




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__							: Tuple					,	UpperCamelCase__							: int )	->			Dict:





				"""simple docstring"""


				__lowerCamelCase									=       XCLIPTextConfig()

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

				if "large" in model_name:
								__lowerCamelCase									=       768
								__lowerCamelCase									=       3072
								__lowerCamelCase									=       12

								__lowerCamelCase									=       1024
								__lowerCamelCase									=       4096
								__lowerCamelCase									=       16
								__lowerCamelCase									=       24
								__lowerCamelCase									=       768
								__lowerCamelCase									=       3072

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

				__lowerCamelCase									=       XCLIPConfig.from_text_vision_configs(UpperCamelCase__					,	UpperCamelCase__ )

				if "large" in model_name:
								__lowerCamelCase									=       768

				return config







def      lowerCamelCase_						(			UpperCamelCase__							: Any )	->			Optional[int]:





				"""simple docstring"""


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

				return name







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





				"""simple docstring"""


				for key in orig_state_dict.copy().keys():
								__lowerCamelCase									=       orig_state_dict.pop(UpperCamelCase__ )

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

				return orig_state_dict







def      lowerCamelCase_						(			UpperCamelCase__							: Dict )	->			str:





				"""simple docstring"""


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







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





				"""simple docstring"""


				__lowerCamelCase									=       {
				    # 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',
				}

				__lowerCamelCase									=       model_to_url[model_name]
				__lowerCamelCase									=       8
				if "16-frames" in model_name:
								__lowerCamelCase									=       16
				elif "shot" in model_name:
								__lowerCamelCase									=       32

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

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

				__lowerCamelCase									=       convert_state_dict(UpperCamelCase__					,	UpperCamelCase__ )

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

				__lowerCamelCase									=       336 if model_name == 'xclip-large-patch14-16-frames' else 224
				__lowerCamelCase									=       VideoMAEImageProcessor(size=UpperCamelCase__ )
				__lowerCamelCase									=       CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32' )
				__lowerCamelCase									=       CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32' )
				__lowerCamelCase									=       XCLIPProcessor(image_processor=UpperCamelCase__					,	tokenizer=UpperCamelCase__ )

				__lowerCamelCase									=       prepare_video(UpperCamelCase__ )
				__lowerCamelCase									=       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():
								__lowerCamelCase									=       model(**UpperCamelCase__ )

				# Verify outputs
				__lowerCamelCase									=       outputs.logits_per_video
				__lowerCamelCase									=       logits_per_video.softmax(dim=1 )
				print('Probs:'					,	UpperCamelCase__ )
				# kinetics-400
				if model_name == "xclip-base-patch32":
								__lowerCamelCase									=       torch.tensor([[0.00_19, 0.99_51, 0.00_30]] )
				elif model_name == "xclip-base-patch32-16-frames":
								__lowerCamelCase									=       torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]] )
				elif model_name == "xclip-base-patch16":
								__lowerCamelCase									=       torch.tensor([[0.00_83, 0.96_81, 0.02_36]] )
				elif model_name == "xclip-base-patch16-16-frames":
								__lowerCamelCase									=       torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]] )
				elif model_name == "xclip-large-patch14":
								__lowerCamelCase									=       torch.tensor([[0.00_62, 0.98_64, 0.00_75]] )
				elif model_name == "xclip-large-patch14-16-frames":
								__lowerCamelCase									=       torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]] )
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								__lowerCamelCase									=       torch.tensor([[0.05_55, 0.89_14, 0.05_31]] )
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								__lowerCamelCase									=       torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]] )
				elif model_name == "xclip-large-patch14-kinetics-600":
								__lowerCamelCase									=       torch.tensor([[0.00_36, 0.99_20, 0.00_45]] )
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								__lowerCamelCase									=       torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]] )
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								__lowerCamelCase									=       torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]] )
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								__lowerCamelCase									=       torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]] )
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								__lowerCamelCase									=       torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]] )
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								__lowerCamelCase									=       torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								__lowerCamelCase									=       torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]] )
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								__lowerCamelCase									=       torch.tensor([[0.00_27, 0.99_04, 0.00_70]] )
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								__lowerCamelCase									=       torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]] )
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								__lowerCamelCase									=       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(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								=       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								=       parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








def      lowerCamelCase_						(			)	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       0
				for i in range(1					,	1001 ):
								total += i**i
				return str(UpperCamelCase__ )[-10:]


if __name__ == "__main__":
					print(solution())


