






'''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_		(lowercase__						:       List[str]       )		->    List[str]:


				'''simple docstring'''




				lowerCAmelCase__      			=						VideoMAEConfig()

				set_architecture_configs(lowercase__     ,					lowercase__       )

				if "finetuned" not in model_name:
								lowerCAmelCase__      			=						False

				if "finetuned" in model_name:
								lowerCAmelCase__      			=						'''huggingface/label-files'''
								if "kinetics" in model_name:
												lowerCAmelCase__      			=						4_00
												lowerCAmelCase__      			=						'''kinetics400-id2label.json'''
								elif "ssv2" in model_name:
												lowerCAmelCase__      			=						1_74
												lowerCAmelCase__      			=						'''something-something-v2-id2label.json'''
								else:
												raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'''       )
								lowerCAmelCase__      			=						json.load(open(hf_hub_download(lowercase__     ,					lowercase__     ,					repo_type='''dataset'''       )     ,					'''r'''       )       )
								lowerCAmelCase__      			=						{int(lowercase__       ): v for k, v in idalabel.items()}
								lowerCAmelCase__      			=						idalabel
								lowerCAmelCase__      			=						{v: k for k, v in idalabel.items()}

				return config





def 	lowerCAmelCase_		(lowercase__						:       List[str]     ,					lowercase__						:       Union[str, Any]       )		->    Any:


				'''simple docstring'''




				if "small" in model_name:
								lowerCAmelCase__      			=						3_84
								lowerCAmelCase__      			=						15_36
								lowerCAmelCase__      			=						12
								lowerCAmelCase__      			=						16
								lowerCAmelCase__      			=						12
								lowerCAmelCase__      			=						3
								lowerCAmelCase__      			=						1_92
								lowerCAmelCase__      			=						7_68
				elif "large" in model_name:
								lowerCAmelCase__      			=						10_24
								lowerCAmelCase__      			=						40_96
								lowerCAmelCase__      			=						24
								lowerCAmelCase__      			=						16
								lowerCAmelCase__      			=						12
								lowerCAmelCase__      			=						8
								lowerCAmelCase__      			=						5_12
								lowerCAmelCase__      			=						20_48
				elif "huge" in model_name:
								lowerCAmelCase__      			=						12_80
								lowerCAmelCase__      			=						51_20
								lowerCAmelCase__      			=						32
								lowerCAmelCase__      			=						16
								lowerCAmelCase__      			=						12
								lowerCAmelCase__      			=						8
								lowerCAmelCase__      			=						6_40
								lowerCAmelCase__      			=						25_60
				elif "base" not in model_name:
								raise ValueError('''Model name should include either "small", "base", "large", or "huge"'''       )





def 	lowerCAmelCase_		(lowercase__						:       Dict       )		->    List[Any]:


				'''simple docstring'''




				if "encoder." in name:
								lowerCAmelCase__      			=						name.replace('''encoder.'''     ,					''''''       )
				if "cls_token" in name:
								lowerCAmelCase__      			=						name.replace('''cls_token'''     ,					'''videomae.embeddings.cls_token'''       )
				if "decoder_pos_embed" in name:
								lowerCAmelCase__      			=						name.replace('''decoder_pos_embed'''     ,					'''decoder.decoder_pos_embed'''       )
				if "pos_embed" in name and "decoder" not in name:
								lowerCAmelCase__      			=						name.replace('''pos_embed'''     ,					'''videomae.embeddings.position_embeddings'''       )
				if "patch_embed.proj" in name:
								lowerCAmelCase__      			=						name.replace('''patch_embed.proj'''     ,					'''videomae.embeddings.patch_embeddings.projection'''       )
				if "patch_embed.norm" in name:
								lowerCAmelCase__      			=						name.replace('''patch_embed.norm'''     ,					'''videomae.embeddings.norm'''       )
				if "decoder.blocks" in name:
								lowerCAmelCase__      			=						name.replace('''decoder.blocks'''     ,					'''decoder.decoder_layers'''       )
				if "blocks" in name:
								lowerCAmelCase__      			=						name.replace('''blocks'''     ,					'''videomae.encoder.layer'''       )
				if "attn.proj" in name:
								lowerCAmelCase__      			=						name.replace('''attn.proj'''     ,					'''attention.output.dense'''       )
				if "attn" in name and "bias" not in name:
								lowerCAmelCase__      			=						name.replace('''attn'''     ,					'''attention.self'''       )
				if "attn" in name:
								lowerCAmelCase__      			=						name.replace('''attn'''     ,					'''attention.attention'''       )
				if "norm1" in name:
								lowerCAmelCase__      			=						name.replace('''norm1'''     ,					'''layernorm_before'''       )
				if "norm2" in name:
								lowerCAmelCase__      			=						name.replace('''norm2'''     ,					'''layernorm_after'''       )
				if "mlp.fc1" in name:
								lowerCAmelCase__      			=						name.replace('''mlp.fc1'''     ,					'''intermediate.dense'''       )
				if "mlp.fc2" in name:
								lowerCAmelCase__      			=						name.replace('''mlp.fc2'''     ,					'''output.dense'''       )
				if "decoder_embed" in name:
								lowerCAmelCase__      			=						name.replace('''decoder_embed'''     ,					'''decoder.decoder_embed'''       )
				if "decoder_norm" in name:
								lowerCAmelCase__      			=						name.replace('''decoder_norm'''     ,					'''decoder.decoder_norm'''       )
				if "decoder_pred" in name:
								lowerCAmelCase__      			=						name.replace('''decoder_pred'''     ,					'''decoder.decoder_pred'''       )
				if "norm.weight" in name and "decoder" not in name and "fc" not in name:
								lowerCAmelCase__      			=						name.replace('''norm.weight'''     ,					'''videomae.layernorm.weight'''       )
				if "norm.bias" in name and "decoder" not in name and "fc" not in name:
								lowerCAmelCase__      			=						name.replace('''norm.bias'''     ,					'''videomae.layernorm.bias'''       )
				if "head" in name and "decoder" not in name:
								lowerCAmelCase__      			=						name.replace('''head'''     ,					'''classifier'''       )

				return name





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]     ,					lowercase__						:       int       )		->    Tuple:


				'''simple docstring'''




				for key in orig_state_dict.copy().keys():
								lowerCAmelCase__      			=						orig_state_dict.pop(lowercase__       )

								if key.startswith('''encoder.'''       ):
												lowerCAmelCase__      			=						key.replace('''encoder.'''     ,					''''''       )

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

				return orig_state_dict





def 	lowerCAmelCase_		()		->    str:


				'''simple docstring'''




				lowerCAmelCase__      			=						hf_hub_download(
				    repo_id='''hf-internal-testing/spaghetti-video'''     ,					filename='''eating_spaghetti.npy'''     ,					repo_type='''dataset'''       )
				lowerCAmelCase__      			=						np.load(lowercase__       )
				return list(lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       str     ,					lowercase__						:       List[Any]     ,					lowercase__						:       List[Any]       )		->    List[str]:


				'''simple docstring'''




				lowerCAmelCase__      			=						get_videomae_config(lowercase__       )

				if "finetuned" in model_name:
								lowerCAmelCase__      			=						VideoMAEForVideoClassification(lowercase__       )
				else:
								lowerCAmelCase__      			=						VideoMAEForPreTraining(lowercase__       )

				# download original checkpoint, hosted on Google Drive
				lowerCAmelCase__      			=						'''pytorch_model.bin'''
				gdown.cached_download(lowercase__     ,					lowercase__     ,					quiet=lowercase__       )
				lowerCAmelCase__      			=						torch.load(lowercase__     ,					map_location='''cpu'''       )
				if "model" in files:
								lowerCAmelCase__      			=						files['''model''']
				else:
								lowerCAmelCase__      			=						files['''module''']
				lowerCAmelCase__      			=						convert_state_dict(lowercase__     ,					lowercase__       )

				model.load_state_dict(lowercase__       )
				model.eval()

				# verify model on basic input
				lowerCAmelCase__      			=						VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,					image_std=[0.5, 0.5, 0.5]       )
				lowerCAmelCase__      			=						prepare_video()
				lowerCAmelCase__      			=						image_processor(lowercase__     ,					return_tensors='''pt'''       )

				if "finetuned" not in model_name:
								lowerCAmelCase__      			=						hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''     ,					filename='''bool_masked_pos.pt'''       )
								lowerCAmelCase__      			=						torch.load(lowercase__       )

				lowerCAmelCase__      			=						model(**lowercase__       )
				lowerCAmelCase__      			=						outputs.logits

				lowerCAmelCase__      			=						[
				    '''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":
								lowerCAmelCase__      			=						torch.Size([1, 4_00]       )
								lowerCAmelCase__      			=						torch.tensor([-0.9291, -0.4061, -0.9307]       )
				elif model_name == "videomae-small-finetuned-ssv2":
								lowerCAmelCase__      			=						torch.Size([1, 1_74]       )
								lowerCAmelCase__      			=						torch.tensor([0.2671, -0.4689, -0.8235]       )
				elif model_name == "videomae-base":
								lowerCAmelCase__      			=						torch.Size([1, 14_08, 15_36]       )
								lowerCAmelCase__      			=						torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]       )
				elif model_name == "videomae-base-short":
								lowerCAmelCase__      			=						torch.Size([1, 14_08, 15_36]       )
								lowerCAmelCase__      			=						torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]       )
								# we verified the loss both for normalized and unnormalized targets for this one
								lowerCAmelCase__      			=						torch.tensor([0.5142]       ) if config.norm_pix_loss else torch.tensor([0.6469]       )
				elif model_name == "videomae-large":
								lowerCAmelCase__      			=						torch.Size([1, 14_08, 15_36]       )
								lowerCAmelCase__      			=						torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]       )
				elif model_name == "videomae-large-finetuned-kinetics":
								lowerCAmelCase__      			=						torch.Size([1, 4_00]       )
								lowerCAmelCase__      			=						torch.tensor([0.0771, 0.0011, -0.3625]       )
				elif model_name == "videomae-huge-finetuned-kinetics":
								lowerCAmelCase__      			=						torch.Size([1, 4_00]       )
								lowerCAmelCase__      			=						torch.tensor([0.2433, 0.1632, -0.4894]       )
				elif model_name == "videomae-base-short-finetuned-kinetics":
								lowerCAmelCase__      			=						torch.Size([1, 4_00]       )
								lowerCAmelCase__      			=						torch.tensor([0.6588, 0.0990, -0.2493]       )
				elif model_name == "videomae-base-finetuned-kinetics":
								lowerCAmelCase__      			=						torch.Size([1, 4_00]       )
								lowerCAmelCase__      			=						torch.tensor([0.3669, -0.0688, -0.2421]       )
				elif model_name == "videomae-base-short-ssv2":
								lowerCAmelCase__      			=						torch.Size([1, 14_08, 15_36]       )
								lowerCAmelCase__      			=						torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]       )
				elif model_name == "videomae-base-short-finetuned-ssv2":
								lowerCAmelCase__      			=						torch.Size([1, 1_74]       )
								lowerCAmelCase__      			=						torch.tensor([-0.0537, -0.1539, -0.3266]       )
				elif model_name == "videomae-base-ssv2":
								lowerCAmelCase__      			=						torch.Size([1, 14_08, 15_36]       )
								lowerCAmelCase__      			=						torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]       )
				elif model_name == "videomae-base-finetuned-ssv2":
								lowerCAmelCase__      			=						torch.Size([1, 1_74]       )
								lowerCAmelCase__      			=						torch.tensor([0.1961, -0.8337, -0.6389]       )
				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]     ,					lowercase__     ,					atol=1e-4       )
				else:
								print('''Logits:'''     ,					logits[0, :3, :3]       )
								assert torch.allclose(logits[0, :3, :3]     ,					lowercase__     ,					atol=1e-4       )
				print('''Logits ok!'''       )

				# verify loss, if applicable
				if model_name == "videomae-base-short":
								lowerCAmelCase__      			=						outputs.loss
								assert torch.allclose(lowercase__     ,					lowercase__     ,					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(lowercase__       )
								model.save_pretrained(lowercase__       )

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


if __name__ == "__main__":
			_UpperCAmelCase			:		int   				=    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			:		Optional[int]   				=    parser.parse_args()
			convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)













from __future__ import annotations





def 	lowerCAmelCase_		(lowercase__						:       list[int]       )		->    bool:


				'''simple docstring'''




				return len(set(lowercase__       )       ) == len(lowercase__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





