

'''simple docstring'''





import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]						)				->  Any:
						lowercase :							Optional[int]    		=VideoMAEConfig()

						set_architecture_configs(__magic_name__   , __magic_name__						)

						if "finetuned" not in model_name:
												lowercase :							str    		=False

						if "finetuned" in model_name:
												lowercase :							Optional[int]    		='''huggingface/label-files'''
												if "kinetics" in model_name:
																		lowercase :							Union[str, Any]    		=400
																		lowercase :							List[Any]    		='''kinetics400-id2label.json'''
												elif "ssv2" in model_name:
																		lowercase :							Union[str, Any]    		=174
																		lowercase :							str    		='''something-something-v2-id2label.json'''
												else:
																		raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'''						)
												lowercase :							str    		=json.load(open(hf_hub_download(__magic_name__   , __magic_name__   , repo_type='''dataset'''						)   , '''r'''						)						)
												lowercase :							str    		={int(__magic_name__						): v for k, v in idalabel.items()}
												lowercase :							Any    		=idalabel
												lowercase :							Any    		={v: k for k, v in idalabel.items()}

						return config






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     List[Any]						)				->  Optional[int]:
						if "small" in model_name:
												lowercase :							Any    		=384
												lowercase :							Any    		=1536
												lowercase :							Dict    		=12
												lowercase :							Union[str, Any]    		=16
												lowercase :							Dict    		=12
												lowercase :							Any    		=3
												lowercase :							Optional[Any]    		=192
												lowercase :							Optional[int]    		=768
						elif "large" in model_name:
												lowercase :							Any    		=1024
												lowercase :							int    		=4096
												lowercase :							Any    		=24
												lowercase :							Any    		=16
												lowercase :							List[str]    		=12
												lowercase :							Any    		=8
												lowercase :							List[str]    		=512
												lowercase :							List[str]    		=2048
						elif "huge" in model_name:
												lowercase :							int    		=1280
												lowercase :							Any    		=5120
												lowercase :							int    		=32
												lowercase :							List[Any]    		=16
												lowercase :							List[Any]    		=12
												lowercase :							Optional[Any]    		=8
												lowercase :							Dict    		=640
												lowercase :							int    		=2560
						elif "base" not in model_name:
												raise ValueError('''Model name should include either "small", "base", "large", or "huge"'''						)






def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  Optional[int]:
						if "encoder." in name:
												lowercase :							List[str]    		=name.replace('''encoder.'''   , ''''''						)
						if "cls_token" in name:
												lowercase :							List[str]    		=name.replace('''cls_token'''   , '''videomae.embeddings.cls_token'''						)
						if "decoder_pos_embed" in name:
												lowercase :							Union[str, Any]    		=name.replace('''decoder_pos_embed'''   , '''decoder.decoder_pos_embed'''						)
						if "pos_embed" in name and "decoder" not in name:
												lowercase :							str    		=name.replace('''pos_embed'''   , '''videomae.embeddings.position_embeddings'''						)
						if "patch_embed.proj" in name:
												lowercase :							int    		=name.replace('''patch_embed.proj'''   , '''videomae.embeddings.patch_embeddings.projection'''						)
						if "patch_embed.norm" in name:
												lowercase :							Optional[Any]    		=name.replace('''patch_embed.norm'''   , '''videomae.embeddings.norm'''						)
						if "decoder.blocks" in name:
												lowercase :							List[Any]    		=name.replace('''decoder.blocks'''   , '''decoder.decoder_layers'''						)
						if "blocks" in name:
												lowercase :							Dict    		=name.replace('''blocks'''   , '''videomae.encoder.layer'''						)
						if "attn.proj" in name:
												lowercase :							Tuple    		=name.replace('''attn.proj'''   , '''attention.output.dense'''						)
						if "attn" in name and "bias" not in name:
												lowercase :							List[Any]    		=name.replace('''attn'''   , '''attention.self'''						)
						if "attn" in name:
												lowercase :							List[str]    		=name.replace('''attn'''   , '''attention.attention'''						)
						if "norm1" in name:
												lowercase :							Any    		=name.replace('''norm1'''   , '''layernorm_before'''						)
						if "norm2" in name:
												lowercase :							Union[str, Any]    		=name.replace('''norm2'''   , '''layernorm_after'''						)
						if "mlp.fc1" in name:
												lowercase :							Dict    		=name.replace('''mlp.fc1'''   , '''intermediate.dense'''						)
						if "mlp.fc2" in name:
												lowercase :							Optional[int]    		=name.replace('''mlp.fc2'''   , '''output.dense'''						)
						if "decoder_embed" in name:
												lowercase :							Union[str, Any]    		=name.replace('''decoder_embed'''   , '''decoder.decoder_embed'''						)
						if "decoder_norm" in name:
												lowercase :							List[str]    		=name.replace('''decoder_norm'''   , '''decoder.decoder_norm'''						)
						if "decoder_pred" in name:
												lowercase :							Dict    		=name.replace('''decoder_pred'''   , '''decoder.decoder_pred'''						)
						if "norm.weight" in name and "decoder" not in name and "fc" not in name:
												lowercase :							Tuple    		=name.replace('''norm.weight'''   , '''videomae.layernorm.weight'''						)
						if "norm.bias" in name and "decoder" not in name and "fc" not in name:
												lowercase :							Any    		=name.replace('''norm.bias'''   , '''videomae.layernorm.bias'''						)
						if "head" in name and "decoder" not in name:
												lowercase :							int    		=name.replace('''head'''   , '''classifier'''						)

						return name






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     Optional[Any]						)				->  str:
						for key in orig_state_dict.copy().keys():
												lowercase :							int    		=orig_state_dict.pop(__magic_name__						)

												if key.startswith('''encoder.'''						):
																		lowercase :							Optional[Any]    		=key.replace('''encoder.'''   , ''''''						)

												if "qkv" in key:
																		lowercase :							Union[str, Any]    		=key.split('''.'''						)
																		if key.startswith('''decoder.blocks'''						):
																								lowercase :							Optional[int]    		=config.decoder_hidden_size
																								lowercase :							Optional[int]    		=int(key_split[2]						)
																								lowercase :							List[Any]    		='''decoder.decoder_layers.'''
																								if "weight" in key:
																														lowercase :							Any    		=val[:dim, :]
																														lowercase :							Optional[Any]    		=val[dim : dim * 2, :]
																														lowercase :							int    		=val[-dim:, :]
																		else:
																								lowercase :							Union[str, Any]    		=config.hidden_size
																								lowercase :							List[Any]    		=int(key_split[1]						)
																								lowercase :							List[str]    		='''videomae.encoder.layer.'''
																								if "weight" in key:
																														lowercase :							str    		=val[:dim, :]
																														lowercase :							str    		=val[dim : dim * 2, :]
																														lowercase :							List[Any]    		=val[-dim:, :]
												else:
																		lowercase :							Any    		=val

						return orig_state_dict






def 				_lowerCAmelCase			(   )				->  Dict:
						lowercase :							int    		=hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video'''   , filename='''eating_spaghetti.npy'''   , repo_type='''dataset'''						)
						lowercase :							List[str]    		=np.load(__magic_name__						)
						return list(__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     str   , __magic_name__						:     Optional[Any]						)				->  Any:
						lowercase :							Any    		=get_videomae_config(__magic_name__						)

						if "finetuned" in model_name:
												lowercase :							Union[str, Any]    		=VideoMAEForVideoClassification(__magic_name__						)
						else:
												lowercase :							Tuple    		=VideoMAEForPreTraining(__magic_name__						)

						# download original checkpoint, hosted on Google Drive
						lowercase :							List[str]    		='''pytorch_model.bin'''
						gdown.cached_download(__magic_name__   , __magic_name__   , quiet=__magic_name__						)
						lowercase :							int    		=torch.load(__magic_name__   , map_location='''cpu'''						)
						if "model" in files:
												lowercase :							str    		=files['''model''']
						else:
												lowercase :							List[Any]    		=files['''module''']
						lowercase :							Optional[Any]    		=convert_state_dict(__magic_name__   , __magic_name__						)

						model.load_state_dict(__magic_name__						)
						model.eval()

						# verify model on basic input
						lowercase :							Optional[int]    		=VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   , image_std=[0.5, 0.5, 0.5]						)
						lowercase :							Any    		=prepare_video()
						lowercase :							Tuple    		=image_processor(__magic_name__   , return_tensors='''pt'''						)

						if "finetuned" not in model_name:
												lowercase :							Union[str, Any]    		=hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''   , filename='''bool_masked_pos.pt'''						)
												lowercase :							Optional[int]    		=torch.load(__magic_name__						)

						lowercase :							Optional[int]    		=model(**__magic_name__						)
						lowercase :							Optional[Any]    		=outputs.logits

						lowercase :							Tuple    		=[
						    '''videomae-small-finetuned-kinetics''',
						    '''videomae-small-finetuned-ssv2''',
						    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
						    '''videomae-base-short''',
						    '''videomae-base-short-finetuned-kinetics''',
						    '''videomae-base''',
						    '''videomae-base-finetuned-kinetics''',
						    '''videomae-large''',
						    '''videomae-large-finetuned-kinetics''',
						    '''videomae-huge-finetuned-kinetics''',
						    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
						    '''videomae-base-short-ssv2''',
						    '''videomae-base-short-finetuned-ssv2''',
						    '''videomae-base-ssv2''',
						    '''videomae-base-finetuned-ssv2''',
						]

						# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
						if model_name == "videomae-small-finetuned-kinetics":
												lowercase :							List[Any]    		=torch.Size([1, 400]						)
												lowercase :							Optional[Any]    		=torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]						)
						elif model_name == "videomae-small-finetuned-ssv2":
												lowercase :							Union[str, Any]    		=torch.Size([1, 174]						)
												lowercase :							List[str]    		=torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]						)
						elif model_name == "videomae-base":
												lowercase :							List[Any]    		=torch.Size([1, 1408, 1536]						)
												lowercase :							Optional[Any]    		=torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]						)
						elif model_name == "videomae-base-short":
												lowercase :							Dict    		=torch.Size([1, 1408, 1536]						)
												lowercase :							int    		=torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]						)
												# we verified the loss both for normalized and unnormalized targets for this one
												lowercase :							Any    		=torch.tensor([0.5_1_4_2]						) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]						)
						elif model_name == "videomae-large":
												lowercase :							Tuple    		=torch.Size([1, 1408, 1536]						)
												lowercase :							List[Any]    		=torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]						)
						elif model_name == "videomae-large-finetuned-kinetics":
												lowercase :							str    		=torch.Size([1, 400]						)
												lowercase :							Any    		=torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]						)
						elif model_name == "videomae-huge-finetuned-kinetics":
												lowercase :							Optional[int]    		=torch.Size([1, 400]						)
												lowercase :							Optional[Any]    		=torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]						)
						elif model_name == "videomae-base-short-finetuned-kinetics":
												lowercase :							List[str]    		=torch.Size([1, 400]						)
												lowercase :							List[Any]    		=torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]						)
						elif model_name == "videomae-base-finetuned-kinetics":
												lowercase :							Any    		=torch.Size([1, 400]						)
												lowercase :							Optional[int]    		=torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]						)
						elif model_name == "videomae-base-short-ssv2":
												lowercase :							int    		=torch.Size([1, 1408, 1536]						)
												lowercase :							Union[str, Any]    		=torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]						)
						elif model_name == "videomae-base-short-finetuned-ssv2":
												lowercase :							Tuple    		=torch.Size([1, 174]						)
												lowercase :							Union[str, Any]    		=torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]						)
						elif model_name == "videomae-base-ssv2":
												lowercase :							List[Any]    		=torch.Size([1, 1408, 1536]						)
												lowercase :							Dict    		=torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]						)
						elif model_name == "videomae-base-finetuned-ssv2":
												lowercase :							Optional[Any]    		=torch.Size([1, 174]						)
												lowercase :							List[str]    		=torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]						)
						else:
												raise ValueError(f'''Model name not supported. Should be one of {model_names}'''						)

						# verify logits
						assert logits.shape == expected_shape
						if "finetuned" in model_name:
												assert torch.allclose(logits[0, :3]   , __magic_name__   , atol=1E-4						)
						else:
												print('''Logits:'''   , logits[0, :3, :3]						)
												assert torch.allclose(logits[0, :3, :3]   , __magic_name__   , atol=1E-4						)
						print('''Logits ok!'''						)

						# verify loss, if applicable
						if model_name == "videomae-base-short":
												lowercase :							Optional[Any]    		=outputs.loss
												assert torch.allclose(__magic_name__   , __magic_name__   , atol=1E-4						)
												print('''Loss ok!'''						)

						if pytorch_dump_folder_path is not None:
												print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''						)
												image_processor.save_pretrained(__magic_name__						)
												model.save_pretrained(__magic_name__						)

						if push_to_hub:
												print('''Pushing to the hub...'''						)
												model.push_to_hub(__magic_name__   , organization='''nielsr'''						)


if __name__ == "__main__":
							UpperCamelCase_    						=       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--checkpoint_url""",
							    default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
							    type=str,
							    help=(
							        """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
							        """ download link."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
							    type=str,
							    help="""Path to the output PyTorch model directory.""",
							)
							parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
							parser.add_argument(
							    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
							)

							UpperCamelCase_    						=       parser.parse_args()
							convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)








'''simple docstring'''





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										if dst_width < 0 or dst_height < 0:
																raise ValueError('''Destination width/height should be > 0'''   )

										lowercase :							Union[str, Any]    		=img
										lowercase :							Union[str, Any]    		=img.shape[1]
										lowercase :							str    		=img.shape[0]
										lowercase :							Union[str, Any]    		=dst_width
										lowercase :							str    		=dst_height

										lowercase :							str    		=self.src_w / self.dst_w
										lowercase :							Optional[Any]    		=self.src_h / self.dst_h

										lowercase :							int    		=(
										    np.ones((self.dst_h, self.dst_w, 3)		,					np.uinta   ) * 255
										)







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										for i in range(self.dst_h   ):
																for j in range(self.dst_w   ):
																						lowercase :							List[Any]    		=self.img[self.get_y(UpperCAmelCase__   )][self.get_x(UpperCAmelCase__   )]







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_x * x   )







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_y * y   )


if __name__ == "__main__":
							UpperCamelCase_						,   UpperCamelCase_    						=       800, 600
							UpperCamelCase_    						=       imread("""image_data/lena.jpg""", 1)
							UpperCamelCase_    						=       NearestNeighbour(im, dst_w, dst_h)
							n.process()

							imshow(
							    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
							)
							waitKey(0)
							destroyAllWindows()





