
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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						List[Any]       ) ->       Tuple:

				"""simple docstring"""







				A__  :	Dict         =							XCLIPTextConfig()

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

				if "large" in model_name:
								A__  :	List[Any]         =							7_68
								A__  :	Optional[Any]         =							30_72
								A__  :	Optional[Any]         =							12

								A__  :	Dict         =							10_24
								A__  :	Optional[int]         =							40_96
								A__  :	Union[str, Any]         =							16
								A__  :	int         =							24
								A__  :	List[Any]         =							7_68
								A__  :	str         =							30_72

				if model_name == "xclip-large-patch14-16-frames":
								A__  :	List[str]         =							3_36

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

				if "large" in model_name:
								A__  :	int         =							7_68

				return config



def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple       ) ->       Union[str, Any]:

				"""simple docstring"""







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

				return name



def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]							,						__UpperCamelCase :						Any       ) ->       Tuple:

				"""simple docstring"""







				for key in orig_state_dict.copy().keys():
								A__  :	Union[str, Any]         =							orig_state_dict.pop(_snake_case       )

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

				return orig_state_dict



def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, Any]       ) ->       str:

				"""simple docstring"""







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



def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]							,						__UpperCamelCase :						int=None							,						__UpperCamelCase :						Union[str, Any]=False       ) ->       Dict:

				"""simple docstring"""







				A__  :	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''',
				}

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

				A__  :	Optional[Any]         =							get_xclip_config(_snake_case							,						_snake_case       )
				A__  :	Union[str, Any]         =							XCLIPModel(_snake_case       )
				model.eval()

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

				A__  :	Any         =							convert_state_dict(_snake_case							,						_snake_case       )

				A__  :	Union[str, Any]         =							XCLIPModel(_snake_case       )
				A__       ,							A__  :	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()

				A__  :	Tuple         =							3_36 if model_name == '''xclip-large-patch14-16-frames''' else 2_24
				A__  :	Tuple         =							VideoMAEImageProcessor(size=_snake_case       )
				A__  :	str         =							CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''       )
				A__  :	Tuple         =							CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''       )
				A__  :	Optional[Any]         =							XCLIPProcessor(image_processor=_snake_case							,						tokenizer=_snake_case       )

				A__  :	List[Any]         =							prepare_video(_snake_case       )
				A__  :	Optional[int]         =							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():
								A__  :	List[str]         =							model(**_snake_case       )

				# Verify outputs
				A__  :	Dict         =							outputs.logits_per_video
				A__  :	Union[str, Any]         =							logits_per_video.softmax(dim=1       )
				print('''Probs:'''							,						_snake_case       )
				# kinetics-400
				if model_name == "xclip-base-patch32":
								A__  :	Optional[Any]         =							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":
								A__  :	Dict         =							torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]       )
				elif model_name == "xclip-base-patch16":
								A__  :	List[Any]         =							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":
								A__  :	Dict         =							torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]       )
				elif model_name == "xclip-large-patch14":
								A__  :	str         =							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":
								A__  :	Union[str, Any]         =							torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]       )
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								A__  :	List[Any]         =							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":
								A__  :	Optional[int]         =							torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]       )
				elif model_name == "xclip-large-patch14-kinetics-600":
								A__  :	str         =							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":
								A__  :	Union[str, Any]         =							torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]       )
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								A__  :	List[str]         =							torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]       )
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								A__  :	Dict         =							torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]       )
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								A__  :	List[Any]         =							torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]       )
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								A__  :	Dict         =							torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]       )
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								A__  :	Union[str, Any]         =							torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]       )
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								A__  :	str         =							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":
								A__  :	Optional[int]         =							torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]       )
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								A__  :	List[Any]         =							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(_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__":
					_SCREAMING_SNAKE_CASE    :      Tuple          =			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.'
					)

					_SCREAMING_SNAKE_CASE    :      List[Any]          =			parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def   __snake_case				(			UpperCamelCase__		):
					raise NotImplementedError()





	@abstractmethod
	def   __snake_case				(			self		):
					raise NotImplementedError()