



'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class      _lowerCAmelCase							(      unittest.TestCase			):


	@property
	def    _a     (self							):
				torch.manual_seed(0							)
				A_     :						List[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    _a     (self							):
				A_     :						Tuple     =					self.dummy_uncond_unet
				A_     :						Any     =					PNDMScheduler()

				A_     :						List[str]     =					PNDMPipeline(unet=lowercase	,				scheduler=lowercase							)
				pndm.to(lowercase							)
				pndm.set_progress_bar_config(disable=lowercase							)

				A_     :						List[Any]     =					torch.manual_seed(0							)
				A_     :						int     =					pndm(generator=lowercase	,				num_inference_steps=20	,				output_type="""numpy"""							).images

				A_     :						Tuple     =					torch.manual_seed(0							)
				A_     :						Any     =					pndm(generator=lowercase	,				num_inference_steps=20	,				output_type="""numpy"""	,				return_dict=lowercase							)[0]

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

				assert image.shape == (1, 32, 32, 3)
				A_     :						List[str]     =					np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







@slow
@require_torch
class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						Union[str, Any]     =					"""google/ddpm-cifar10-32"""

				A_     :						List[Any]     =					UNetaDModel.from_pretrained(lowercase							)
				A_     :						Union[str, Any]     =					PNDMScheduler()

				A_     :						Union[str, Any]     =					PNDMPipeline(unet=lowercase	,				scheduler=lowercase							)
				pndm.to(lowercase							)
				pndm.set_progress_bar_config(disable=lowercase							)
				A_     :						List[str]     =					torch.manual_seed(0							)
				A_     :						List[str]     =					pndm(generator=lowercase	,				output_type="""numpy"""							).images

				A_     :						List[Any]     =					image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				A_     :						List[str]     =					np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]							)

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




'''simple docstring'''




lowerCamelCase							:Any       = {
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowerCamelCase							:Any       = {value: key for key, value in MORSE_CODE_DICT.items()}



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(MORSE_CODE_DICT[char] for char in message.upper()   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return "".join(REVERSE_DICT[char] for char in message.split()   )



def      a      (			):
			'''simple docstring'''





			A_     :						List[str]     =					"""Morse code here!"""
			print(lowerCamelCase__   )
			A_     :						str     =					encrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )
			A_     :						Dict     =					decrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )


if __name__ == "__main__":
						main()