





"""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        __UpperCamelCase				(						a__		,       unittest.TestCase						):
							lowerCamelCase   :					Optional[int]      					=DDIMPipeline
							lowerCamelCase   :					Any      					=UNCONDITIONAL_IMAGE_GENERATION_PARAMS
							lowerCamelCase   :					List[str]      					=PipelineTesterMixin.required_optional_params - {
							    """num_images_per_prompt""",
							    """latents""",
							    """callback""",
							    """callback_steps""",
							}
							lowerCamelCase   :					List[str]      					=UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
							lowerCamelCase   :					Optional[Any]      					=False






							def     __a   (   self      )	-> List[Any]:
								torch.manual_seed(0      )
								a      :		str	=				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")      ,					)
								a      :		List[str]	=				DDIMScheduler()
								a      :		Optional[int]	=				{"unet": unet, "scheduler": scheduler}
								return components






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__=0      )	-> Any:
								if str(lowerCAmelCase__      ).startswith("mps"      ):
									a      :		List[str]	=				torch.manual_seed(lowerCAmelCase__      )
								else:
									a      :		Optional[Any]	=				torch.Generator(device=lowerCAmelCase__      ).manual_seed(lowerCAmelCase__      )
								a      :		Union[str, Any]	=				{
								    "batch_size": 1,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}
								return inputs






							def     __a   (   self      )	-> List[Any]:
								a      :		Dict	=				"cpu"

								a      :		str	=				self.get_dummy_components()
								a      :		Optional[Any]	=				self.pipeline_class(**lowerCAmelCase__      )
								pipe.to(lowerCAmelCase__      )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__      )

								a      :		Tuple	=				self.get_dummy_inputs(lowerCAmelCase__      )
								a      :		str	=				pipe(**lowerCAmelCase__      ).images
								a      :		Tuple	=				image[0, -3:, -3:, -1]

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






							def     __a   (   self      )	-> List[Any]:
								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )






							def     __a   (   self      )	-> str:
								super().test_save_load_local(expected_max_difference=3E-3      )






							def     __a   (   self      )	-> Union[str, Any]:
								super().test_save_load_optional_components(expected_max_difference=3E-3      )






							def     __a   (   self      )	-> Any:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3      )


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






							def     __a   (   self      )	-> Tuple:
								a      :		Optional[Any]	=				"google/ddpm-cifar10-32"

								a      :		Tuple	=				UNetaDModel.from_pretrained(lowerCAmelCase__      )
								a      :		List[str]	=				DDIMScheduler()

								a      :		Union[str, Any]	=				DDIMPipeline(unet=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )
								ddim.to(lowerCAmelCase__      )
								ddim.set_progress_bar_config(disable=lowerCAmelCase__      )

								a      :		Union[str, Any]	=				torch.manual_seed(0      )
								a      :		Any	=				ddim(generator=lowerCAmelCase__      ,					eta=0.0      ,					output_type="numpy"      ).images

								a      :		Optional[Any]	=				image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								a      :		List[str]	=				np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]      )

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






							def     __a   (   self      )	-> List[str]:
								a      :		Optional[int]	=				"google/ddpm-ema-bedroom-256"

								a      :		Any	=				UNetaDModel.from_pretrained(lowerCAmelCase__      )
								a      :		Optional[int]	=				DDIMScheduler.from_pretrained(lowerCAmelCase__      )

								a      :		Optional[Any]	=				DDIMPipeline(unet=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )
								ddpm.to(lowerCAmelCase__      )
								ddpm.set_progress_bar_config(disable=lowerCAmelCase__      )

								a      :		str	=				torch.manual_seed(0      )
								a      :		List[str]	=				ddpm(generator=lowerCAmelCase__      ,					output_type="numpy"      ).images

								a      :		Optional[int]	=				image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)
								a      :		str	=				np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]      )

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









"""simple docstring"""




a						: Any     	=			{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


a						: List[Any]     	=			{value: key for key, value in encode_dict.items()}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	a      :		int	=				""
	for letter in word.lower():
		if letter.isalpha() or letter == " ":
			encoded += encode_dict[letter]
		else:
			raise Exception("encode() accepts only letters of the alphabet and spaces"					)
	return encoded






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	if set(_lowercase					) - {"A", "B", " "} != set():
		raise Exception("decode() accepts only 'A', 'B' and spaces"					)
	a      :		Optional[Any]	=				""
	for word in coded.split():
		while len(_lowercase					) != 0:
			decoded += decode_dict[word[:5]]
			a      :		List[Any]	=				word[5:]
		decoded += " "
	return decoded.strip()


if __name__ == "__main__":
		from doctest import testmod

		testmod()


