




'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase      :   Any      =       False







class 					_a (unittest.TestCase	):





     '''simple docstring'''



     pass







@nightly
@require_torch_gpu
class 					_a (unittest.TestCase	):





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				List[Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      Union[str, Any]        =			VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      List[str]        =			"""A painting of a squirrel eating a burger """
          snake_case			:      Tuple        =			torch.manual_seed(0							)
          snake_case			:      Optional[Any]        =			pipe(
              prompt=__a							,generator=__a							,guidance_scale=7.5							,num_inference_steps=2							,output_type="""numpy"""							).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__a							)
               snake_case			:      Union[str, Any]        =			VersatileDiffusionTextToImagePipeline.from_pretrained(__a							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      Tuple        =			generator.manual_seed(0							)
          snake_case			:      int        =			pipe(
              prompt=__a							,generator=__a							,guidance_scale=7.5							,num_inference_steps=2							,output_type="""numpy"""							).images

          assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"







     def 			snake_case_      (				self							)      ->				Optional[Any]:
          snake_case			:      int        =			VersatileDiffusionTextToImagePipeline.from_pretrained(
              """shi-labs/versatile-diffusion"""							,torch_dtype=torch.floataa							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      Any        =			"""A painting of a squirrel eating a burger """
          snake_case			:      Tuple        =			torch.manual_seed(0							)
          snake_case			:      Tuple        =			pipe(
              prompt=__a							,generator=__a							,guidance_scale=7.5							,num_inference_steps=50							,output_type="""numpy"""							).images

          snake_case			:      Dict        =			image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          snake_case			:      Dict        =			np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

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









'''simple docstring'''







import numpy as np

lowercase      :   str      =       [
    ["""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 					_a :





     '''simple docstring'''



     def __init__(				self							)      ->				None:
          snake_case			:      Dict        =			np.array(__a							)







     def 			snake_case_      (				self							,__a							)      ->				np.ndarray:
          snake_case ,		snake_case			:      Optional[Any]        =			np.where(letter == self.SQUARE							)
          snake_case			:      List[Any]        =			np.concatenate([indexa + 1, indexa + 1]							)
          return indexes







     def 			snake_case_      (				self							,__a							,__a							)      ->				str:
          snake_case			:      List[Any]        =			self.SQUARE[indexa - 1, indexa - 1]
          return letter







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      List[Any]        =			message.lower()
          snake_case			:      Dict        =			message.replace(""" """							,""""""							)
          snake_case			:      List[str]        =			message.replace("""j"""							,"""i"""							)

          snake_case			:      Optional[int]        =			np.empty((2, len(__a							))							)
          for letter_index in range(len(__a							)							):
               snake_case			:      Tuple        =			self.letter_to_numbers(message[letter_index]							)

               snake_case			:      Optional[Any]        =			numbers[0]
               snake_case			:      List[str]        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape(2 * len(__a							)							)
          snake_case			:      List[Any]        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      List[str]        =			int(second_step[numbers_index * 2]							)
               snake_case			:      List[Any]        =			int(second_step[(numbers_index * 2) + 1]							)
               snake_case			:      str        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      Any        =			encoded_message + letter

          return encoded_message







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      Any        =			message.lower()
          message.replace(""" """							,""""""							)
          snake_case			:      Dict        =			np.empty(2 * len(__a							)							)
          for letter_index in range(len(__a							)							):
               snake_case			:      str        =			self.letter_to_numbers(message[letter_index]							)
               snake_case			:      Any        =			numbers[0]
               snake_case			:      int        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape((2, len(__a							))							)
          snake_case			:      Dict        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      Optional[Any]        =			int(second_step[0, numbers_index]							)
               snake_case			:      Optional[int]        =			int(second_step[1, numbers_index]							)
               snake_case			:      Tuple        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      int        =			decoded_message + letter

          return decoded_message



