





'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()


class        __a   (unittest.TestCase  ):

							@property
							def      UpperCAmelCase__						(				self				:  Any )       ->							Optional[int]:







													"""simple docstring"""
													torch.manual_seed(0 )
													UpperCAmelCase_      :   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''')							,  )
													return model

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







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	"""cpu"""
													UpperCAmelCase_      :   Optional[Any]      =	self.dummy_uncond_unet
													UpperCAmelCase_      :   Optional[Any]      =	DDPMScheduler()

													UpperCAmelCase_      :   List[str]      =	DDPMPipeline(unet=lowerCAmelCase__							,  scheduler=lowerCAmelCase__ )
													ddpm.to(lowerCAmelCase__ )
													ddpm.set_progress_bar_config(disable=lowerCAmelCase__ )

													UpperCAmelCase_      :   str      =	torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
													UpperCAmelCase_      :   str      =	ddpm(generator=lowerCAmelCase__							,  num_inference_steps=2							,  output_type='''numpy''' ).images

													UpperCAmelCase_      :   Optional[int]      =	torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
													UpperCAmelCase_      :   Dict      =	ddpm(generator=lowerCAmelCase__							,  num_inference_steps=2							,  output_type='''numpy'''							,  return_dict=lowerCAmelCase__ )[0]

													UpperCAmelCase_      :   List[str]      =	image[0, -3:, -3:, -1]
													UpperCAmelCase_      :   Optional[int]      =	image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_      :   Dict      =	np.array(
													    [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 1.199E-03, 2.648E-04, 5.101E-04] )

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



							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	self.dummy_uncond_unet
													UpperCAmelCase_      :   str      =	DDPMScheduler(prediction_type='''sample''' )

													UpperCAmelCase_      :   Union[str, Any]      =	DDPMPipeline(unet=lowerCAmelCase__							,  scheduler=lowerCAmelCase__ )
													ddpm.to(lowerCAmelCase__ )
													ddpm.set_progress_bar_config(disable=lowerCAmelCase__ )

													UpperCAmelCase_      :   str      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   List[str]      =	ddpm(generator=lowerCAmelCase__							,  num_inference_steps=2							,  output_type='''numpy''' ).images

													UpperCAmelCase_      :   str      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   int      =	ddpm(generator=lowerCAmelCase__							,  num_inference_steps=2							,  output_type='''numpy''' )[0]

													UpperCAmelCase_      :   Any      =	image[0, -3:, -3:, -1]
													UpperCAmelCase_      :   List[str]      =	image_eps[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_      :   Optional[Any]      =	1E-2 if torch_device != """mps""" else 3E-2
													assert np.abs(image_slice.flatten() - image_eps_slice.flatten() ).max() < tolerance





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

							def      UpperCAmelCase__						(				self				:  Union[str, Any] )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	"""google/ddpm-cifar10-32"""

													UpperCAmelCase_      :   Optional[int]      =	UNetaDModel.from_pretrained(lowerCAmelCase__ )
													UpperCAmelCase_      :   Any      =	DDPMScheduler.from_pretrained(lowerCAmelCase__ )

													UpperCAmelCase_      :   Tuple      =	DDPMPipeline(unet=lowerCAmelCase__							,  scheduler=lowerCAmelCase__ )
													ddpm.to(lowerCAmelCase__ )
													ddpm.set_progress_bar_config(disable=lowerCAmelCase__ )

													UpperCAmelCase_      :   Optional[int]      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Optional[int]      =	ddpm(generator=lowerCAmelCase__							,  output_type='''numpy''' ).images

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

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_      :   Any      =	np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5] )
													assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






'''simple docstring'''

import numpy as np

_lowerCamelCase :				int														= [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]




class    lowerCamelCase__  :



   def __init__(    self     )				->     None:
        """simple docstring"""





        _UpperCamelCase  :Any       =np.array(lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     np.ndarray:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :str       =np.where(letter == self.SQUARE     )
        _UpperCamelCase  :Union[str, Any]       =np.concatenate([indexa + 1, indexa + 1]     )
        return indexes



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        return self.SQUARE[indexa - 1, indexa - 1]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :str       =message.lower()
        _UpperCamelCase  :List[Any]       =message.replace("""j"""					,      """i"""     )

        _UpperCamelCase  :List[str]       =""""""
        for letter_index in range(len(lowerCAmelCase__     )     ):
             if message[letter_index] != " ":
                  _UpperCamelCase  :Dict       =self.letter_to_numbers(message[letter_index]     )
                  _UpperCamelCase  :List[Any]       =encoded_message + str(numbers[0]     ) + str(numbers[1]     )
             elif message[letter_index] == " ":
                  _UpperCamelCase  :List[Any]       =encoded_message + """ """

        return encoded_message




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =message.replace(""" """					,      """  """     )
        _UpperCamelCase  :Dict       =""""""
        for numbers_index in range(int(len(lowerCAmelCase__     ) / 2     )     ):
             if message[numbers_index * 2] != " ":
                  _UpperCamelCase  :Tuple       =message[numbers_index * 2]
                  _UpperCamelCase  :List[str]       =message[numbers_index * 2 + 1]

                  _UpperCamelCase  :Dict       =self.numbers_to_letter(int(lowerCAmelCase__     )					,      int(lowerCAmelCase__     )     )
                  _UpperCamelCase  :Union[str, Any]       =decoded_message + letter
             elif message[numbers_index * 2] == " ":
                  _UpperCamelCase  :Dict       =decoded_message + """ """

        return decoded_message