


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 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				List[str]   ):
							a__             =     XCLIPTextConfig()

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

							if "large" in model_name:
														a__             =     7_6_8
														a__             =     3_0_7_2
														a__             =     1_2

														a__             =     1_0_2_4
														a__             =     4_0_9_6
														a__             =     1_6
														a__             =     2_4
														a__             =     7_6_8
														a__             =     3_0_7_2

							if model_name == "xclip-large-patch14-16-frames":
														a__             =     3_3_6

							a__             =     XCLIPConfig.from_text_vision_configs(__lowerCAmelCase					,    __lowerCAmelCase   )

							if "large" in model_name:
														a__             =     7_6_8

							return config






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

							return name






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				List[str]   ):
							for key in orig_state_dict.copy().keys():
														a__             =     orig_state_dict.pop(__lowerCAmelCase   )

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

							return orig_state_dict






def 							__lowercase   ( __lowerCAmelCase							:				List[str]   ):
							if num_frames == 8:
														a__             =     'eating_spaghetti_8_frames.npy'
							elif num_frames == 1_6:
														a__             =     'eating_spaghetti.npy'
							elif num_frames == 3_2:
														a__             =     'eating_spaghetti_32_frames.npy'
							a__             =     hf_hub_download(
							    repo_id='hf-internal-testing/spaghetti-video'					,    filename=__lowerCAmelCase					,    repo_type='dataset'					,    )
							a__             =     np.load(__lowerCAmelCase   )
							return list(__lowerCAmelCase   )






def 							__lowercase   ( __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				str=None					,    __lowerCAmelCase							:				Optional[Any]=False   ):
							a__             =     {
							    # 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__             =     model_to_url[model_name]
							a__             =     8
							if "16-frames" in model_name:
														a__             =     1_6
							elif "shot" in model_name:
														a__             =     3_2

							a__             =     get_xclip_config(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     XCLIPModel(__lowerCAmelCase   )
							model.eval()

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

							a__             =     convert_state_dict(__lowerCAmelCase					,    __lowerCAmelCase   )

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

							a__             =     3_3_6 if model_name == 'xclip-large-patch14-16-frames' else 2_2_4
							a__             =     VideoMAEImageProcessor(size=__lowerCAmelCase   )
							a__             =     CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'   )
							a__             =     CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'   )
							a__             =     XCLIPProcessor(image_processor=__lowerCAmelCase					,    tokenizer=__lowerCAmelCase   )

							a__             =     prepare_video(__lowerCAmelCase   )
							a__             =     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():
														a__             =     model(**__lowerCAmelCase   )

							# Verify outputs
							a__             =     outputs.logits_per_video
							a__             =     logits_per_video.softmax(dim=1   )
							print('Probs:'					,    __lowerCAmelCase   )
							# kinetics-400
							if model_name == "xclip-base-patch32":
														a__             =     torch.tensor([[0.0_019, 0.9_951, 0.0_030]]   )
							elif model_name == "xclip-base-patch32-16-frames":
														a__             =     torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]]   )
							elif model_name == "xclip-base-patch16":
														a__             =     torch.tensor([[0.0_083, 0.9_681, 0.0_236]]   )
							elif model_name == "xclip-base-patch16-16-frames":
														a__             =     torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]]   )
							elif model_name == "xclip-large-patch14":
														a__             =     torch.tensor([[0.0_062, 0.9_864, 0.0_075]]   )
							elif model_name == "xclip-large-patch14-16-frames":
														a__             =     torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]]   )
							# kinetics-600
							elif model_name == "xclip-base-patch16-kinetics-600":
														a__             =     torch.tensor([[0.0_555, 0.8_914, 0.0_531]]   )
							elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
														a__             =     torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]]   )
							elif model_name == "xclip-large-patch14-kinetics-600":
														a__             =     torch.tensor([[0.0_036, 0.9_920, 0.0_045]]   )
							# few shot
							elif model_name == "xclip-base-patch16-hmdb-2-shot":
														a__             =     torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]]   )
							elif model_name == "xclip-base-patch16-hmdb-4-shot":
														a__             =     torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]]   )
							elif model_name == "xclip-base-patch16-hmdb-8-shot":
														a__             =     torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]]   )
							elif model_name == "xclip-base-patch16-hmdb-16-shot":
														a__             =     torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]]   )
							elif model_name == "xclip-base-patch16-ucf-2-shot":
														a__             =     torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]   )
							elif model_name == "xclip-base-patch16-ucf-4-shot":
														a__             =     torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]   )
							elif model_name == "xclip-base-patch16-ucf-8-shot":
														a__             =     torch.tensor([[0.0_027, 0.9_904, 0.0_070]]   )
							elif model_name == "xclip-base-patch16-ucf-16-shot":
														a__             =     torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]]   )
							# zero shot
							elif model_name == "xclip-base-patch16-zero-shot":
														a__             =     torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]]   )
							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__":
						snake_case							:       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.'''
						)

						snake_case							:       int     			=      parser.parse_args()
						convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








def 							__lowercase   ( __lowerCAmelCase							:				list[int]   ):
							a__             =     []
							if len(__lowerCAmelCase   ) == 1:
														return [nums.copy()]
							for _ in range(len(__lowerCAmelCase   )   ):
														a__             =     nums.pop(0   )
														a__             =     permute(__lowerCAmelCase   )
														for perm in permutations:
																					perm.append(__lowerCAmelCase   )
														result.extend(__lowerCAmelCase   )
														nums.append(__lowerCAmelCase   )
							return result






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):

							def backtrack(__lowerCAmelCase							:				Any   ):
														if start == len(__lowerCAmelCase   ) - 1:
																					output.append(nums[:]   )
														else:
																					for i in range(__lowerCAmelCase					,    len(__lowerCAmelCase   )   ):
																												a__      ,					a__             =     nums[i], nums[start]
																												backtrack(start + 1   )
																												a__      ,					a__             =     nums[i], nums[start]  # backtrack

							a__             =     []
							backtrack(0   )
							return output


if __name__ == "__main__":
						import doctest

						# use res to print the data in permute2 function
						snake_case							:       Optional[Any]     			=      permutea([1, 2, 3])
						print(res)
						doctest.testmod()




