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 					lowercase__						(   UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		DDIMPipeline
						UpperCamelCase_     =		UNCONDITIONAL_IMAGE_GENERATION_PARAMS
						UpperCamelCase_     =		PipelineTesterMixin.required_optional_params - {
						    """num_images_per_prompt""",
						    """latents""",
						    """callback""",
						    """callback_steps""",
						}
						UpperCamelCase_     =		UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
						UpperCamelCase_     =		False


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Dict							=							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''')    ,		)
												SCREAMING_SNAKE_CASE							:    Tuple							=							DDIMScheduler()
												SCREAMING_SNAKE_CASE							:    str							=							{'''unet''': unet, '''scheduler''': scheduler}
												return components


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				Any=0						):





												'''simple docstring'''





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


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							'''cpu'''

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_dummy_components()
												SCREAMING_SNAKE_CASE							:    int							=							self.pipeline_class(**UpperCamelCase__						)
												pipe.to(UpperCamelCase__						)
												pipe.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.get_dummy_inputs(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Any							=							pipe(**UpperCamelCase__						).images
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							image[0, -3:, -3:, -1]

												self.assertEqual(image.shape    ,		(1, 32, 32, 3)						)
												SCREAMING_SNAKE_CASE							:    Dict							=							np.array(
												    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							np.abs(image_slice.flatten() - expected_slice						).max()
												self.assertLessEqual(UpperCamelCase__    ,		1E-3						)


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





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


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





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


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





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



						def        __A       (       self	:				str						):





												'''simple docstring'''





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






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


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''google/ddpm-cifar10-32'''

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							UNetaDModel.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							DDIMScheduler()

												SCREAMING_SNAKE_CASE							:    str							=							DDIMPipeline(unet=UpperCamelCase__    ,		scheduler=UpperCamelCase__						)
												ddim.to(UpperCamelCase__						)
												ddim.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Any							=							torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Any							=							ddim(generator=UpperCamelCase__    ,		eta=0.0    ,		output_type='''numpy'''						).images

												SCREAMING_SNAKE_CASE							:    Tuple							=							image[0, -3:, -3:, -1]

												assert image.shape == (1, 32, 32, 3)
												SCREAMING_SNAKE_CASE							:    int							=							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        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							'''google/ddpm-ema-bedroom-256'''

												SCREAMING_SNAKE_CASE							:    List[str]							=							UNetaDModel.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							DDIMScheduler.from_pretrained(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							DDIMPipeline(unet=UpperCamelCase__    ,		scheduler=UpperCamelCase__						)
												ddpm.to(UpperCamelCase__						)
												ddpm.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    int							=							torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							ddpm(generator=UpperCamelCase__    ,		output_type='''numpy'''						).images

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

												assert image.shape == (1, 256, 256, 3)
												SCREAMING_SNAKE_CASE							:    int							=							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       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





