





'''simple docstring'''






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class 		UpperCAmelCase   (				lowercase_   ,     lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				VQModel
  lowerCAmelCase_					=				"""sample"""


  @property
  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					Union[str, Any]=(32, 32)  )     ->						Optional[Any]:
    _UpperCamelCase       			=4
    _UpperCamelCase       			=3

    _UpperCamelCase       			=floats_tensor((batch_size, num_channels) + sizes  ).to(UpperCamelCase__  )

    return {"sample": image}


  @property
  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Optional[int]:
    return (3, 32, 32)


  @property
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    return (3, 32, 32)


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    _UpperCamelCase       			={
        '''block_out_channels''': [32, 64],
        '''in_channels''': 3,
        '''out_channels''': 3,
        '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
        '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
        '''latent_channels''': 3,
    }
    _UpperCamelCase       			=self.dummy_input
    return init_dict, inputs_dict


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    pass


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Optional[Any]:
    pass


  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[int]:
    _UpperCamelCase			,     _UpperCamelCase       			=VQModel.from_pretrained('''fusing/vqgan-dummy'''							,	output_loading_info=UpperCamelCase__  )
    self.assertIsNotNone(UpperCamelCase__  )
    self.assertEqual(len(loading_info['''missing_keys''']  )							,	0  )

    model.to(UpperCamelCase__  )
    _UpperCamelCase       			=model(**self.dummy_input  )

    assert image is not None, "Make sure output is not None"




  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[Any]:
    _UpperCamelCase       			=VQModel.from_pretrained('''fusing/vqgan-dummy'''  )
    model.to(UpperCamelCase__  ).eval()

    torch.manual_seed(0  )
    if torch.cuda.is_available():
      torch.cuda.manual_seed_all(0  )

    _UpperCamelCase       			=torch.randn(1							,	model.config.in_channels							,	model.config.sample_size							,	model.config.sample_size  )
    _UpperCamelCase       			=image.to(UpperCamelCase__  )
    with torch.no_grad():
      _UpperCamelCase       			=model(UpperCamelCase__  ).sample

    _UpperCamelCase       			=output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    _UpperCamelCase       			=torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]  )
    # fmt: on
    self.assertTrue(torch.allclose(UpperCamelCase__							,	UpperCamelCase__							,	atol=1E-3  )  )













'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]
  _UpperCamelCase       			=key - 1

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

  for position, character in enumerate(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[''''''.join(__SCREAMING_SNAKE_CASE   ) for row in temp_grid]
  _UpperCamelCase       			=''''''.join(__SCREAMING_SNAKE_CASE   )

  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[]
  _UpperCamelCase       			=key - 1

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

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]  # generates template
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append('''*'''   )

  _UpperCamelCase       			=0
  for row in temp_grid:  # fills in the characters
    _UpperCamelCase       			=input_string[counter : counter + len(__SCREAMING_SNAKE_CASE   )]
    grid.append(list(__SCREAMING_SNAKE_CASE   )   )
    counter += len(__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=''''''  # reads as zigzag
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    output_string += grid[num][0]
    grid[num].pop(0   )
  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			={}
  for key_guess in range(1  ,      len(__SCREAMING_SNAKE_CASE   )   ):  # tries every key
    _UpperCamelCase       			=decrypt(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  return results


if __name__ == "__main__":
      import doctest

      doctest.testmod()






