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  (		snake_case     , snake_case  ):
							_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=lowerCamelCase__     , num_frames=lowerCamelCase__  )

							if "large" in model_name:
														_lowerCAmelCase							=							768
														_lowerCAmelCase							=							3_072
														_lowerCAmelCase							=							12

														_lowerCAmelCase							=							1_024
														_lowerCAmelCase							=							4_096
														_lowerCAmelCase							=							16
														_lowerCAmelCase							=							24
														_lowerCAmelCase							=							768
														_lowerCAmelCase							=							3_072

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

							_lowerCAmelCase							=							XCLIPConfig.from_text_vision_configs(lowerCamelCase__     , lowerCamelCase__  )

							if "large" in model_name:
														_lowerCAmelCase							=							768

							return config






def _lowerCamelCase  (		snake_case  ):
							# text encoder
							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  (		snake_case     , snake_case  ):
							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase							=							orig_state_dict.pop(lowerCamelCase__  )

														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(lowerCamelCase__  )
																					if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																												_lowerCAmelCase							=							val.T
																					_lowerCAmelCase							=							val

							return orig_state_dict






def _lowerCamelCase  (		snake_case  ):
							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=lowerCamelCase__     , repo_type='dataset'     , )
							_lowerCAmelCase							=							np.load(lowerCamelCase__  )
							return list(lowerCamelCase__  )






def _lowerCamelCase  (		snake_case     , snake_case=None     , snake_case=False  ):
							_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(lowerCamelCase__     , lowerCamelCase__  )
							_lowerCAmelCase							=							XCLIPModel(lowerCamelCase__  )
							model.eval()

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

							_lowerCAmelCase							=							convert_state_dict(lowerCamelCase__     , lowerCamelCase__  )

							_lowerCAmelCase							=							XCLIPModel(lowerCamelCase__  )
							_lowerCAmelCase			,		_lowerCAmelCase							=							model.load_state_dict(lowerCamelCase__     , strict=lowerCamelCase__  )
							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=lowerCamelCase__  )
							_lowerCAmelCase							=							CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'  )
							_lowerCAmelCase							=							CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'  )
							_lowerCAmelCase							=							XCLIPProcessor(image_processor=lowerCamelCase__     , tokenizer=lowerCamelCase__  )

							_lowerCAmelCase							=							prepare_video(lowerCamelCase__  )
							_lowerCAmelCase							=							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():
														_lowerCAmelCase							=							model(**lowerCamelCase__  )

							# Verify outputs
							_lowerCAmelCase							=							outputs.logits_per_video
							_lowerCAmelCase							=							logits_per_video.softmax(dim=1  )
							print('Probs:'     , lowerCamelCase__  )
							# 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([[7.0999E-04, 9.9883E-01, 4.5580E-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([[7.6937E-04, 9.9728E-01, 1.9473E-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([[3.3877E-04, 9.9937E-01, 2.8888E-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([[3.8554E-04, 9.9929E-01, 3.2754E-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([[7.1890E-06, 9.9994E-01, 5.6559E-05]]  )
							elif model_name == "xclip-base-patch16-hmdb-4-shot":
														_lowerCAmelCase							=							torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]  )
							elif model_name == "xclip-base-patch16-hmdb-8-shot":
														_lowerCAmelCase							=							torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]  )
							elif model_name == "xclip-base-patch16-hmdb-16-shot":
														_lowerCAmelCase							=							torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]  )
							elif model_name == "xclip-base-patch16-ucf-2-shot":
														_lowerCAmelCase							=							torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]  )
							elif model_name == "xclip-base-patch16-ucf-4-shot":
														_lowerCAmelCase							=							torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-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([[9.8219E-04, 9.9593E-01, 3.0863E-03]]  )
							# zero shot
							elif model_name == "xclip-base-patch16-zero-shot":
														_lowerCAmelCase							=							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__":
				_lowercase:	Tuple       			=		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:	Optional[int]       			=		parser.parse_args()
				convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from math import isqrt





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     [True] * max_number
				for i in range(2      ,					isqrt(max_number - 1       ) + 1       ):
								if is_prime[i]:
												for j in range(i**2      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
																lowerCamelCase_         =     False

				return [i for i in range(2      ,					lowerCamelCase__       ) if is_prime[i]]





def      lowerCamelCase_					(							lowerCamelCase__ = 1_0**8       ):
				lowerCamelCase_         =     calculate_prime_numbers(max_number // 2       )

				lowerCamelCase_         =     0
				lowerCamelCase_         =     0
				lowerCamelCase_         =     len(lowerCamelCase__       ) - 1
				while left <= right:
								while prime_numbers[left] * prime_numbers[right] >= max_number:
												right -= 1
								semiprimes_count += right - left + 1
								left += 1

				return semiprimes_count


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


