






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     (	a_):
							snake_case_           =				VideoMAEConfig()

							set_architecture_configs(a_				,	a_)

							if "finetuned" not in model_name:
														snake_case_           =				False

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

							return config





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





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

							return name





def 		__UpperCAmelCase     (	a_				,	a_):
							for key in orig_state_dict.copy().keys():
														snake_case_           =				orig_state_dict.pop(a_)

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

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

							return orig_state_dict





def 		__UpperCAmelCase     (	):
							snake_case_           =				hf_hub_download(
							    repo_id='hf-internal-testing/spaghetti-video'				,	filename='eating_spaghetti.npy'				,	repo_type='dataset')
							snake_case_           =				np.load(a_)
							return list(a_)





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							snake_case_           =				get_videomae_config(a_)

							if "finetuned" in model_name:
														snake_case_           =				VideoMAEForVideoClassification(a_)
							else:
														snake_case_           =				VideoMAEForPreTraining(a_)

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

							model.load_state_dict(a_)
							model.eval()

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

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

							snake_case_           =				model(**a_)
							snake_case_           =				outputs.logits

							snake_case_           =				[
							    '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":
														snake_case_           =				torch.Size([1, 4_00])
														snake_case_           =				torch.tensor([-0.92_91, -0.40_61, -0.93_07])
							elif model_name == "videomae-small-finetuned-ssv2":
														snake_case_           =				torch.Size([1, 1_74])
														snake_case_           =				torch.tensor([0.26_71, -0.46_89, -0.82_35])
							elif model_name == "videomae-base":
														snake_case_           =				torch.Size([1, 14_08, 15_36])
														snake_case_           =				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":
														snake_case_           =				torch.Size([1, 14_08, 15_36])
														snake_case_           =				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
														snake_case_           =				torch.tensor([0.51_42]) if config.norm_pix_loss else torch.tensor([0.64_69])
							elif model_name == "videomae-large":
														snake_case_           =				torch.Size([1, 14_08, 15_36])
														snake_case_           =				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":
														snake_case_           =				torch.Size([1, 4_00])
														snake_case_           =				torch.tensor([0.07_71, 0.00_11, -0.36_25])
							elif model_name == "videomae-huge-finetuned-kinetics":
														snake_case_           =				torch.Size([1, 4_00])
														snake_case_           =				torch.tensor([0.24_33, 0.16_32, -0.48_94])
							elif model_name == "videomae-base-short-finetuned-kinetics":
														snake_case_           =				torch.Size([1, 4_00])
														snake_case_           =				torch.tensor([0.65_88, 0.09_90, -0.24_93])
							elif model_name == "videomae-base-finetuned-kinetics":
														snake_case_           =				torch.Size([1, 4_00])
														snake_case_           =				torch.tensor([0.36_69, -0.06_88, -0.24_21])
							elif model_name == "videomae-base-short-ssv2":
														snake_case_           =				torch.Size([1, 14_08, 15_36])
														snake_case_           =				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":
														snake_case_           =				torch.Size([1, 1_74])
														snake_case_           =				torch.tensor([-0.05_37, -0.15_39, -0.32_66])
							elif model_name == "videomae-base-ssv2":
														snake_case_           =				torch.Size([1, 14_08, 15_36])
														snake_case_           =				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":
														snake_case_           =				torch.Size([1, 1_74])
														snake_case_           =				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]				,	a_				,	atol=1E-4)
							else:
														print('Logits:'				,	logits[0, :3, :3])
														assert torch.allclose(logits[0, :3, :3]				,	a_				,	atol=1E-4)
							print('Logits ok!')

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

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


if __name__ == "__main__":
	lowercase    				=   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."
	)

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













from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_           =				DDIMScheduler.from_config(scheduler.config   )

								self.register_modules(unet=a			,     scheduler=a   )






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(a			,     a   ) and len(a   ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a   )}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

								# set step values
								self.scheduler.set_timesteps(a   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

								snake_case_           =				(image / 2 + 0.5).clamp(0			,     1   )
								snake_case_           =				image.cpu().permute(0			,     2			,     3			,     1   ).numpy()
								if output_type == "pil":
															snake_case_           =				self.numpy_to_pil(a   )

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





