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


a  :      List[str]            =    False

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    pass





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






    '''simple docstring'''




    def      _a    (    self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							int       =	VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	generator.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						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      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	VersatileDiffusionTextToImagePipeline.from_pretrained(
           "shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							str       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							int       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy" ).images

       lowerCAmelCase_:							List[Any]       =	image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCAmelCase_:							Optional[int]       =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							list[list[str]]       =	[[] for _ in range(lowercase					)]
   lowerCAmelCase_:							Optional[Any]       =	key - 1

   if key <= 0:
      raise ValueError("Height of grid can't be 0 or negative"					)
   if key == 1 or len(lowercase					) <= key:
      return input_string

   for position, character in enumerate(lowercase					):
      lowerCAmelCase_:							Optional[Any]       =	position % (lowest * 2)  # puts it in bounds
      lowerCAmelCase_:							Any       =	min(lowercase							,				lowest * 2 - num					)  # creates zigzag pattern
      temp_grid[num].append(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	["".join(lowercase					) for row in temp_grid]
   lowerCAmelCase_:							int       =	"".join(lowercase					)

   return output_string
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							Tuple       =	[]
   lowerCAmelCase_:							str       =	key - 1

   if key <= 0:
      raise ValueError("Height of grid can't be 0 or negative"					)
   if key == 1:
      return input_string

   lowerCAmelCase_:							list[list[str]]       =	[[] for _ in range(lowercase					)]  # generates template
   for position in range(len(lowercase					)					):
      lowerCAmelCase_:							List[str]       =	position % (lowest * 2)  # puts it in bounds
      lowerCAmelCase_:							Optional[int]       =	min(lowercase							,				lowest * 2 - num					)  # creates zigzag pattern
      temp_grid[num].append("*"					)

   lowerCAmelCase_:							Optional[Any]       =	0
   for row in temp_grid:  # fills in the characters
      lowerCAmelCase_:							Tuple       =	input_string[counter : counter + len(lowercase					)]
      grid.append(list(lowercase					)					)
      counter += len(lowercase					)

   lowerCAmelCase_:							int       =	""  # reads as zigzag
   for position in range(len(lowercase					)					):
      lowerCAmelCase_:							str       =	position % (lowest * 2)  # puts it in bounds
      lowerCAmelCase_:							Optional[int]       =	min(lowercase							,				lowest * 2 - num					)  # creates zigzag pattern
      output_string += grid[num][0]
      grid[num].pop(0					)
   return output_string
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Dict       =	{}
   for key_guess in range(1							,				len(lowercase					)					):  # tries every key
      lowerCAmelCase_:							int       =	decrypt(lowercase							,				lowercase					)
   return results


if __name__ == "__main__":
 import doctest

 doctest.testmod()