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        __UpperCAmelCase    (       UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			lowercase							=		VideoMAEConfig()

			set_architecture_configs(UpperCAmelCase,		UpperCAmelCase       )

			if "finetuned" not in model_name:
						lowercase							=		False

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

			return config






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			if "small" in model_name:
						lowercase							=		384
						lowercase							=		1536
						lowercase							=		12
						lowercase							=		16
						lowercase							=		12
						lowercase							=		3
						lowercase							=		192
						lowercase							=		768
			elif "large" in model_name:
						lowercase							=		1024
						lowercase							=		4096
						lowercase							=		24
						lowercase							=		16
						lowercase							=		12
						lowercase							=		8
						lowercase							=		512
						lowercase							=		2048
			elif "huge" in model_name:
						lowercase							=		1280
						lowercase							=		5120
						lowercase							=		32
						lowercase							=		16
						lowercase							=		12
						lowercase							=		8
						lowercase							=		640
						lowercase							=		2560
			elif "base" not in model_name:
						raise ValueError('''Model name should include either "small", "base", "large", or "huge"'''       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







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

			return name






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			for key in orig_state_dict.copy().keys():
						lowercase							=		orig_state_dict.pop(UpperCAmelCase       )

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

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

			return orig_state_dict






def        __UpperCAmelCase    (       )->       List[str]:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			lowercase							=		get_videomae_config(UpperCAmelCase       )

			if "finetuned" in model_name:
						lowercase							=		VideoMAEForVideoClassification(UpperCAmelCase       )
			else:
						lowercase							=		VideoMAEForPreTraining(UpperCAmelCase       )

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

			model.load_state_dict(UpperCAmelCase       )
			model.eval()

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

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

			lowercase							=		model(**UpperCAmelCase       )
			lowercase							=		outputs.logits

			lowercase							=		[
			    '''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							=		torch.Size([1, 400]       )
						lowercase							=		torch.tensor([-0.9291, -0.4061, -0.9307]       )
			elif model_name == "videomae-small-finetuned-ssv2":
						lowercase							=		torch.Size([1, 174]       )
						lowercase							=		torch.tensor([0.2671, -0.4689, -0.8235]       )
			elif model_name == "videomae-base":
						lowercase							=		torch.Size([1, 1408, 1536]       )
						lowercase							=		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":
						lowercase							=		torch.Size([1, 1408, 1536]       )
						lowercase							=		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
						lowercase							=		torch.tensor([0.5142]       ) if config.norm_pix_loss else torch.tensor([0.6469]       )
			elif model_name == "videomae-large":
						lowercase							=		torch.Size([1, 1408, 1536]       )
						lowercase							=		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":
						lowercase							=		torch.Size([1, 400]       )
						lowercase							=		torch.tensor([0.0771, 0.0011, -0.3625]       )
			elif model_name == "videomae-huge-finetuned-kinetics":
						lowercase							=		torch.Size([1, 400]       )
						lowercase							=		torch.tensor([0.2433, 0.1632, -0.4894]       )
			elif model_name == "videomae-base-short-finetuned-kinetics":
						lowercase							=		torch.Size([1, 400]       )
						lowercase							=		torch.tensor([0.6588, 0.0990, -0.2493]       )
			elif model_name == "videomae-base-finetuned-kinetics":
						lowercase							=		torch.Size([1, 400]       )
						lowercase							=		torch.tensor([0.3669, -0.0688, -0.2421]       )
			elif model_name == "videomae-base-short-ssv2":
						lowercase							=		torch.Size([1, 1408, 1536]       )
						lowercase							=		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":
						lowercase							=		torch.Size([1, 174]       )
						lowercase							=		torch.tensor([-0.0537, -0.1539, -0.3266]       )
			elif model_name == "videomae-base-ssv2":
						lowercase							=		torch.Size([1, 1408, 1536]       )
						lowercase							=		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":
						lowercase							=		torch.Size([1, 174]       )
						lowercase							=		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],		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":
						lowercase							=		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__":
						A_     :   Any      =      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."
						)

						A_     :   List[Any]      =      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

from math import pi






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       dict[str, float]:



			"""simple docstring"""







			if (inductance, frequency, reactance).count(0       ) != 1:
						raise ValueError('''One and only one argument must be 0'''       )
			if inductance < 0:
						raise ValueError('''Inductance cannot be negative'''       )
			if frequency < 0:
						raise ValueError('''Frequency cannot be negative'''       )
			if reactance < 0:
						raise ValueError('''Inductive reactance cannot be negative'''       )
			if inductance == 0:
						return {"inductance": reactance / (2 * pi * frequency)}
			elif frequency == 0:
						return {"frequency": reactance / (2 * pi * inductance)}
			elif reactance == 0:
						return {"reactance": 2 * pi * frequency * inductance}
			else:
						raise ValueError('''Exactly one argument must be 0'''       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





