






"""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   a    (     __UpperCAmelCase      :    int  )    ->							Optional[Any]:
						__magic_name__:	Dict							=       VideoMAEConfig()

						set_architecture_configs(__UpperCAmelCase      ,   __UpperCAmelCase  )

						if "finetuned" not in model_name:
												__magic_name__:	Dict							=       False

						if "finetuned" in model_name:
												__magic_name__:	Tuple							=       """huggingface/label-files"""
												if "kinetics" in model_name:
																		__magic_name__:	Any							=       4_0_0
																		__magic_name__:	Any							=       """kinetics400-id2label.json"""
												elif "ssv2" in model_name:
																		__magic_name__:	List[str]							=       1_7_4
																		__magic_name__:	Any							=       """something-something-v2-id2label.json"""
												else:
																		raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""  )
												__magic_name__:	int							=       json.load(open(hf_hub_download(__UpperCAmelCase      ,   __UpperCAmelCase      ,   repo_type="""dataset"""  )      ,   """r"""  )  )
												__magic_name__:	Union[str, Any]							=       {int(__UpperCAmelCase  ): v for k, v in idalabel.items()}
												__magic_name__:	int							=       idalabel
												__magic_name__:	str							=       {v: k for k, v in idalabel.items()}

						return config



def   a    (     __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    Optional[Any]  )    ->							Optional[int]:
						if "small" in model_name:
												__magic_name__:	List[Any]							=       3_8_4
												__magic_name__:	Optional[int]							=       1_5_3_6
												__magic_name__:	Union[str, Any]							=       1_2
												__magic_name__:	List[Any]							=       1_6
												__magic_name__:	Tuple							=       1_2
												__magic_name__:	Optional[Any]							=       3
												__magic_name__:	Dict							=       1_9_2
												__magic_name__:	Tuple							=       7_6_8
						elif "large" in model_name:
												__magic_name__:	int							=       1_0_2_4
												__magic_name__:	Optional[int]							=       4_0_9_6
												__magic_name__:	Dict							=       2_4
												__magic_name__:	Any							=       1_6
												__magic_name__:	Optional[int]							=       1_2
												__magic_name__:	str							=       8
												__magic_name__:	Optional[Any]							=       5_1_2
												__magic_name__:	Tuple							=       2_0_4_8
						elif "huge" in model_name:
												__magic_name__:	str							=       1_2_8_0
												__magic_name__:	Optional[int]							=       5_1_2_0
												__magic_name__:	Any							=       3_2
												__magic_name__:	int							=       1_6
												__magic_name__:	Any							=       1_2
												__magic_name__:	int							=       8
												__magic_name__:	Any							=       6_4_0
												__magic_name__:	Tuple							=       2_5_6_0
						elif "base" not in model_name:
												raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""  )



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

						return name



def   a    (     __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    Dict  )    ->							List[Any]:
						for key in orig_state_dict.copy().keys():
												__magic_name__:	List[Any]							=       orig_state_dict.pop(__UpperCAmelCase  )

												if key.startswith("""encoder."""  ):
																		__magic_name__:	Optional[Any]							=       key.replace("""encoder."""      ,   """"""  )

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

						return orig_state_dict



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



def   a    (     __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    Tuple  )    ->							Tuple:
						__magic_name__:	Tuple							=       get_videomae_config(__UpperCAmelCase  )

						if "finetuned" in model_name:
												__magic_name__:	int							=       VideoMAEForVideoClassification(__UpperCAmelCase  )
						else:
												__magic_name__:	List[Any]							=       VideoMAEForPreTraining(__UpperCAmelCase  )

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

						model.load_state_dict(__UpperCAmelCase  )
						model.eval()

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

						if "finetuned" not in model_name:
												__magic_name__:	Dict							=       hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""      ,   filename="""bool_masked_pos.pt"""  )
												__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase  )

						__magic_name__:	int							=       model(**__UpperCAmelCase  )
						__magic_name__:	int							=       outputs.logits

						__magic_name__:	Optional[int]							=       [
						    """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":
												__magic_name__:	Optional[int]							=       torch.Size([1, 4_0_0]  )
												__magic_name__:	int							=       torch.tensor([-0.92_91, -0.40_61, -0.93_07]  )
						elif model_name == "videomae-small-finetuned-ssv2":
												__magic_name__:	str							=       torch.Size([1, 1_7_4]  )
												__magic_name__:	str							=       torch.tensor([0.26_71, -0.46_89, -0.82_35]  )
						elif model_name == "videomae-base":
												__magic_name__:	Any							=       torch.Size([1, 1_4_0_8, 1_5_3_6]  )
												__magic_name__:	Optional[Any]							=       torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]]  )
						elif model_name == "videomae-base-short":
												__magic_name__:	List[str]							=       torch.Size([1, 1_4_0_8, 1_5_3_6]  )
												__magic_name__:	int							=       torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]  )
												# we verified the loss both for normalized and unnormalized targets for this one
												__magic_name__:	Optional[int]							=       torch.tensor([0.51_42]  ) if config.norm_pix_loss else torch.tensor([0.64_69]  )
						elif model_name == "videomae-large":
												__magic_name__:	str							=       torch.Size([1, 1_4_0_8, 1_5_3_6]  )
												__magic_name__:	int							=       torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]]  )
						elif model_name == "videomae-large-finetuned-kinetics":
												__magic_name__:	str							=       torch.Size([1, 4_0_0]  )
												__magic_name__:	Union[str, Any]							=       torch.tensor([0.07_71, 0.00_11, -0.36_25]  )
						elif model_name == "videomae-huge-finetuned-kinetics":
												__magic_name__:	Any							=       torch.Size([1, 4_0_0]  )
												__magic_name__:	Tuple							=       torch.tensor([0.24_33, 0.16_32, -0.48_94]  )
						elif model_name == "videomae-base-short-finetuned-kinetics":
												__magic_name__:	List[Any]							=       torch.Size([1, 4_0_0]  )
												__magic_name__:	int							=       torch.tensor([0.65_88, 0.09_90, -0.24_93]  )
						elif model_name == "videomae-base-finetuned-kinetics":
												__magic_name__:	List[str]							=       torch.Size([1, 4_0_0]  )
												__magic_name__:	int							=       torch.tensor([0.36_69, -0.06_88, -0.24_21]  )
						elif model_name == "videomae-base-short-ssv2":
												__magic_name__:	str							=       torch.Size([1, 1_4_0_8, 1_5_3_6]  )
												__magic_name__:	str							=       torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]]  )
						elif model_name == "videomae-base-short-finetuned-ssv2":
												__magic_name__:	List[Any]							=       torch.Size([1, 1_7_4]  )
												__magic_name__:	Optional[int]							=       torch.tensor([-0.05_37, -0.15_39, -0.32_66]  )
						elif model_name == "videomae-base-ssv2":
												__magic_name__:	List[str]							=       torch.Size([1, 1_4_0_8, 1_5_3_6]  )
												__magic_name__:	Any							=       torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]]  )
						elif model_name == "videomae-base-finetuned-ssv2":
												__magic_name__:	Optional[Any]							=       torch.Size([1, 1_7_4]  )
												__magic_name__:	Any							=       torch.tensor([0.19_61, -0.83_37, -0.63_89]  )
						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]      ,   __UpperCAmelCase      ,   atol=1E-4  )
						else:
												print("""Logits:"""      ,   logits[0, :3, :3]  )
												assert torch.allclose(logits[0, :3, :3]      ,   __UpperCAmelCase      ,   atol=1E-4  )
						print("""Logits ok!"""  )

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

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


if __name__ == "__main__":
							__lowerCamelCase							=  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.'
							)

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











"""simple docstring"""





from typing import Union

import fire
import torch
from tqdm import tqdm



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str = "cpu"      ,   __UpperCAmelCase      :    Union[str, None] = None  )    ->							None:
						__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase      ,   map_location=__UpperCAmelCase  )
						for k, v in tqdm(state_dict.items()  ):
												if not isinstance(__UpperCAmelCase      ,   torch.Tensor  ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""  )
												__magic_name__:	Any							=       v.half()
						if save_path is None:  # overwrite src_path
												__magic_name__:	List[str]							=       src_path
						torch.save(__UpperCAmelCase      ,   __UpperCAmelCase  )


if __name__ == "__main__":
							fire.Fire(convert)



