

'''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)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase   :    str     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[Any]     =		{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[Any]			       =		"vit_mae"







		def __init__( self		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.0		,      A=0.0		,      A=0.02		,      A=1e-1_2		,      A=2_24		,      A=16		,      A=3		,      A=True		,      A=16		,      A=5_12		,      A=8		,      A=20_48		,      A=0.75		,      A=False		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(**A	)

					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps
					lowerCamelCase    		=  image_size
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  qkv_bias
					lowerCamelCase    		=  decoder_num_attention_heads
					lowerCamelCase    		=  decoder_hidden_size
					lowerCamelCase    		=  decoder_num_hidden_layers
					lowerCamelCase    		=  decoder_intermediate_size
					lowerCamelCase    		=  mask_ratio
					lowerCamelCase    		=  norm_pix_loss

