
'''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    UpperCAmelCase							(  UpperCAmelCase__							:							int):
				lowerCamelCase					:			str									= VideoMAEConfig()

				set_architecture_configs(UpperCAmelCase__       ,							UpperCAmelCase__)

				if "finetuned" not in model_name:
								lowerCamelCase					:			int									= False

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

				return config





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							Tuple):
				if "small" in model_name:
								lowerCamelCase					:			List[Any]									= 3_84
								lowerCamelCase					:			Dict									= 15_36
								lowerCamelCase					:			Tuple									= 12
								lowerCamelCase					:			Any									= 16
								lowerCamelCase					:			Optional[Any]									= 12
								lowerCamelCase					:			Tuple									= 3
								lowerCamelCase					:			Dict									= 1_92
								lowerCamelCase					:			Optional[int]									= 7_68
				elif "large" in model_name:
								lowerCamelCase					:			int									= 10_24
								lowerCamelCase					:			List[str]									= 40_96
								lowerCamelCase					:			List[str]									= 24
								lowerCamelCase					:			Dict									= 16
								lowerCamelCase					:			Union[str, Any]									= 12
								lowerCamelCase					:			Tuple									= 8
								lowerCamelCase					:			List[str]									= 5_12
								lowerCamelCase					:			Tuple									= 20_48
				elif "huge" in model_name:
								lowerCamelCase					:			Dict									= 12_80
								lowerCamelCase					:			List[Any]									= 51_20
								lowerCamelCase					:			Any									= 32
								lowerCamelCase					:			Any									= 16
								lowerCamelCase					:			Union[str, Any]									= 12
								lowerCamelCase					:			Dict									= 8
								lowerCamelCase					:			int									= 6_40
								lowerCamelCase					:			int									= 25_60
				elif "base" not in model_name:
								raise ValueError('Model name should include either "small", "base", "large", or "huge"')





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

				return name





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							Optional[int]):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase					:			Union[str, Any]									= orig_state_dict.pop(UpperCAmelCase__)

								if key.startswith('encoder.'):
												lowerCamelCase					:			List[Any]									= key.replace('encoder.'       ,							'')

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

				return orig_state_dict





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			Union[str, Any]									= hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video'       ,							filename='eating_spaghetti.npy'       ,							repo_type='dataset')
				lowerCamelCase					:			Union[str, Any]									= np.load(UpperCAmelCase__)
				return list(UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple       ,							UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Any):
				lowerCamelCase					:			Optional[int]									= get_videomae_config(UpperCAmelCase__)

				if "finetuned" in model_name:
								lowerCamelCase					:			int									= VideoMAEForVideoClassification(UpperCAmelCase__)
				else:
								lowerCamelCase					:			List[str]									= VideoMAEForPreTraining(UpperCAmelCase__)

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

				model.load_state_dict(UpperCAmelCase__)
				model.eval()

				# verify model on basic input
				lowerCamelCase					:			int									= VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]       ,							image_std=[0.5, 0.5, 0.5])
				lowerCamelCase					:			Dict									= prepare_video()
				lowerCamelCase					:			Any									= image_processor(UpperCAmelCase__       ,							return_tensors='pt')

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

				lowerCamelCase					:			Tuple									= model(**UpperCAmelCase__)
				lowerCamelCase					:			Any									= outputs.logits

				lowerCamelCase					:			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":
								lowerCamelCase					:			int									= torch.Size([1, 4_00])
								lowerCamelCase					:			Dict									= torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7])
				elif model_name == "videomae-small-finetuned-ssv2":
								lowerCamelCase					:			Tuple									= torch.Size([1, 1_74])
								lowerCamelCase					:			Any									= torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5])
				elif model_name == "videomae-base":
								lowerCamelCase					:			Any									= torch.Size([1, 14_08, 15_36])
								lowerCamelCase					:			List[str]									= 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":
								lowerCamelCase					:			int									= torch.Size([1, 14_08, 15_36])
								lowerCamelCase					:			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
								lowerCamelCase					:			int									= 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":
								lowerCamelCase					:			Any									= torch.Size([1, 14_08, 15_36])
								lowerCamelCase					:			Dict									= 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":
								lowerCamelCase					:			List[str]									= torch.Size([1, 4_00])
								lowerCamelCase					:			Optional[int]									= torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5])
				elif model_name == "videomae-huge-finetuned-kinetics":
								lowerCamelCase					:			List[str]									= torch.Size([1, 4_00])
								lowerCamelCase					:			int									= 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":
								lowerCamelCase					:			Dict									= torch.Size([1, 4_00])
								lowerCamelCase					:			Optional[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":
								lowerCamelCase					:			Any									= torch.Size([1, 4_00])
								lowerCamelCase					:			Any									= torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1])
				elif model_name == "videomae-base-short-ssv2":
								lowerCamelCase					:			Optional[Any]									= torch.Size([1, 14_08, 15_36])
								lowerCamelCase					:			List[str]									= 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":
								lowerCamelCase					:			Optional[int]									= torch.Size([1, 1_74])
								lowerCamelCase					:			str									= torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6])
				elif model_name == "videomae-base-ssv2":
								lowerCamelCase					:			Any									= torch.Size([1, 14_08, 15_36])
								lowerCamelCase					:			Union[str, Any]									= 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":
								lowerCamelCase					:			Optional[Any]									= torch.Size([1, 1_74])
								lowerCamelCase					:			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]       ,							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":
								lowerCamelCase					:			Any									= 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			      =      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			      =      parser.parse_args()
					convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)


'''simple docstring'''






A			      =      {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A			      =      ['a', 'b', 'c', 'd', 'e']





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			List[Any]									= start
				# add current to visited
				visited.append(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= edges[current]
				for neighbor in neighbors:
								# if neighbor not in visited, visit
								if neighbor not in visited:
												lowerCamelCase					:			Any									= topological_sort(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
    # if all neighbors visited add current to sort
				sort.append(UpperCAmelCase__)
				# if all vertices haven't been visited select a new one to visit
				if len(UpperCAmelCase__) != len(UpperCAmelCase__):
								for vertice in vertices:
												if vertice not in visited:
																lowerCamelCase					:			List[str]									= topological_sort(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
    # return sort
				return sort


if __name__ == "__main__":
					A			      =      topological_sort('a', [], [])
					print(sort)
