

"""simple docstring"""






def 	lowercase_						(     _lowercase							:		str				,   _lowercase							:		int						):




					'''simple docstring'''






					UpperCAmelCase      : list[list[str]]   =					[[] for _ in range(_lowercase						)]
					UpperCAmelCase      : 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						):
										UpperCAmelCase      : Optional[int]   =					position % (lowest * 2)  # puts it in bounds
										UpperCAmelCase      : Dict   =					min(_lowercase				,   lowest * 2 - num						)  # creates zigzag pattern
										temp_grid[num].append(_lowercase						)
					UpperCAmelCase      : Union[str, Any]   =					[''''''.join(_lowercase						) for row in temp_grid]
					UpperCAmelCase      : Union[str, Any]   =					''''''.join(_lowercase						)

					return output_string



def 	lowercase_						(     _lowercase							:		str				,   _lowercase							:		int						):




					'''simple docstring'''






					UpperCAmelCase      : str   =					[]
					UpperCAmelCase      : List[Any]   =					key - 1

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

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

					UpperCAmelCase      : Union[str, Any]   =					0
					for row in temp_grid:  # fills in the characters
										UpperCAmelCase      : List[Any]   =					input_string[counter : counter + len(_lowercase						)]
										grid.append(list(_lowercase						)						)
										counter += len(_lowercase						)

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



def 	lowercase_						(     _lowercase							:		str						):




					'''simple docstring'''






					UpperCAmelCase      : List[str]   =					{}
					for key_guess in range(1				,   len(_lowercase						)						):  # tries every key
										UpperCAmelCase      : List[str]   =					decrypt(_lowercase				,   _lowercase						)
					return results


if __name__ == "__main__":
	import doctest

	doctest.testmod()



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 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = VQModel
   lowerCAmelCase   :     List[str]	      = '''sample'''



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	Tuple=(32, 32)				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			4
    _UpperCamelCase:  Tuple    						=			3

    _UpperCamelCase:  List[str]    						=			floats_tensor((batch_size, num_channels) + sizes				).to(_lowercase				)

    return {"sample": image}



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return (3, 32, 32)



   @property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    return (3, 32, 32)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			{
        '''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:  Optional[Any]    						=			self.dummy_input
    return init_dict, inputs_dict



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



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

    model.to(_lowercase				)
    _UpperCamelCase:  Tuple    						=			model(**self.dummy_input				)

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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
    model.to(_lowercase				).eval()

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

    _UpperCamelCase:  int    						=			torch.randn(1		,	model.config.in_channels		,	model.config.sample_size		,	model.config.sample_size				)
    _UpperCamelCase:  Tuple    						=			image.to(_lowercase				)
    with torch.no_grad():
     _UpperCamelCase:  Optional[int]    						=			model(_lowercase				).sample

    _UpperCamelCase:  List[Any]    						=			output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    _UpperCamelCase:  Any    						=			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(_lowercase		,	_lowercase		,	atol=1E-3				)				)