






import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class 							__a							(  lowercase__ ,	lowercase__ ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Any				    =						AutoencoderKL
		UpperCAmelCase__      :				Any				    =						"""sample"""
		UpperCAmelCase__      :				str				    =						1e-2


		@property
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   4
						SCREAMING_SNAKE_CASE_   :	List[str]													=   3
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   (32, 32)

						SCREAMING_SNAKE_CASE_   :	Any													=   floats_tensor((batch_size, num_channels) + sizes       ).to(__lowercase       )

						return {"sample": image}


		@property
		def   __snake_case			(						self       ):
						return (3, 32, 32)


		@property
		def   __snake_case			(						self       ):
						return (3, 32, 32)


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   {
						    'block_out_channels': [32, 64],
						    'in_channels': 3,
						    'out_channels': 3,
						    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
						    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
						    'latent_channels': 4,
						}
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.dummy_input
						return init_dict, inputs_dict


		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						pass


		@unittest.skipIf(torch_device == 'mps' ,    'Gradient checkpointing skipped on MPS'       )
		def   __snake_case			(						self       ):
						# enable deterministic behavior for gradient checkpointing
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Tuple													=   self.prepare_init_args_and_inputs_for_common()
						SCREAMING_SNAKE_CASE_   :	str													=   self.model_class(**__lowercase       )
						model.to(__lowercase       )

						assert not model.is_gradient_checkpointing and model.training

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model(**__lowercase       ).sample
						# run the backwards pass on the model. For backwards pass, for simplicity purpose,
						# we won't calculate the loss and rather backprop on out.sum()
						model.zero_grad()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.randn_like(__lowercase       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   (out - labels).mean()
						loss.backward()

						# re-instantiate the model now enabling gradient checkpointing
						SCREAMING_SNAKE_CASE_   :	Any													=   self.model_class(**__lowercase       )
						# clone model
						model_a.load_state_dict(model.state_dict()       )
						model_a.to(__lowercase       )
						model_a.enable_gradient_checkpointing()

						assert model_a.is_gradient_checkpointing and model_a.training

						SCREAMING_SNAKE_CASE_   :	List[str]													=   model_a(**__lowercase       ).sample
						# run the backwards pass on the model. For backwards pass, for simplicity purpose,
						# we won't calculate the loss and rather backprop on out.sum()
						model_a.zero_grad()
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   (out_a - labels).mean()
						loss_a.backward()

						# compare the output and parameters gradients
						self.assertTrue((loss - loss_a).abs() < 1E-5       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   dict(model.named_parameters()       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   dict(model_a.named_parameters()       )
						for name, param in named_params.items():
										self.assertTrue(torch_all_close(param.grad.data ,    named_params_a[name].grad.data ,    atol=5E-5       )       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy' ,    output_loading_info=__lowercase       )
						self.assertIsNotNone(__lowercase       )
						self.assertEqual(len(loading_info['missing_keys']       ) ,    0       )

						model.to(__lowercase       )
						SCREAMING_SNAKE_CASE_   :	str													=   model(**self.dummy_input       )

						assert image is not None, "Make sure output is not None"






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model.to(__lowercase       )
						model.eval()

						if torch_device == "mps":
										SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.manual_seed(0       )
						else:
										SCREAMING_SNAKE_CASE_   :	Dict													=   torch.Generator(device=__lowercase       ).manual_seed(0       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.randn(
						    1 ,    model.config.in_channels ,    model.config.sample_size ,    model.config.sample_size ,    generator=torch.manual_seed(0       ) ,    )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   image.to(__lowercase       )
						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model(__lowercase ,    sample_posterior=__lowercase ,    generator=__lowercase       ).sample

						SCREAMING_SNAKE_CASE_   :	Dict													=   output[0, -1, -3:, -3:].flatten().cpu()

						# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
						# the expected output slices are not the same for CPU and GPU.
						if torch_device == "mps":
										SCREAMING_SNAKE_CASE_   :	Any													=   torch.tensor(
										    [
										        -4.0_078E-01,
										        -3.8_323E-04,
										        -1.2_681E-01,
										        -1.1_462E-01,
										        2.0_095E-01,
										        1.0_893E-01,
										        -8.8_247E-02,
										        -3.0_361E-01,
										        -9.8_644E-03,
										    ]       )
						elif torch_device == "cpu":
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.tensor(
										    [-0.13_52, 0.08_78, 0.04_19, -0.08_18, -0.10_69, 0.06_88, -0.14_58, -0.44_46, -0.00_26]       )
						else:
										SCREAMING_SNAKE_CASE_   :	Dict													=   torch.tensor(
										    [-0.24_21, 0.46_42, 0.25_07, -0.04_38, 0.06_82, 0.31_60, -0.20_18, -0.07_27, 0.24_85]       )

						self.assertTrue(torch_all_close(__lowercase ,    __lowercase ,    rtol=1E-2       )       )


@slow
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						return F'''gaussian_noise_s={seed}_shape={'_'.join([str(__lowercase       ) for s in shape]       )}.npy'''


		def   __snake_case			(						self       ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()


		def   __snake_case			(						self ,    UpperCamelCase__=0 ,    UpperCamelCase__=(4, 3, 512, 512) ,    UpperCamelCase__=False       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.floataa if fpaa else torch.floataa
						SCREAMING_SNAKE_CASE_   :	Any													=   torch.from_numpy(load_hf_numpy(self.get_file_format(__lowercase ,    __lowercase       )       )       ).to(__lowercase       ).to(__lowercase       )
						return image


		def   __snake_case			(						self ,    UpperCamelCase__="CompVis/stable-diffusion-v1-4" ,    UpperCamelCase__=False       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   'fp16' if fpaa else None
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.floataa if fpaa else torch.floataa

						SCREAMING_SNAKE_CASE_   :	str													=   AutoencoderKL.from_pretrained(
						    __lowercase ,    subfolder='vae' ,    torch_dtype=__lowercase ,    revision=__lowercase ,    )
						model.to(__lowercase       ).eval()

						return model


		def   __snake_case			(						self ,    UpperCamelCase__=0       ):
						if torch_device == "mps":
										return torch.manual_seed(__lowercase       )
						return torch.Generator(device=__lowercase       ).manual_seed(__lowercase       )


		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.16_03, 0.98_78, -0.04_95, -0.07_90, -0.27_09, 0.83_75, -0.20_60, -0.08_24], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
		        [47, [-0.23_76, 0.11_68, 0.13_32, -0.48_40, -0.25_08, -0.07_91, -0.04_93, -0.40_89], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
		        # fmt: on
		    ]       )
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.get_sd_image(__lowercase       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_generator(__lowercase       )

						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model(__lowercase ,    generator=__lowercase ,    sample_posterior=__lowercase       ).sample

						assert sample.shape == image.shape

						SCREAMING_SNAKE_CASE_   :	int													=   sample[-1, -2:, -2:, :2].flatten().float().cpu()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice       )

						assert torch_all_close(__lowercase ,    __lowercase ,    atol=3E-3       )


		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.05_13, 0.02_89, 1.37_99, 0.21_66, -0.25_73, -0.08_71, 0.51_03, -0.09_99]],
		        [47, [-0.41_28, -0.13_20, -0.37_04, 0.19_65, -0.41_16, -0.23_32, -0.33_40, 0.22_47]],
		        # fmt: on
		    ]       )
		@require_torch_gpu
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.get_sd_vae_model(fpaa=__lowercase       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.get_sd_image(__lowercase ,    fpaa=__lowercase       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.get_generator(__lowercase       )

						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   model(__lowercase ,    generator=__lowercase ,    sample_posterior=__lowercase       ).sample

						assert sample.shape == image.shape

						SCREAMING_SNAKE_CASE_   :	Any													=   sample[-1, -2:, :2, -2:].flatten().float().cpu()
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.tensor(__lowercase       )

						assert torch_all_close(__lowercase ,    __lowercase ,    atol=1E-2       )


		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.16_09, 0.98_66, -0.04_87, -0.07_77, -0.27_16, 0.83_68, -0.20_55, -0.08_14], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]],
		        [47, [-0.23_77, 0.11_47, 0.13_33, -0.48_41, -0.25_06, -0.08_05, -0.04_91, -0.40_85], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]],
		        # fmt: on
		    ]       )
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_sd_image(__lowercase       )

						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   model(__lowercase       ).sample

						assert sample.shape == image.shape

						SCREAMING_SNAKE_CASE_   :	List[str]													=   sample[-1, -2:, -2:, :2].flatten().float().cpu()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice       )

						assert torch_all_close(__lowercase ,    __lowercase ,    atol=3E-3       )


		@parameterized.expand(
		    [
		        # fmt: off
		        [13, [-0.20_51, -0.18_03, -0.23_11, -0.21_14, -0.32_92, -0.35_74, -0.29_53, -0.33_23]],
		        [37, [-0.26_32, -0.26_25, -0.21_99, -0.27_41, -0.45_39, -0.49_90, -0.37_20, -0.49_25]],
		        # fmt: on
		    ]       )
		@require_torch_gpu
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_sd_image(__lowercase ,    shape=(3, 4, 64, 64)       )

						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   model.decode(__lowercase       ).sample

						assert list(sample.shape       ) == [3, 3, 512, 512]

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   sample[-1, -2:, :2, -2:].flatten().cpu()
						SCREAMING_SNAKE_CASE_   :	int													=   torch.tensor(__lowercase       )

						assert torch_all_close(__lowercase ,    __lowercase ,    atol=1E-3       )


		@parameterized.expand(
		    [
		        # fmt: off
		        [27, [-0.03_69, 0.02_07, -0.07_76, -0.06_82, -0.17_47, -0.19_30, -0.14_65, -0.20_39]],
		        [16, [-0.16_28, -0.21_34, -0.27_47, -0.26_42, -0.37_74, -0.44_04, -0.36_87, -0.42_77]],
		        # fmt: on
		    ]       )
		@require_torch_gpu
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_sd_vae_model(fpaa=__lowercase       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.get_sd_image(__lowercase ,    shape=(3, 4, 64, 64) ,    fpaa=__lowercase       )

						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   model.decode(__lowercase       ).sample

						assert list(sample.shape       ) == [3, 3, 512, 512]

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   sample[-1, -2:, :2, -2:].flatten().float().cpu()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.tensor(__lowercase       )

						assert torch_all_close(__lowercase ,    __lowercase ,    atol=5E-3       )


		@parameterized.expand([(13,), (16,), (27,)]       )
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available() ,    reason='xformers is not required when using PyTorch 2.0.'       )
		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.get_sd_vae_model(fpaa=__lowercase       )
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_sd_image(__lowercase ,    shape=(3, 4, 64, 64) ,    fpaa=__lowercase       )

						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	Tuple													=   model.decode(__lowercase       ).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	Tuple													=   model.decode(__lowercase       ).sample

						assert list(sample.shape       ) == [3, 3, 512, 512]

						assert torch_all_close(__lowercase ,    __lowercase ,    atol=1E-1       )


		@parameterized.expand([(13,), (16,), (37,)]       )
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available() ,    reason='xformers is not required when using PyTorch 2.0.'       )
		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_sd_image(__lowercase ,    shape=(3, 4, 64, 64)       )

						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	List[str]													=   model.decode(__lowercase       ).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model.decode(__lowercase       ).sample

						assert list(sample.shape       ) == [3, 3, 512, 512]

						assert torch_all_close(__lowercase ,    __lowercase ,    atol=1E-2       )






		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.30_01, 0.09_18, -2.69_84, -3.97_20, -3.20_99, -5.03_53, 1.73_38, -0.20_65, 3.42_67]],
		        [47, [-1.50_30, -4.38_71, -6.03_55, -9.11_57, -1.66_61, -2.78_53, 2.16_07, -5.08_23, 2.56_33]],
		        # fmt: on
		    ]       )
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.get_sd_vae_model()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_sd_image(__lowercase       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_generator(__lowercase       )

						with torch.no_grad():
										SCREAMING_SNAKE_CASE_   :	List[str]													=   model.encode(__lowercase       ).latent_dist
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   dist.sample(generator=__lowercase       )

						assert list(sample.shape       ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   sample[0, -1, -3:, -3:].flatten().cpu()
						SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.tensor(__lowercase       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   3E-3 if torch_device != 'mps' else 1E-2
						assert torch_all_close(__lowercase ,    __lowercase ,    atol=__lowercase       )







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







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

				return name



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE_   :	Tuple													=   orig_state_dict.pop(lowerCAmelCase__      )

								if "qkv" in key:
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   key.split('.'      )
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   int(key_split[1]      )
												if "decoder_blocks" in key:
																SCREAMING_SNAKE_CASE_   :	Any													=   config.decoder_hidden_size
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   'decoder.decoder_layers.'
																if "weight" in key:
																				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   val[:dim, :]
																				SCREAMING_SNAKE_CASE_   :	List[str]													=   val[dim : dim * 2, :]
																				SCREAMING_SNAKE_CASE_   :	Any													=   val[-dim:, :]
																elif "bias" in key:
																				SCREAMING_SNAKE_CASE_   :	str													=   val[:dim]
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   val[-dim:]
												else:
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   config.hidden_size
																SCREAMING_SNAKE_CASE_   :	str													=   'vit.encoder.layer.'
																if "weight" in key:
																				SCREAMING_SNAKE_CASE_   :	Any													=   val[:dim, :]
																				SCREAMING_SNAKE_CASE_   :	Tuple													=   val[dim : dim * 2, :]
																				SCREAMING_SNAKE_CASE_   :	int													=   val[-dim:, :]
																elif "bias" in key:
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   val[:dim]
																				SCREAMING_SNAKE_CASE_   :	Dict													=   val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE_   :	str													=   val[-dim:]

								else:
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   val

				return orig_state_dict



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   ViTMAEConfig()
				if "large" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	Any													=   1024
								SCREAMING_SNAKE_CASE_   :	Any													=   4096
								SCREAMING_SNAKE_CASE_   :	List[str]													=   24
								SCREAMING_SNAKE_CASE_   :	List[str]													=   16
				elif "huge" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	str													=   14
								SCREAMING_SNAKE_CASE_   :	List[str]													=   1280
								SCREAMING_SNAKE_CASE_   :	int													=   5120
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   32
								SCREAMING_SNAKE_CASE_   :	List[str]													=   16

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ViTMAEForPreTraining(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.hub.load_state_dict_from_url(lowerCAmelCase__    ,    map_location='cpu'      )['model']

				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ViTMAEImageProcessor(size=config.image_size      )

				SCREAMING_SNAKE_CASE_   :	int													=   convert_state_dict(lowerCAmelCase__    ,    lowerCAmelCase__      )

				model.load_state_dict(lowerCAmelCase__      )
				model.eval()

				SCREAMING_SNAKE_CASE_   :	Any													=   'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

				SCREAMING_SNAKE_CASE_   :	Dict													=   Image.open(requests.get(lowerCAmelCase__    ,    stream=lowerCAmelCase__      ).raw      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ViTMAEImageProcessor(size=config.image_size      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_processor(images=lowerCAmelCase__    ,    return_tensors='pt'      )

				# forward pass
				torch.manual_seed(2      )
				SCREAMING_SNAKE_CASE_   :	Any													=   model(**lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   outputs.logits

				if "large" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	Any													=   torch.tensor(
								    [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]      )
				elif "huge" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	Dict													=   torch.tensor(
								    [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]      )
				else:
								SCREAMING_SNAKE_CASE_   :	int													=   torch.tensor(
								    [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]      )

				# verify logits
				assert torch.allclose(logits[0, :3, :3]    ,    lowerCAmelCase__    ,    atol=1E-4      )

				print(F'''Saving model to {pytorch_dump_folder_path}'''      )
				model.save_pretrained(lowerCAmelCase__      )

				print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
				image_processor.save_pretrained(lowerCAmelCase__      )


if __name__ == "__main__":
				A        =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--checkpoint_url',
				    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
				    type=str,
				    help='URL of the checkpoint you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)

				A        =			parser.parse_args()
				convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)