import os
from collections.abc import Iterator




def     UpperCamelCase						(	__lowercase						:			str = "."     ):
		'''simple docstring'''




		for dir_path, dir_names, filenames in os.walk(__lowercase     ):
				A_		:    Any         =				[d for d in dir_names if d != 'scripts' and d[0] not in '._']
				for filename in filenames:
						if filename == "__init__.py":
								continue
						if os.path.splitext(__lowercase     )[1] in (".py", ".ipynb"):
								yield os.path.join(__lowercase						,__lowercase     ).lstrip('./'     )




def     UpperCamelCase						(	__lowercase						:			List[Any]     ):
		'''simple docstring'''




		return f'''{i * '  '}*''' if i else "\n##"




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    List[Any]         =				old_path.split(os.sep     )
		for i, new_part in enumerate(new_path.split(os.sep     )     ):
				if (i + 1 > len(__lowercase     ) or old_parts[i] != new_part) and new_part:
						print(f'''{md_prefix(__lowercase     )} {new_part.replace('_'						,' '     ).title()}'''     )
		return new_path




def     UpperCamelCase						(	__lowercase						:			str = "."     ):
		'''simple docstring'''




		A_		:    str         =				''
		for filepath in sorted(good_file_paths(__lowercase     )     ):
				A_		:    Optional[Any]         =				os.path.split(__lowercase     )
				if filepath != old_path:
						A_		:    Tuple         =				print_path(__lowercase						,__lowercase     )
				A_		:    int         =				(filepath.count(os.sep     ) + 1) if filepath else 0
				A_		:    Dict         =				f'''{filepath}/{filename}'''.replace(' '						,'%20'     )
				A_		:    int         =				os.path.splitext(filename.replace('_'						,' '     ).title()     )[0]
				print(f'''{md_prefix(__lowercase     )} [{filename}]({url})'''     )


if __name__ == "__main__":
	print_directory_md(""".""")







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    int         =				UNetaDModel(
						    block_out_channels=(3_2, 6_4)		,     layers_per_block=2		,     sample_size=3_2		,     in_channels=3		,     out_channels=3		,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    Optional[Any]         =				VQModel(
						    block_out_channels=[3_2, 6_4]		,     in_channels=3		,     out_channels=3		,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,     latent_channels=3		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    List[Any]         =				CLIPTextConfig(
						    bos_token_id=0		,     eos_token_id=2		,     hidden_size=3_2		,     intermediate_size=3_7		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1_0_0_0		,     )
						return CLIPTextModel(lowercase )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.dummy_uncond_unet
						A_		:    List[Any]         =				DDIMScheduler()
						A_		:    Any         =				self.dummy_vq_model

						A_		:    int         =				LDMPipeline(unet=lowercase		,     vqvae=lowercase		,     scheduler=lowercase )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    Dict         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy' ).images

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy'		,     return_dict=lowercase )[0]

						A_		:    Union[str, Any]         =				image[0, -3:, -3:, -1]
						A_		:    int         =				image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						A_		:    List[str]         =				np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
						A_		:    str         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance





@slow
@require_torch
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=5		,     output_type='numpy' ).images

						A_		:    List[Any]         =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						A_		:    Tuple         =				np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
						A_		:    Dict         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance






