






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 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    XCLIPTextConfig()

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

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

								lowercase__     				=    1024
								lowercase__     				=    4096
								lowercase__     				=    16
								lowercase__     				=    24
								lowercase__     				=    768
								lowercase__     				=    3072

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

				lowercase__     				=    XCLIPConfig.from_text_vision_configs(lowerCamelCase_	,     lowerCamelCase_       )

				if "large" in model_name:
								lowercase__     				=    768

				return config






def 						a    (		lowerCamelCase_       ):






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

				return name






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase__     				=    orig_state_dict.pop(lowerCamelCase_       )

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

				return orig_state_dict






def 						a    (		lowerCamelCase_       ):






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






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=None	,     lowerCamelCase_=False       ):






				'''simple docstring'''
				lowercase__     				=    {
				    # 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''',
				}

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

				lowercase__     				=    get_xclip_config(lowerCamelCase_	,     lowerCamelCase_       )
				lowercase__     				=    XCLIPModel(lowerCamelCase_       )
				model.eval()

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

				lowercase__     				=    convert_state_dict(lowerCamelCase_	,     lowerCamelCase_       )

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

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

				lowercase__     				=    prepare_video(lowerCamelCase_       )
				lowercase__     				=    processor(
				    text=['''playing sports''', '''eating spaghetti''', '''go shopping''']	,     videos=lowerCamelCase_	,     return_tensors='''pt'''	,     padding=lowerCamelCase_       )

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

				with torch.no_grad():
								lowercase__     				=    model(**lowerCamelCase_       )

				# Verify outputs
				lowercase__     				=    outputs.logits_per_video
				lowercase__     				=    logits_per_video.softmax(dim=1       )
				print('''Probs:'''	,     lowerCamelCase_       )
				# kinetics-400
				if model_name == "xclip-base-patch32":
								lowercase__     				=    torch.tensor([[0.00_19, 0.99_51, 0.00_30]]       )
				elif model_name == "xclip-base-patch32-16-frames":
								lowercase__     				=    torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]       )
				elif model_name == "xclip-base-patch16":
								lowercase__     				=    torch.tensor([[0.00_83, 0.96_81, 0.02_36]]       )
				elif model_name == "xclip-base-patch16-16-frames":
								lowercase__     				=    torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]       )
				elif model_name == "xclip-large-patch14":
								lowercase__     				=    torch.tensor([[0.00_62, 0.98_64, 0.00_75]]       )
				elif model_name == "xclip-large-patch14-16-frames":
								lowercase__     				=    torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]       )
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								lowercase__     				=    torch.tensor([[0.05_55, 0.89_14, 0.05_31]]       )
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								lowercase__     				=    torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]       )
				elif model_name == "xclip-large-patch14-kinetics-600":
								lowercase__     				=    torch.tensor([[0.00_36, 0.99_20, 0.00_45]]       )
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								lowercase__     				=    torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]       )
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								lowercase__     				=    torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]       )
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								lowercase__     				=    torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]       )
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								lowercase__     				=    torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]       )
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								lowercase__     				=    torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]       )
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								lowercase__     				=    torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]       )
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								lowercase__     				=    torch.tensor([[0.00_27, 0.99_04, 0.00_70]]       )
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								lowercase__     				=    torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]       )
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								lowercase__     				=    torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]       )
				else:
								raise ValueError(F"""Model name {model_name} not supported"""       )
				assert torch.allclose(lowerCamelCase_	,     lowerCamelCase_	,     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(lowerCamelCase_       )

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


if __name__ == "__main__":
							A__			:    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.'
							)

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












def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''

				if exponent == 1:
								return base
				if exponent % 2 == 0:
								lowercase__     				=    _modexpt(lowerCamelCase_	,     exponent // 2	,     lowerCamelCase_       ) % modulo_value
								return (x * x) % modulo_value
				else:
								return (base * _modexpt(lowerCamelCase_	,     exponent - 1	,     lowerCamelCase_       )) % modulo_value






def 						a    (		lowerCamelCase_ = 1777	,     lowerCamelCase_ = 1855	,     lowerCamelCase_ = 8       ):






				'''simple docstring'''
				lowercase__     				=    base
				for _ in range(1	,     lowerCamelCase_       ):
								lowercase__     				=    _modexpt(lowerCamelCase_	,     lowerCamelCase_	,     10**digits       )

				return result


if __name__ == "__main__":
							print(F"{solution() = }")




