





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__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[int]    )->Any:
       A__		   =		XCLIPTextConfig()

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

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

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

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

       A__		   =		XCLIPConfig.from_text_vision_configs(UpperCamelCase__					,	UpperCamelCase__    )

       if "large" in model_name:
              A__		   =		7_68

       return config


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

       return name


def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			Union[str, Any]    )->Any:
       for key in orig_state_dict.copy().keys():
              A__		   =		orig_state_dict.pop(UpperCamelCase__    )

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

       return orig_state_dict


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


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Dict=None					,	UpperCamelCase__     :			Optional[Any]=False    )->int:
       A__		   =		{
           # 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__		   =		model_to_url[model_name]
       A__		   =		8
       if "16-frames" in model_name:
              A__		   =		16
       elif "shot" in model_name:
              A__		   =		32

       A__		   =		get_xclip_config(UpperCamelCase__					,	UpperCamelCase__    )
       A__		   =		XCLIPModel(UpperCamelCase__    )
       model.eval()

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

       A__		   =		convert_state_dict(UpperCamelCase__					,	UpperCamelCase__    )

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

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

       A__		   =		prepare_video(UpperCamelCase__    )
       A__		   =		processor(
           text=['''playing sports''', '''eating spaghetti''', '''go shopping''']					,	videos=UpperCamelCase__					,	return_tensors='''pt'''					,	padding=UpperCamelCase__    )

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

       with torch.no_grad():
              A__		   =		model(**UpperCamelCase__    )

       # Verify outputs
       A__		   =		outputs.logits_per_video
       A__		   =		logits_per_video.softmax(dim=1    )
       print('''Probs:'''					,	UpperCamelCase__    )
       # kinetics-400
       if model_name == "xclip-base-patch32":
              A__		   =		torch.tensor([[0.0019, 0.9951, 0.0030]]    )
       elif model_name == "xclip-base-patch32-16-frames":
              A__		   =		torch.tensor([[7.0_9_9_9e-0_4, 9.9_8_8_3e-0_1, 4.5_5_8_0e-0_4]]    )
       elif model_name == "xclip-base-patch16":
              A__		   =		torch.tensor([[0.0083, 0.9681, 0.0236]]    )
       elif model_name == "xclip-base-patch16-16-frames":
              A__		   =		torch.tensor([[7.6_9_3_7e-0_4, 9.9_7_2_8e-0_1, 1.9_4_7_3e-0_3]]    )
       elif model_name == "xclip-large-patch14":
              A__		   =		torch.tensor([[0.0062, 0.9864, 0.0075]]    )
       elif model_name == "xclip-large-patch14-16-frames":
              A__		   =		torch.tensor([[3.3_8_7_7e-0_4, 9.9_9_3_7e-0_1, 2.8_8_8_8e-0_4]]    )
       # kinetics-600
       elif model_name == "xclip-base-patch16-kinetics-600":
              A__		   =		torch.tensor([[0.0555, 0.8914, 0.0531]]    )
       elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
              A__		   =		torch.tensor([[3.8_5_5_4e-0_4, 9.9_9_2_9e-0_1, 3.2_7_5_4e-0_4]]    )
       elif model_name == "xclip-large-patch14-kinetics-600":
              A__		   =		torch.tensor([[0.0036, 0.9920, 0.0045]]    )
       # few shot
       elif model_name == "xclip-base-patch16-hmdb-2-shot":
              A__		   =		torch.tensor([[7.1_8_9_0e-0_6, 9.9_9_9_4e-0_1, 5.6_5_5_9e-0_5]]    )
       elif model_name == "xclip-base-patch16-hmdb-4-shot":
              A__		   =		torch.tensor([[1.0_3_2_0e-0_5, 9.9_9_9_3e-0_1, 6.2_4_3_5e-0_5]]    )
       elif model_name == "xclip-base-patch16-hmdb-8-shot":
              A__		   =		torch.tensor([[4.1_3_7_7e-0_6, 9.9_9_9_0e-0_1, 9.8_3_8_6e-0_5]]    )
       elif model_name == "xclip-base-patch16-hmdb-16-shot":
              A__		   =		torch.tensor([[4.1_3_4_7e-0_5, 9.9_9_6_2e-0_1, 3.3_4_1_1e-0_4]]    )
       elif model_name == "xclip-base-patch16-ucf-2-shot":
              A__		   =		torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]    )
       elif model_name == "xclip-base-patch16-ucf-4-shot":
              A__		   =		torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]    )
       elif model_name == "xclip-base-patch16-ucf-8-shot":
              A__		   =		torch.tensor([[0.0027, 0.9904, 0.0070]]    )
       elif model_name == "xclip-base-patch16-ucf-16-shot":
              A__		   =		torch.tensor([[9.8_2_1_9e-0_4, 9.9_5_9_3e-0_1, 3.0_8_6_3e-0_3]]    )
       # zero shot
       elif model_name == "xclip-base-patch16-zero-shot":
              A__		   =		torch.tensor([[3.5_0_8_2e-0_4, 9.9_7_8_5e-0_1, 1.7_9_6_6e-0_3]]    )
       else:
              raise ValueError(f"Model name {model_name} not supported"    )
       assert torch.allclose(UpperCamelCase__					,	UpperCamelCase__					,	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(UpperCamelCase__    )

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


if __name__ == "__main__":
       a__: Optional[int]							   =						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.'
       )

       a__: Optional[int]							   =						parser.parse_args()
       convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












class        SCREAMING_SNAKE_CASE__					:



 def __init__(				self,__lowerCamelCase					):
        A__		   =		size
        A__		   =		[0] * size
        A__		   =		[0] * size



 @staticmethod
 def 							UpperCamelCase     (				__lowerCamelCase					):
        return index | (index + 1)



 @staticmethod
 def 							UpperCamelCase     (				__lowerCamelCase					):
        return (index & (index + 1)) - 1



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		value
        while index < self.size:
               A__		   =		self.get_prev(__lowerCamelCase					) + 1
               if current_left_border == index:
                      A__		   =		value
               else:
                      A__		   =		max(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
               A__		   =		self.get_next(__lowerCamelCase					)




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        right -= 1  # Because of right is exclusive
        A__		   =		0
        while left <= right:
               A__		   =		self.get_prev(__lowerCamelCase					)
               if left <= current_left:
                      A__		   =		max(__lowerCamelCase,self.tree[right]					)
                      A__		   =		current_left
               else:
                      A__		   =		max(__lowerCamelCase,self.arr[right]					)
                      right -= 1
        return result


if __name__ == "__main__":
       import doctest

       doctest.testmod()





