Any]:







     """simple docstring"""


     if num_frames == 8:
          UpperCAmelCase_          =							"eating_spaghetti_8_frames.npy"
     elif num_frames == 16:
          UpperCAmelCase_          =							"eating_spaghetti.npy"
     elif num_frames == 32:
          UpperCAmelCase_          =							"eating_spaghetti_32_frames.npy"
     UpperCAmelCase_          =							hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''   ,				filename=__snake_case   ,				repo_type='''dataset'''   ,				)
     UpperCAmelCase_          =							np.load(__snake_case       )
     return list(__snake_case       )


def    A						(__A   :    Dict   ,				__A   :    List[str]=None   ,				__A   :    Any=False       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							{
         # 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_          =							model_to_url[model_name]
     UpperCAmelCase_          =							8
     if "16-frames" in model_name:
          UpperCAmelCase_          =							16
     elif "shot" in model_name:
          UpperCAmelCase_          =							32

     UpperCAmelCase_          =							get_xclip_config(__snake_case   ,				__snake_case       )
     UpperCAmelCase_          =							XCLIPModel(__snake_case       )
     model.eval()

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

     UpperCAmelCase_          =							convert_state_dict(__snake_case   ,				__snake_case       )

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

     UpperCAmelCase_          =							prepare_video(__snake_case       )
     UpperCAmelCase_          =							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_          =							model(**__snake_case       )

     # Verify outputs
     UpperCAmelCase_          =							outputs.logits_per_video
     UpperCAmelCase_          =							logits_per_video.softmax(dim=1       )
     print('''Probs:'''   ,				__snake_case       )
     # kinetics-400
     if model_name == "xclip-base-patch32":
          UpperCAmelCase_          =							torch.tensor([[0.0_019, 0.9_951, 0.0_030]]       )
     elif model_name == "xclip-base-patch32-16-frames":
          UpperCAmelCase_          =							torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]]       )
     elif model_name == "xclip-base-patch16":
          UpperCAmelCase_          =							torch.tensor([[0.0_083, 0.9_681, 0.0_236]]       )
     elif model_name == "xclip-base-patch16-16-frames":
          UpperCAmelCase_          =							torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]]       )
     elif model_name == "xclip-large-patch14":
          UpperCAmelCase_          =							torch.tensor([[0.0_062, 0.9_864, 0.0_075]]       )
     elif model_name == "xclip-large-patch14-16-frames":
          UpperCAmelCase_          =							torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]]       )
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          UpperCAmelCase_          =							torch.tensor([[0.0_555, 0.8_914, 0.0_531]]       )
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          UpperCAmelCase_          =							torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]]       )
     elif model_name == "xclip-large-patch14-kinetics-600":
          UpperCAmelCase_          =							torch.tensor([[0.0_036, 0.9_920, 0.0_045]]       )
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          UpperCAmelCase_          =							torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]]       )
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          UpperCAmelCase_          =							torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]]       )
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          UpperCAmelCase_          =							torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]]       )
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          UpperCAmelCase_          =							torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]]       )
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          UpperCAmelCase_          =							torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]       )
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          UpperCAmelCase_          =							torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]       )
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          UpperCAmelCase_          =							torch.tensor([[0.0_027, 0.9_904, 0.0_070]]       )
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          UpperCAmelCase_          =							torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]]       )
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          UpperCAmelCase_          =							torch.tensor([[3.50_82E-04, 9.97_85E-01, 1.79_66E-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__":
    snake_case_ : str    		=       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."
    )

    snake_case_ : int    		=       parser.parse_args()
    convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Dict:

				"""simple docstring"""

				with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as input_file:
							__lowerCAmelCase      :  List[str]												=       re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)")
							__lowerCAmelCase      :  List[Any]												=       input_file.read()
							__lowerCAmelCase      :  Any												=       regexp.search(_SCREAMING_SNAKE_CASE)

				return match

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Optional[Any]:

				"""simple docstring"""

				with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as input_file:
							__lowerCAmelCase      :  Any												=       re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"       ,			re.DOTALL)
							__lowerCAmelCase      :  Optional[int]												=       input_file.read()
							# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
							__lowerCAmelCase      :  int												=       regexp.finditer(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Union[str, Any]												=       [match for match in matches if match is not None and match.group(1) is not None]
				return matches[0] if matches else None

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       Path("./datasets")
				__lowerCAmelCase      :  Optional[int]												=       list(dataset_paths.absolute().glob("**/*.py"))

				for dataset in dataset_files:
							if self._no_encoding_on_file_open(str(_SCREAMING_SNAKE_CASE)):
										raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""")







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       Path("./datasets")
				__lowerCAmelCase      :  Union[str, Any]												=       list(dataset_paths.absolute().glob("**/*.py"))

				for dataset in dataset_files:
							if self._no_print_statements(str(_SCREAMING_SNAKE_CASE)):
										raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead.""")