

'''simple docstring'''





A											=     {
    '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
A											=     {value: key for key, value in MORSE_CODE_DICT.items()}





def a(lowercase__	):





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





def a(lowercase__	):





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





def a():





	'''simple docstring'''
	snake_case_      		= 'Morse code here!'
	print(UpperCAmelCase__	)
	snake_case_      		= encrypt(UpperCAmelCase__	)
	print(UpperCAmelCase__	)
	snake_case_      		= decrypt(UpperCAmelCase__	)
	print(UpperCAmelCase__	)


if __name__ == "__main__":
		main()



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= 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


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= VQModel(
				    block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=3 ,				)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= CLIPTextConfig(
				    bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=10_00 ,				)
				return CLIPTextModel(__UpperCamelCase							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= DDIMScheduler()
				snake_case_      		= self.dummy_vq_model

				snake_case_      		= LDMPipeline(unet=__UpperCamelCase ,				vqvae=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=2 ,				output_type='numpy'							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=2 ,				output_type='numpy' ,				return_dict=__UpperCamelCase							)[0]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				snake_case_      		= np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < tolerance







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






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=5 ,				output_type='numpy'							).images

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

				assert image.shape == (1, 2_56, 2_56, 3)
				snake_case_      		= np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
