



"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class        __A       (	__snake_case     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				DDIMPipeline
		lowerCAmelCase			:					Tuple   	=				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
		lowerCAmelCase			:					List[str]   	=				PipelineTesterMixin.required_optional_params - {
		    """num_images_per_prompt""",
		    """latents""",
		    """callback""",
		    """callback_steps""",
		}
		lowerCAmelCase			:					int   	=				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
		lowerCAmelCase			:					Optional[Any]   	=				False







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				torch.manual_seed(0						)
				lowercase__   :    Optional[Any]				=			UNetaDModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,sample_size=32			,in_channels=3			,out_channels=3			,down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')			,up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')			,)
				lowercase__   :    List[str]				=			DDIMScheduler()
				lowercase__   :    Dict				=			{'unet': unet, 'scheduler': scheduler}
				return components







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  Optional[Any]			,_snake_case						:  Dict=0						)					->		str:






				"""simple docstring"""



				if str(_snake_case						).startswith('''mps'''						):
						lowercase__   :    Optional[int]				=			torch.manual_seed(_snake_case						)
				else:
						lowercase__   :    List[Any]				=			torch.Generator(device=_snake_case						).manual_seed(_snake_case						)
				lowercase__   :    Tuple				=			{
				    'batch_size': 1,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'output_type': 'numpy',
				}
				return inputs







		def 					UpperCAmelCase    (	self						:  int						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			'cpu'

				lowercase__   :    Optional[Any]				=			self.get_dummy_components()
				lowercase__   :    Union[str, Any]				=			self.pipeline_class(**_snake_case						)
				pipe.to(_snake_case						)
				pipe.set_progress_bar_config(disable=_snake_case						)

				lowercase__   :    List[str]				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    str				=			pipe(**_snake_case						).images
				lowercase__   :    Union[str, Any]				=			image[0, -3:, -3:, -1]

				self.assertEqual(image.shape			,(1, 32, 32, 3)						)
				lowercase__   :    List[str]				=			np.array(
				    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]						)
				lowercase__   :    int				=			np.abs(image_slice.flatten() - expected_slice						).max()
				self.assertLessEqual(_snake_case			,1e-3						)







		def 					UpperCAmelCase    (	self						:  Any						)					->		List[str]:






				"""simple docstring"""



				super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3						)







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Tuple:






				"""simple docstring"""



				super().test_save_load_local(expected_max_difference=3e-3						)







		def 					UpperCAmelCase    (	self						:  Any						)					->		Dict:






				"""simple docstring"""



				super().test_save_load_optional_components(expected_max_difference=3e-3						)







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				super().test_inference_batch_single_identical(expected_max_diff=3e-3						)





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







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		int:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			'google/ddpm-cifar10-32'

				lowercase__   :    Tuple				=			UNetaDModel.from_pretrained(_snake_case						)
				lowercase__   :    Tuple				=			DDIMScheduler()

				lowercase__   :    Optional[int]				=			DDIMPipeline(unet=_snake_case			,scheduler=_snake_case						)
				ddim.to(_snake_case						)
				ddim.set_progress_bar_config(disable=_snake_case						)

				lowercase__   :    Any				=			torch.manual_seed(0						)
				lowercase__   :    str				=			ddim(generator=_snake_case			,eta=0.0			,output_type='''numpy'''						).images

				lowercase__   :    Any				=			image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				lowercase__   :    Dict				=			np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    Tuple				=			'google/ddpm-ema-bedroom-256'

				lowercase__   :    List[Any]				=			UNetaDModel.from_pretrained(_snake_case						)
				lowercase__   :    List[Any]				=			DDIMScheduler.from_pretrained(_snake_case						)

				lowercase__   :    Tuple				=			DDIMPipeline(unet=_snake_case			,scheduler=_snake_case						)
				ddpm.to(_snake_case						)
				ddpm.set_progress_bar_config(disable=_snake_case						)

				lowercase__   :    int				=			torch.manual_seed(0						)
				lowercase__   :    Union[str, Any]				=			ddpm(generator=_snake_case			,output_type='''numpy'''						).images

				lowercase__   :    Union[str, Any]				=			image[0, -3:, -3:, -1]

				assert image.shape == (1, 256, 256, 3)
				lowercase__   :    Optional[Any]				=			np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2



def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   str      =       set()

     # Replace all the whitespace in our sentence
     lowercase__    :   Tuple      =       input_str.replace(' '	,     ''			)
     for alpha in input_str:
          if "a" <= alpha.lower() <= "z":
               frequency.add(alpha.lower()			)
     return len(SCREAMING_SNAKE_CASE_			) == 26

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   Dict      =       [False] * 26
     for char in input_str:
          if char.islower():
               lowercase__    :   List[Any]      =       True
          elif char.isupper():
               lowercase__    :   Optional[Any]      =       True
     return all(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def 	snake_case__			(    ):



     '''simple docstring'''





     from timeit import timeit

     lowercase__    :   Union[str, Any]      =       'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
     print(timeit('is_pangram()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_faster()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_fastest()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()

