



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    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	List[Any]    =DDIMPipeline
							__lowerCamelCase	:	List[str]    =UNCONDITIONAL_IMAGE_GENERATION_PARAMS
							__lowerCamelCase	:	Tuple    =PipelineTesterMixin.required_optional_params - {
							    'num_images_per_prompt',
							    'latents',
							    'callback',
							    'callback_steps',
							}
							__lowerCamelCase	:	Optional[int]    =UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
							__lowerCamelCase	:	Any    =False





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											torch.manual_seed(0	)
											__a       =		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       =		DDIMScheduler()
											__a       =		{"""unet""": unet, """scheduler""": scheduler}
											return components





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		List[str]      ,				__lowercase		:		int=0	):

											'''simple docstring'''
											if str(__lowercase	).startswith("""mps"""	):
															__a       =		torch.manual_seed(__lowercase	)
											else:
															__a       =		torch.Generator(device=__lowercase	).manual_seed(__lowercase	)
											__a       =		{
											    """batch_size""": 1,
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """output_type""": """numpy""",
											}
											return inputs





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		"""cpu"""

											__a       =		self.get_dummy_components()
											__a       =		self.pipeline_class(**__lowercase	)
											pipe.to(__lowercase	)
											pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		self.get_dummy_inputs(__lowercase	)
											__a       =		pipe(**__lowercase	).images
											__a       =		image[0, -3:, -3:, -1]

											self.assertEqual(image.shape      ,				(1, 32, 32, 3)	)
											__a       =		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       =		np.abs(image_slice.flatten() - expected_slice	).max()
											self.assertLessEqual(__lowercase      ,				1E-3	)





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											super().test_save_load_local(expected_max_difference=3E-3	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											super().test_save_load_optional_components(expected_max_difference=3E-3	)







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											super().test_inference_batch_single_identical(expected_max_diff=3E-3	)






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





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		"""google/ddpm-cifar10-32"""

											__a       =		UNetaDModel.from_pretrained(__lowercase	)
											__a       =		DDIMScheduler()

											__a       =		DDIMPipeline(unet=__lowercase      ,				scheduler=__lowercase	)
											ddim.to(__lowercase	)
											ddim.set_progress_bar_config(disable=__lowercase	)

											__a       =		torch.manual_seed(0	)
											__a       =		ddim(generator=__lowercase      ,				eta=0.0      ,				output_type="""numpy"""	).images

											__a       =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											__a       =		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		:		str	):

											'''simple docstring'''
											__a       =		"""google/ddpm-ema-bedroom-256"""

											__a       =		UNetaDModel.from_pretrained(__lowercase	)
											__a       =		DDIMScheduler.from_pretrained(__lowercase	)

											__a       =		DDIMPipeline(unet=__lowercase      ,				scheduler=__lowercase	)
											ddpm.to(__lowercase	)
											ddpm.set_progress_bar_config(disable=__lowercase	)

											__a       =		torch.manual_seed(0	)
											__a       =		ddpm(generator=__lowercase      ,				output_type="""numpy"""	).images

											__a       =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 256, 256, 3)
											__a       =		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





import itertools
import string
from collections.abc import Generator, Iterable

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Iterable[str]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		iter(_SCREAMING_SNAKE_CASE    )
				while True:
								__a       =		tuple(itertools.islice(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )    )
								if not chunk:
												return
								yield chunk

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		"""""".join([c.upper() for c in dirty if c in string.ascii_letters]    )
				__a       =		""""""

				if len(_SCREAMING_SNAKE_CASE    ) < 2:
								return dirty

				for i in range(len(_SCREAMING_SNAKE_CASE    ) - 1    ):
								clean += dirty[i]

								if dirty[i] == dirty[i + 1]:
												clean += "X"

				clean += dirty[-1]

				if len(_SCREAMING_SNAKE_CASE    ) & 1:
								clean += "X"

				return clean

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
				# we're using a list instead of a '2d' array because it makes the math
				# for setting up the table and doing the actual encoding/decoding simpler
				__a       =		[]

				# copy key chars into the table if they are in `alphabet` ignoring duplicates
				for char in key.upper():
								if char not in table and char in alphabet:
												table.append(_SCREAMING_SNAKE_CASE    )

    # fill the rest of the table in with the remaining alphabet chars
				for char in alphabet:
								if char not in table:
												table.append(_SCREAMING_SNAKE_CASE    )

				return table

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		generate_table(_SCREAMING_SNAKE_CASE    )
				__a       =		prepare_input(_SCREAMING_SNAKE_CASE    )
				__a       =		""""""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(_SCREAMING_SNAKE_CASE				,     2    ):
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     5    )
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     5    )

								if rowa == rowa:
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
								elif cola == cola:
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
								else:  # rectangle
												ciphertext += table[rowa * 5 + cola]
												ciphertext += table[rowa * 5 + cola]

				return ciphertext

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		generate_table(_SCREAMING_SNAKE_CASE    )
				__a       =		""""""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(_SCREAMING_SNAKE_CASE				,     2    ):
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     5    )
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     5    )

								if rowa == rowa:
												plaintext += table[rowa * 5 + (cola - 1) % 5]
												plaintext += table[rowa * 5 + (cola - 1) % 5]
								elif cola == cola:
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
								else:  # rectangle
												plaintext += table[rowa * 5 + cola]
												plaintext += table[rowa * 5 + cola]

				return plaintext
