

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      (lowercase_:				List[Any]					,   lowercase_:				Union[str, Any]      )  ->   Union[str, Any]:
	A__							:					Optional[Any]       =	XCLIPTextConfig()

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

	if "large" in model_name:
		A__							:					str       =	768
		A__							:					str       =	3072
		A__							:					Any       =	12

		A__							:					Tuple       =	1024
		A__							:					str       =	4096
		A__							:					Any       =	16
		A__							:					List[Any]       =	24
		A__							:					Optional[Any]       =	768
		A__							:					Any       =	3072

	if model_name == "xclip-large-patch14-16-frames":
		A__							:					List[Any]       =	336

	A__							:					List[str]       =	XCLIPConfig.from_text_vision_configs(lowercase_					,   lowercase_      )

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

	return config

def 		UpperCamelCase      (lowercase_:				Dict      )  ->   Dict:
	# text encoder
	if name == "token_embedding.weight":
		A__							:					Dict       =	name.replace("""token_embedding.weight"""					,   """text_model.embeddings.token_embedding.weight"""      )
	if name == "positional_embedding":
		A__							:					Union[str, Any]       =	name.replace("""positional_embedding"""					,   """text_model.embeddings.position_embedding.weight"""      )
	if "ln_1" in name:
		A__							:					Optional[Any]       =	name.replace("""ln_1"""					,   """layer_norm1"""      )
	if "ln_2" in name:
		A__							:					Optional[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__							:					str       =	name.replace("""c_proj"""					,   """fc2"""      )
	if name.startswith("""transformer.resblocks"""      ):
		A__							:					Dict       =	name.replace("""transformer.resblocks"""					,   """text_model.encoder.layers"""      )
	if "attn.out_proj" in name and "message" not in name:
		A__							:					Optional[Any]       =	name.replace("""attn.out_proj"""					,   """self_attn.out_proj"""      )
	if "ln_final" in name:
		A__							:					List[Any]       =	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__							:					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__							:					List[str]       =	name.replace("""visual.ln_pre"""					,   """vision_model.pre_layernorm"""      )
	if "visual.ln_post" in name:
		A__							:					str       =	name.replace("""visual.ln_post"""					,   """vision_model.post_layernorm"""      )
	if "visual.proj" in name:
		A__							:					Dict       =	name.replace("""visual.proj"""					,   """visual_projection.weight"""      )
	if "text_projection" in name:
		A__							:					Any       =	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__							:					List[Any]       =	name.replace("""prompts_visual_ln"""					,   """prompts_visual_layernorm"""      )
	# mit
	if name == "mit.positional_embedding":
		A__							:					List[str]       =	name.replace("""positional"""					,   """position"""      )
	if name.startswith("""mit.resblocks"""      ):
		A__							:					Union[str, Any]       =	name.replace("""mit.resblocks"""					,   """mit.encoder.layers"""      )
	# prompts generator
	if name.startswith("""prompts_generator.norm"""      ):
		A__							:					List[Any]       =	name.replace("""prompts_generator.norm"""					,   """prompts_generator.layernorm"""      )

	return name

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				int      )  ->   Optional[Any]:
	for key in orig_state_dict.copy().keys():
		A__							:					Dict       =	orig_state_dict.pop(lowercase_      )

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

	return orig_state_dict

def 		UpperCamelCase      (lowercase_:				Optional[int]      )  ->   List[str]:
	if num_frames == 8:
		A__							:					Tuple       =	"""eating_spaghetti_8_frames.npy"""
	elif num_frames == 16:
		A__							:					Tuple       =	"""eating_spaghetti.npy"""
	elif num_frames == 32:
		A__							:					Optional[int]       =	"""eating_spaghetti_32_frames.npy"""
	A__							:					Any       =	hf_hub_download(
	    repo_id="""hf-internal-testing/spaghetti-video"""					,   filename=lowercase_					,   repo_type="""dataset"""					,   )
	A__							:					str       =	np.load(lowercase_      )
	return list(lowercase_      )

def 		UpperCamelCase      (lowercase_:				Union[str, Any]					,   lowercase_:				Dict=None					,   lowercase_:				Optional[Any]=False      )  ->   Union[str, Any]:
	A__							:					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[Any]       =	model_to_url[model_name]
	A__							:					List[str]       =	8
	if "16-frames" in model_name:
		A__							:					Any       =	16
	elif "shot" in model_name:
		A__							:					Dict       =	32

	A__							:					Any       =	get_xclip_config(lowercase_					,   lowercase_      )
	A__							:					str       =	XCLIPModel(lowercase_      )
	model.eval()

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

	A__							:					List[Any]       =	convert_state_dict(lowercase_					,   lowercase_      )

	A__							:					Tuple       =	XCLIPModel(lowercase_      )
	A__     ,     A__							:					List[Any]       =	model.load_state_dict(lowercase_					,   strict=lowercase_      )
	assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
	model.eval()

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

	A__							:					str       =	prepare_video(lowercase_      )
	A__							:					Tuple       =	processor(
	    text=["""playing sports""", """eating spaghetti""", """go shopping"""]					,   videos=lowercase_					,   return_tensors="""pt"""					,   padding=lowercase_      )

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

	with torch.no_grad():
		A__							:					List[str]       =	model(**lowercase_      )

	# Verify outputs
	A__							:					int       =	outputs.logits_per_video
	A__							:					str       =	logits_per_video.softmax(dim=1      )
	print("""Probs:"""					,   lowercase_      )
	# 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__							:					Union[str, Any]       =	torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]]      )
	elif model_name == "xclip-base-patch16":
		A__							:					Tuple       =	torch.tensor([[0.0083, 0.9681, 0.0236]]      )
	elif model_name == "xclip-base-patch16-16-frames":
		A__							:					Union[str, Any]       =	torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]]      )
	elif model_name == "xclip-large-patch14":
		A__							:					Any       =	torch.tensor([[0.0062, 0.9864, 0.0075]]      )
	elif model_name == "xclip-large-patch14-16-frames":
		A__							:					List[Any]       =	torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]]      )
	# kinetics-600
	elif model_name == "xclip-base-patch16-kinetics-600":
		A__							:					Tuple       =	torch.tensor([[0.0555, 0.8914, 0.0531]]      )
	elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
		A__							:					Tuple       =	torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]]      )
	elif model_name == "xclip-large-patch14-kinetics-600":
		A__							:					List[Any]       =	torch.tensor([[0.0036, 0.9920, 0.0045]]      )
	# few shot
	elif model_name == "xclip-base-patch16-hmdb-2-shot":
		A__							:					List[Any]       =	torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]]      )
	elif model_name == "xclip-base-patch16-hmdb-4-shot":
		A__							:					Optional[int]       =	torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]]      )
	elif model_name == "xclip-base-patch16-hmdb-8-shot":
		A__							:					Union[str, Any]       =	torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]]      )
	elif model_name == "xclip-base-patch16-hmdb-16-shot":
		A__							:					Any       =	torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]]      )
	elif model_name == "xclip-base-patch16-ucf-2-shot":
		A__							:					Any       =	torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]      )
	elif model_name == "xclip-base-patch16-ucf-4-shot":
		A__							:					Optional[int]       =	torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-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([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]]      )
	# zero shot
	elif model_name == "xclip-base-patch16-zero-shot":
		A__							:					Union[str, Any]       =	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(lowercase_					,   lowercase_					,   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(lowercase_      )

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


if __name__ == "__main__":
	A_ :				List[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.'
	)

	A_ :				str						 =    parser.parse_args()
	convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






def 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				int      )  ->   int:
	while second != 0:
		A__							:					int       =	first & second
		first ^= second
		A__							:					int       =	c << 1
	return first


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A_ :				Optional[Any]						 =    int(input('Enter the first number: ').strip())
	A_ :				List[str]						 =    int(input('Enter the second number: ').strip())
	print(f'''{add(first, second) = }''')



