


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 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  List[str]    )			-> Optional[int]:





	"""simple docstring"""







	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 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> List[str]:





	"""simple docstring"""







	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 	_lowerCAmelCase					( UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	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 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Dict:





	"""simple docstring"""







	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 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  List[Any]=None				,       UpperCamelCase__:  str=False    )			-> Tuple:





	"""simple docstring"""







	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([[0.00_19, 0.99_51, 0.00_30]]    )
	elif model_name == "xclip-base-patch32-16-frames":
		A             =					torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]    )
	elif model_name == "xclip-base-patch16":
		A             =					torch.tensor([[0.00_83, 0.96_81, 0.02_36]]    )
	elif model_name == "xclip-base-patch16-16-frames":
		A             =					torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]    )
	elif model_name == "xclip-large-patch14":
		A             =					torch.tensor([[0.00_62, 0.98_64, 0.00_75]]    )
	elif model_name == "xclip-large-patch14-16-frames":
		A             =					torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]    )
	# kinetics-600
	elif model_name == "xclip-base-patch16-kinetics-600":
		A             =					torch.tensor([[0.05_55, 0.89_14, 0.05_31]]    )
	elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
		A             =					torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]    )
	elif model_name == "xclip-large-patch14-kinetics-600":
		A             =					torch.tensor([[0.00_36, 0.99_20, 0.00_45]]    )
	# few shot
	elif model_name == "xclip-base-patch16-hmdb-2-shot":
		A             =					torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]    )
	elif model_name == "xclip-base-patch16-hmdb-4-shot":
		A             =					torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]    )
	elif model_name == "xclip-base-patch16-hmdb-8-shot":
		A             =					torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]    )
	elif model_name == "xclip-base-patch16-hmdb-16-shot":
		A             =					torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]    )
	elif model_name == "xclip-base-patch16-ucf-2-shot":
		A             =					torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]    )
	elif model_name == "xclip-base-patch16-ucf-4-shot":
		A             =					torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]    )
	elif model_name == "xclip-base-patch16-ucf-8-shot":
		A             =					torch.tensor([[0.00_27, 0.99_04, 0.00_70]]    )
	elif model_name == "xclip-base-patch16-ucf-16-shot":
		A             =					torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]    )
	# zero shot
	elif model_name == "xclip-base-patch16-zero-shot":
		A             =					torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]    )
	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__":
				_lowercase							:   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."
				)

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







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]    )
			else:
				wrong_args.append(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



