
"""simple docstring"""


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase						,	unittest.TestCase			):
				a__:      Optional[int]          =    StableDiffusionDiffEditPipeline
				a__:      Any          =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
				a__:      Optional[int]          =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
				a__:      str          =    frozenset(
				    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				a__:      Dict          =    frozenset([]			)




				def        UpperCAmelCase__						(				self   ):
							torch.manual_seed(0   )
							lowerCamelCase_					=  UNetaDConditionModel(
							    block_out_channels=(32, 64)							,						layers_per_block=2							,						sample_size=32							,						in_channels=4							,						out_channels=4							,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,						cross_attention_dim=32							,						attention_head_dim=(2, 4)							,						use_linear_projection=UpperCAmelCase							,						)
							lowerCamelCase_					=  DDIMScheduler(
							    beta_start=0.0_0_0_8_5							,						beta_end=0.0_1_2							,						beta_schedule='''scaled_linear'''							,						clip_sample=UpperCAmelCase							,						set_alpha_to_one=UpperCAmelCase							,						)
							lowerCamelCase_					=  DDIMInverseScheduler(
							    beta_start=0.0_0_0_8_5							,						beta_end=0.0_1_2							,						beta_schedule='''scaled_linear'''							,						clip_sample=UpperCAmelCase							,						set_alpha_to_zero=UpperCAmelCase							,						)
							torch.manual_seed(0   )
							lowerCamelCase_					=  AutoencoderKL(
							    block_out_channels=[32, 64]							,						in_channels=3							,						out_channels=3							,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,						latent_channels=4							,						sample_size=128							,						)
							torch.manual_seed(0   )
							lowerCamelCase_					=  CLIPTextConfig(
							    bos_token_id=0							,						eos_token_id=2							,						hidden_size=32							,						intermediate_size=37							,						layer_norm_eps=1e-0_5							,						num_attention_heads=4							,						num_hidden_layers=5							,						pad_token_id=1							,						vocab_size=1000							,						hidden_act='''gelu'''							,						projection_dim=512							,						)
							lowerCamelCase_					=  CLIPTextModel(UpperCAmelCase   )
							lowerCamelCase_					=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

							lowerCamelCase_					=  {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''inverse_scheduler''': inverse_scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}

							return components




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  floats_tensor((1, 16, 16)							,						rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  floats_tensor((1, 2, 4, 16, 16)							,						rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
							if str(UpperCAmelCase   ).startswith('''mps'''   ):
										lowerCamelCase_					=  torch.manual_seed(UpperCAmelCase   )
							else:
										lowerCamelCase_					=  torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''prompt''': '''a dog and a newt''',
							    '''mask_image''': mask,
							    '''image_latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''inpaint_strength''': 1.0,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}

							return inputs




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  floats_tensor((1, 3, 32, 32)							,						rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  image.cpu().permute(0							,						2							,						3							,						1   )[0]
							lowerCamelCase_					=  Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('''RGB'''   )
							if str(UpperCAmelCase   ).startswith('''mps'''   ):
										lowerCamelCase_					=  torch.manual_seed(UpperCAmelCase   )
							else:
										lowerCamelCase_					=  torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''image''': image,
							    '''source_prompt''': '''a cat and a frog''',
							    '''target_prompt''': '''a dog and a newt''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''num_maps_per_mask''': 2,
							    '''mask_encode_strength''': 1.0,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}

							return inputs




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  floats_tensor((1, 3, 32, 32)							,						rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  image.cpu().permute(0							,						2							,						3							,						1   )[0]
							lowerCamelCase_					=  Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('''RGB'''   )
							if str(UpperCAmelCase   ).startswith('''mps'''   ):
										lowerCamelCase_					=  torch.manual_seed(UpperCAmelCase   )
							else:
										lowerCamelCase_					=  torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''image''': image,
							    '''prompt''': '''a cat and a frog''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''inpaint_strength''': 1.0,
							    '''guidance_scale''': 6.0,
							    '''decode_latents''': True,
							    '''output_type''': '''numpy''',
							}
							return inputs




				def        UpperCAmelCase__						(				self   ):
							if not hasattr(self.pipeline_class							,						'''_optional_components'''   ):
										return

							lowerCamelCase_					=  self.get_dummy_components()
							lowerCamelCase_					=  self.pipeline_class(**UpperCAmelCase   )
							pipe.to(UpperCAmelCase   )
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							# set all optional components to None and update pipeline config accordingly
							for optional_component in pipe._optional_components:
										setattr(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}   )

							lowerCamelCase_					=  self.get_dummy_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe(**UpperCAmelCase   )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(UpperCAmelCase   )
										lowerCamelCase_					=  self.pipeline_class.from_pretrained(UpperCAmelCase   )
										pipe_loaded.to(UpperCAmelCase   )
										pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

							for optional_component in pipe._optional_components:
										self.assertTrue(
										    getattr(UpperCAmelCase							,						UpperCAmelCase   ) is None							,						f"`{optional_component}` did not stay set to None after loading."							,						)

							lowerCamelCase_					=  self.get_dummy_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe_loaded(**UpperCAmelCase   )[0]

							lowerCamelCase_					=  np.abs(output - output_loaded   ).max()
							self.assertLess(UpperCAmelCase							,						1e-4   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''cpu'''

							lowerCamelCase_					=  self.get_dummy_components()
							lowerCamelCase_					=  self.pipeline_class(**UpperCAmelCase   )
							pipe.to(UpperCAmelCase   )
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  self.get_dummy_mask_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe.generate_mask(**UpperCAmelCase   )
							lowerCamelCase_					=  mask[0, -3:, -3:]

							self.assertEqual(mask.shape							,						(1, 16, 16)   )
							lowerCamelCase_					=  np.array([0] * 9   )
							lowerCamelCase_					=  np.abs(mask_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(UpperCAmelCase							,						1e-3   )
							self.assertEqual(mask[0, -3, -4]							,						0   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''cpu'''

							lowerCamelCase_					=  self.get_dummy_components()
							lowerCamelCase_					=  self.pipeline_class(**UpperCAmelCase   )
							pipe.to(UpperCAmelCase   )
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  self.get_dummy_inversion_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe.invert(**UpperCAmelCase   ).images
							lowerCamelCase_					=  image[0, -1, -3:, -3:]

							self.assertEqual(image.shape							,						(2, 32, 32, 3)   )
							lowerCamelCase_					=  np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]							,						)
							lowerCamelCase_					=  np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(UpperCAmelCase							,						1e-3   )




				def        UpperCAmelCase__						(				self   ):
							super().test_inference_batch_single_identical(expected_max_diff=5e-3   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''cpu'''

							lowerCamelCase_					=  self.get_dummy_components()

							lowerCamelCase_					=  {'''beta_start''': 0.0_0_0_8_5, '''beta_end''': 0.0_1_2, '''beta_schedule''': '''scaled_linear'''}
							lowerCamelCase_					=  DPMSolverMultistepScheduler(**UpperCAmelCase   )
							lowerCamelCase_					=  DPMSolverMultistepInverseScheduler(**UpperCAmelCase   )

							lowerCamelCase_					=  self.pipeline_class(**UpperCAmelCase   )
							pipe.to(UpperCAmelCase   )
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  self.get_dummy_inversion_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe.invert(**UpperCAmelCase   ).images
							lowerCamelCase_					=  image[0, -1, -3:, -3:]

							self.assertEqual(image.shape							,						(2, 32, 32, 3)   )
							lowerCamelCase_					=  np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]							,						)
							lowerCamelCase_					=  np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(UpperCAmelCase							,						1e-3   )




@require_torch_gpu
@slow
class  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				@classmethod
				def        UpperCAmelCase__						(				cls   ):
							lowerCamelCase_					=  load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''   )

							lowerCamelCase_					=  raw_image.convert('''RGB'''   ).resize((768, 768)   )

							lowerCamelCase_					=  raw_image




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  torch.manual_seed(0   )

							lowerCamelCase_					=  StableDiffusionDiffEditPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-1'''							,						safety_checker=UpperCAmelCase							,						torch_dtype=torch.floataa   )
							lowerCamelCase_					=  DDIMScheduler.from_config(pipe.scheduler.config   )
							lowerCamelCase_					=  DDIMInverseScheduler.from_config(pipe.scheduler.config   )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  '''a bowl of fruit'''
							lowerCamelCase_					=  '''a bowl of pears'''

							lowerCamelCase_					=  pipe.generate_mask(
							    image=self.raw_image							,						source_prompt=UpperCAmelCase							,						target_prompt=UpperCAmelCase							,						generator=UpperCAmelCase							,						)

							lowerCamelCase_					=  pipe.invert(
							    prompt=UpperCAmelCase							,						image=self.raw_image							,						inpaint_strength=0.7							,						generator=UpperCAmelCase   ).latents

							lowerCamelCase_					=  pipe(
							    prompt=UpperCAmelCase							,						mask_image=UpperCAmelCase							,						image_latents=UpperCAmelCase							,						generator=UpperCAmelCase							,						negative_prompt=UpperCAmelCase							,						inpaint_strength=0.7							,						output_type='''numpy'''							,						).images[0]

							lowerCamelCase_					=  (
							    np.array(
							        load_image(
							            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							            '''/diffedit/pears.png'''   ).resize((768, 768)   )   )
							    / 255
							)
							assert np.abs((expected_image - image).max()   ) < 5e-1





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  torch.manual_seed(0   )

							lowerCamelCase_					=  StableDiffusionDiffEditPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-1'''							,						safety_checker=UpperCAmelCase							,						torch_dtype=torch.floataa   )
							lowerCamelCase_					=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
							lowerCamelCase_					=  DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config   )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  '''a bowl of fruit'''
							lowerCamelCase_					=  '''a bowl of pears'''

							lowerCamelCase_					=  pipe.generate_mask(
							    image=self.raw_image							,						source_prompt=UpperCAmelCase							,						target_prompt=UpperCAmelCase							,						generator=UpperCAmelCase							,						)

							lowerCamelCase_					=  pipe.invert(
							    prompt=UpperCAmelCase							,						image=self.raw_image							,						inpaint_strength=0.7							,						generator=UpperCAmelCase							,						num_inference_steps=25							,						).latents

							lowerCamelCase_					=  pipe(
							    prompt=UpperCAmelCase							,						mask_image=UpperCAmelCase							,						image_latents=UpperCAmelCase							,						generator=UpperCAmelCase							,						negative_prompt=UpperCAmelCase							,						inpaint_strength=0.7							,						num_inference_steps=25							,						output_type='''numpy'''							,						).images[0]

							lowerCamelCase_					=  (
							    np.array(
							        load_image(
							            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							            '''/diffedit/pears.png'''   ).resize((768, 768)   )   )
							    / 255
							)
							assert np.abs((expected_image - image).max()   ) < 5e-1





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				if "cls_token" in name:
								lowercase_	    =   name.replace('''cls_token'''		,  '''vit.embeddings.cls_token'''   )
				if "mask_token" in name:
								lowercase_	    =   name.replace('''mask_token'''		,  '''decoder.mask_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'''		,  '''vit.embeddings.position_embeddings'''   )
				if "patch_embed.proj" in name:
								lowercase_	    =   name.replace('''patch_embed.proj'''		,  '''vit.embeddings.patch_embeddings.projection'''   )
				if "patch_embed.norm" in name:
								lowercase_	    =   name.replace('''patch_embed.norm'''		,  '''vit.embeddings.norm'''   )
				if "decoder_blocks" in name:
								lowercase_	    =   name.replace('''decoder_blocks'''		,  '''decoder.decoder_layers'''   )
				if "blocks" in name:
								lowercase_	    =   name.replace('''blocks'''		,  '''vit.encoder.layer'''   )
				if "attn.proj" in name:
								lowercase_	    =   name.replace('''attn.proj'''		,  '''attention.output.dense'''   )
				if "attn" in name:
								lowercase_	    =   name.replace('''attn'''		,  '''attention.self'''   )
				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:
								lowercase_	    =   name.replace('''norm.weight'''		,  '''vit.layernorm.weight'''   )
				if "norm.bias" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.bias'''		,  '''vit.layernorm.bias'''   )

				return name




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Any   ):


				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase_	    =   orig_state_dict.pop(snake_case__   )

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

								else:
												lowercase_	    =   val

				return orig_state_dict




def 							a		(				snake_case__:   str		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   ViTMAEConfig()
				if "large" in checkpoint_url:
								lowercase_	    =   1_024
								lowercase_	    =   4_096
								lowercase_	    =   24
								lowercase_	    =   16
				elif "huge" in checkpoint_url:
								lowercase_	    =   14
								lowercase_	    =   1_280
								lowercase_	    =   5_120
								lowercase_	    =   32
								lowercase_	    =   16

				lowercase_	    =   ViTMAEForPreTraining(snake_case__   )

				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''   )['''model''']

				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )

				lowercase_	    =   convert_state_dict(snake_case__		,  snake_case__   )

				model.load_state_dict(snake_case__   )
				model.eval()

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

				lowercase_	    =   Image.open(requests.get(snake_case__		,  stream=snake_case__   ).raw   )
				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )
				lowercase_	    =   image_processor(images=snake_case__		,  return_tensors='''pt'''   )

				# forward pass
				torch.manual_seed(2   )
				lowercase_	    =   model(**snake_case__   )
				lowercase_	    =   outputs.logits

				if "large" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]   )
				elif "huge" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]   )
				else:
								lowercase_	    =   torch.tensor(
								    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]   )

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

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

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


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)




