

'''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	(       __snake_case					:		Union[str, Any]	)	->							Tuple:






						"""simple docstring"""





						A__					:       Optional[int]     =VideoMAEConfig()

						set_architecture_configs(__snake_case,		__snake_case	)

						if "finetuned" not in model_name:
												A__					:       List[Any]     =False

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

						return config
def 						__lowerCamelCase	(       __snake_case					:		int,		__snake_case					:		int	)	->							Optional[int]:






						"""simple docstring"""





						if "small" in model_name:
												A__					:       Union[str, Any]     =384
												A__					:       str     =1_536
												A__					:       Optional[Any]     =12
												A__					:       List[Any]     =16
												A__					:       Dict     =12
												A__					:       int     =3
												A__					:       Tuple     =192
												A__					:       Union[str, Any]     =768
						elif "large" in model_name:
												A__					:       Union[str, Any]     =1_024
												A__					:       Tuple     =4_096
												A__					:       Optional[Any]     =24
												A__					:       Any     =16
												A__					:       Optional[Any]     =12
												A__					:       Union[str, Any]     =8
												A__					:       Any     =512
												A__					:       str     =2_048
						elif "huge" in model_name:
												A__					:       Union[str, Any]     =1_280
												A__					:       int     =5_120
												A__					:       List[str]     =32
												A__					:       str     =16
												A__					:       Dict     =12
												A__					:       Dict     =8
												A__					:       str     =640
												A__					:       Tuple     =2_560
						elif "base" not in model_name:
												raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""	)
def 						__lowerCamelCase	(       __snake_case					:		str	)	->							Optional[Any]:






						"""simple docstring"""





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

						return name
def 						__lowerCamelCase	(       __snake_case					:		Tuple,		__snake_case					:		Optional[int]	)	->							Tuple:






						"""simple docstring"""





						for key in orig_state_dict.copy().keys():
												A__					:       Optional[Any]     =orig_state_dict.pop(__snake_case	)

												if key.startswith("""encoder."""	):
																		A__					:       Dict     =key.replace("""encoder.""",		""""""	)

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

						return orig_state_dict
def 						__lowerCamelCase	(       )	->							Dict:






						"""simple docstring"""





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






						"""simple docstring"""





						A__					:       Tuple     =get_videomae_config(__snake_case	)

						if "finetuned" in model_name:
												A__					:       Optional[Any]     =VideoMAEForVideoClassification(__snake_case	)
						else:
												A__					:       Optional[Any]     =VideoMAEForPreTraining(__snake_case	)

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

						model.load_state_dict(__snake_case	)
						model.eval()

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

						if "finetuned" not in model_name:
												A__					:       Any     =hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""",		filename="""bool_masked_pos.pt"""	)
												A__					:       str     =torch.load(__snake_case	)

						A__					:       int     =model(**__snake_case	)
						A__					:       List[str]     =outputs.logits

						A__					:       Union[str, Any]     =[
						    """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":
												A__					:       Any     =torch.Size([1, 400]	)
												A__					:       List[Any]     =torch.tensor([-0.92_91, -0.40_61, -0.93_07]	)
						elif model_name == "videomae-small-finetuned-ssv2":
												A__					:       str     =torch.Size([1, 174]	)
												A__					:       Optional[Any]     =torch.tensor([0.26_71, -0.46_89, -0.82_35]	)
						elif model_name == "videomae-base":
												A__					:       Optional[int]     =torch.Size([1, 1_408, 1_536]	)
												A__					:       Optional[int]     =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":
												A__					:       str     =torch.Size([1, 1_408, 1_536]	)
												A__					:       Tuple     =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
												A__					:       List[Any]     =torch.tensor([0.51_42]	) if config.norm_pix_loss else torch.tensor([0.64_69]	)
						elif model_name == "videomae-large":
												A__					:       Optional[Any]     =torch.Size([1, 1_408, 1_536]	)
												A__					:       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":
												A__					:       int     =torch.Size([1, 400]	)
												A__					:       str     =torch.tensor([0.07_71, 0.00_11, -0.36_25]	)
						elif model_name == "videomae-huge-finetuned-kinetics":
												A__					:       Any     =torch.Size([1, 400]	)
												A__					:       Tuple     =torch.tensor([0.24_33, 0.16_32, -0.48_94]	)
						elif model_name == "videomae-base-short-finetuned-kinetics":
												A__					:       Union[str, Any]     =torch.Size([1, 400]	)
												A__					:       List[Any]     =torch.tensor([0.65_88, 0.09_90, -0.24_93]	)
						elif model_name == "videomae-base-finetuned-kinetics":
												A__					:       List[str]     =torch.Size([1, 400]	)
												A__					:       List[str]     =torch.tensor([0.36_69, -0.06_88, -0.24_21]	)
						elif model_name == "videomae-base-short-ssv2":
												A__					:       Dict     =torch.Size([1, 1_408, 1_536]	)
												A__					:       Any     =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":
												A__					:       Dict     =torch.Size([1, 174]	)
												A__					:       Dict     =torch.tensor([-0.05_37, -0.15_39, -0.32_66]	)
						elif model_name == "videomae-base-ssv2":
												A__					:       str     =torch.Size([1, 1_408, 1_536]	)
												A__					:       List[str]     =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":
												A__					:       Any     =torch.Size([1, 174]	)
												A__					:       str     =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],		__snake_case,		atol=1E-4	)
						else:
												print("""Logits:""",		logits[0, :3, :3]	)
												assert torch.allclose(logits[0, :3, :3],		__snake_case,		atol=1E-4	)
						print("""Logits ok!"""	)

						# verify loss, if applicable
						if model_name == "videomae-base-short":
												A__					:       Optional[Any]     =outputs.loss
												assert torch.allclose(__snake_case,		__snake_case,		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(__snake_case	)
												model.save_pretrained(__snake_case	)

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


if __name__ == "__main__":
		__snake_case			:     Optional[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.'
		)

		__snake_case			:     Tuple										=      parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''simple docstring'''



from __future__ import annotations

from typing import Any



class lowerCamelCase			:



			'''simple docstring'''






			def __init__(				self	:						List[str]		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''
									A__					:       Any     =num_of_nodes
									A__					:       list[list[int]]     =[]
									A__					:       dict[int, int]     ={}


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''
									self.m_edges.append([u_node, v_node, weight]			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						int			)      ->		int:





									'''simple docstring'''

									if self.m_component[u_node] == u_node:
															return u_node
									return self.find_component(self.m_component[u_node]			)


			def lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''

									if self.m_component[u_node] != u_node:
															for k in self.m_component:
																					A__					:       str     =self.find_component(lowerCAmelCase_			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						list[int]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''

									if component_size[u_node] <= component_size[v_node]:
															A__					:       int     =v_node
															component_size[v_node] += component_size[u_node]
															self.set_component(lowerCAmelCase_			)

									elif component_size[u_node] >= component_size[v_node]:
															A__					:       List[str]     =self.find_component(lowerCAmelCase_			)
															component_size[u_node] += component_size[v_node]
															self.set_component(lowerCAmelCase_			)



			def lowercase__						(				self	:						str			)      ->		None:





									'''simple docstring'''
									A__					:       Union[str, Any]     =[]
									A__					:       List[str]     =0

									A__					:       list[Any]     =[-1] * self.m_num_of_nodes

									# A list of components (initialized to all of the nodes)
									for node in range(self.m_num_of_nodes			):
															self.m_component.update({node: node}			)
															component_size.append(1			)

									A__					:       List[str]     =self.m_num_of_nodes

									while num_of_components > 1:
															for edge in self.m_edges:
																					A__   ,						A__   ,						A__					:       Any     =edge

																					A__					:       Tuple     =self.m_component[u]
																					A__					:       Optional[Any]     =self.m_component[v]

																					if u_component != v_component:

																											for component in (u_component, v_component):
																																	if (
																																	    minimum_weight_edge[component] == -1
																																	    or minimum_weight_edge[component][2] > w
																																	):
																																							A__					:       Optional[int]     =[u, v, w]

															for edge in minimum_weight_edge:
																					if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			):
																											A__   ,						A__   ,						A__					:       Tuple     =edge

																											A__					:       Any     =self.m_component[u]
																											A__					:       Optional[Any]     =self.m_component[v]

																											if u_component != v_component:
																																	mst_weight += w
																																	self.union(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			)
																																	print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"			)
																																	num_of_components -= 1

															A__					:       int     =[-1] * self.m_num_of_nodes
									print(f"The total weight of the minimal spanning tree is: {mst_weight}"			)


def 						__lowerCamelCase	(       )	->							None:






						"""simple docstring"""







if __name__ == "__main__":
		import doctest

		doctest.testmod()

