






"""simple docstring"""
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						(		snake_case__ , snake_case__       ):
	A_		:    str						     = XCLIPTextConfig()

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

	if "large" in model_name:
		A_		:    Optional[Any]						     = 768
		A_		:    Union[str, Any]						     = 3_072
		A_		:    Any						     = 12

		A_		:    List[str]						     = 1_024
		A_		:    Dict						     = 4_096
		A_		:    Union[str, Any]						     = 16
		A_		:    List[Any]						     = 24
		A_		:    List[Any]						     = 768
		A_		:    Union[str, Any]						     = 3_072

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

	A_		:    Union[str, Any]						     = XCLIPConfig.from_text_vision_configs(snake_case__ , snake_case__       )

	if "large" in model_name:
		A_		:    List[str]						     = 768

	return config






def       __UpperCamelCase						(		snake_case__       ):
	# text encoder
	if name == "token_embedding.weight":
		A_		:    Tuple						     = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight"""       )
	if name == "positional_embedding":
		A_		:    Any						     = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight"""       )
	if "ln_1" in name:
		A_		:    List[str]						     = 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_		:    List[Any]						     = name.replace("""c_fc""" , """fc1"""       )
	if "c_proj" in name:
		A_		:    Optional[Any]						     = name.replace("""c_proj""" , """fc2"""       )
	if name.startswith("""transformer.resblocks"""       ):
		A_		:    Any						     = name.replace("""transformer.resblocks""" , """text_model.encoder.layers"""       )
	if "attn.out_proj" in name and "message" not in name:
		A_		:    str						     = name.replace("""attn.out_proj""" , """self_attn.out_proj"""       )
	if "ln_final" in name:
		A_		:    List[str]						     = 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_		:    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_		:    Tuple						     = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding"""       )
	if "visual.ln_pre" in name:
		A_		:    Any						     = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm"""       )
	if "visual.ln_post" in name:
		A_		:    List[Any]						     = name.replace("""visual.ln_post""" , """vision_model.post_layernorm"""       )
	if "visual.proj" in name:
		A_		:    Tuple						     = name.replace("""visual.proj""" , """visual_projection.weight"""       )
	if "text_projection" in name:
		A_		:    Dict						     = name.replace("""text_projection""" , """text_projection.weight"""       )
	# things on top
	if "prompts_visual_proj" in name:
		A_		:    int						     = name.replace("""prompts_visual_proj""" , """prompts_visual_projection"""       )
	if "prompts_visual_ln" in name:
		A_		:    Tuple						     = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm"""       )
	# mit
	if name == "mit.positional_embedding":
		A_		:    str						     = name.replace("""positional""" , """position"""       )
	if name.startswith("""mit.resblocks"""       ):
		A_		:    int						     = 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       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	for key in orig_state_dict.copy().keys():
		A_		:    int						     = orig_state_dict.pop(snake_case__       )

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

	return orig_state_dict






def       __UpperCamelCase						(		snake_case__       ):
	if num_frames == 8:
		A_		:    Optional[Any]						     = """eating_spaghetti_8_frames.npy"""
	elif num_frames == 16:
		A_		:    Union[str, Any]						     = """eating_spaghetti.npy"""
	elif num_frames == 32:
		A_		:    Optional[int]						     = """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       __UpperCamelCase						(		snake_case__ , snake_case__=None , snake_case__=False       ):
	A_		:    List[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_		:    str						     = model_to_url[model_name]
	A_		:    Union[str, Any]						     = 8
	if "16-frames" in model_name:
		A_		:    Tuple						     = 16
	elif "shot" in model_name:
		A_		:    Any						     = 32

	A_		:    List[str]						     = get_xclip_config(snake_case__ , snake_case__       )
	A_		:    Optional[Any]						     = XCLIPModel(snake_case__       )
	model.eval()

	if "drive" in checkpoint_url:
		A_		:    List[Any]						     = """pytorch_model.bin"""
		gdown.cached_download(snake_case__ , snake_case__ , quiet=snake_case__       )
		A_		:    List[str]						     = torch.load(snake_case__ , map_location="""cpu"""       )["""model"""]
	else:
		A_		:    Union[str, Any]						     = torch.hub.load_state_dict_from_url(snake_case__       )["""model"""]

	A_		:    Dict						     = convert_state_dict(snake_case__ , snake_case__       )

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

	A_		:    int						     = prepare_video(snake_case__       )
	A_		:    Tuple						     = 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_		:    Optional[int]						     = model(**snake_case__       )

	# Verify outputs
	A_		:    Optional[Any]						     = outputs.logits_per_video
	A_		:    int						     = logits_per_video.softmax(dim=1       )
	print("""Probs:""" , snake_case__       )
	# kinetics-400
	if model_name == "xclip-base-patch32":
		A_		:    int						     = torch.tensor([[0.0_019, 0.9_951, 0.0_030]]       )
	elif model_name == "xclip-base-patch32-16-frames":
		A_		:    Any						     = torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]]       )
	elif model_name == "xclip-base-patch16":
		A_		:    Any						     = torch.tensor([[0.0_083, 0.9_681, 0.0_236]]       )
	elif model_name == "xclip-base-patch16-16-frames":
		A_		:    Optional[int]						     = torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]]       )
	elif model_name == "xclip-large-patch14":
		A_		:    Dict						     = torch.tensor([[0.0_062, 0.9_864, 0.0_075]]       )
	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_		:    str						     = torch.tensor([[0.0_555, 0.8_914, 0.0_531]]       )
	elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
		A_		:    List[Any]						     = torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]]       )
	elif model_name == "xclip-large-patch14-kinetics-600":
		A_		:    Tuple						     = torch.tensor([[0.0_036, 0.9_920, 0.0_045]]       )
	# few shot
	elif model_name == "xclip-base-patch16-hmdb-2-shot":
		A_		:    Optional[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[Any]						     = torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]]       )
	elif model_name == "xclip-base-patch16-hmdb-8-shot":
		A_		:    Tuple						     = torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]]       )
	elif model_name == "xclip-base-patch16-hmdb-16-shot":
		A_		:    Union[str, 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_		:    List[str]						     = torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]       )
	elif model_name == "xclip-base-patch16-ucf-8-shot":
		A_		:    int						     = torch.tensor([[0.0_027, 0.9_904, 0.0_070]]       )
	elif model_name == "xclip-base-patch16-ucf-16-shot":
		A_		:    Optional[int]						     = 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_		:    str						     = 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__":
							_lowerCAmelCase       =				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."
							)

							_lowerCAmelCase       =				parser.parse_args()
							convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""
from __future__ import annotations






def       __UpperCamelCase						(		snake_case__ , snake_case__ = None , snake_case__ = None       ):
	if start is None:
		A_		:    Dict						     = 0

	if end is None:
		A_		:    Dict						     = len(snake_case__       ) - 1

	if start >= end:
		return

	A_		:    List[Any]						     = (start + end) // 2

	slowsort(snake_case__ , snake_case__ , snake_case__       )
	slowsort(snake_case__ , mid + 1 , snake_case__       )

	if sequence[end] < sequence[mid]:
		A_     ,  A_		:    Dict						     = sequence[mid], sequence[end]

	slowsort(snake_case__ , snake_case__ , end - 1       )


if __name__ == "__main__":
							from doctest import testmod

							testmod()

