


"""simple docstring"""


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




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

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

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

			return output_string


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []
			__lowerCAmelCase       					=    key - 1

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

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

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

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


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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


if __name__ == "__main__":
	import doctest

	doctest.testmod()








"""simple docstring"""


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A  :     Dict    					=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=7_68			):
										super().__init__(__a			)
										__lowerCAmelCase       					=    proj_size

										__lowerCAmelCase       					=    CLIPVisionModel(__a			)
										__lowerCAmelCase       					=    PaintByExampleMapper(__a			)
										__lowerCAmelCase       					=    nn.LayerNorm(config.hidden_size			)
										__lowerCAmelCase       					=    nn.Linear(config.hidden_size					,     self.proj_size			)

										# uncondition for scaling
										__lowerCAmelCase       					=    nn.Parameter(torch.randn((1, 1, self.proj_size)			)			)



							def     snake_case       (  self					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    self.model(pixel_values=__a			)
										__lowerCAmelCase       					=    clip_output.pooler_output
										__lowerCAmelCase       					=    self.mapper(latent_states[:, None]			)
										__lowerCAmelCase       					=    self.final_layer_norm(__a			)
										__lowerCAmelCase       					=    self.proj_out(__a			)
										if return_uncond_vector:
													return latent_states, self.uncond_vector

										return latent_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a			):
										super().__init__()
										__lowerCAmelCase       					=    (config.num_hidden_layers + 1) // 5
										__lowerCAmelCase       					=    config.hidden_size
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    nn.ModuleList(
										    [
										        BasicTransformerBlock(__a					,     __a					,     __a					,     activation_fn="gelu"					,     attention_bias=__a			)
										        for _ in range(__a			)
										    ]			)



							def     snake_case       (  self					,     __a			):
										for block in self.blocks:
													__lowerCAmelCase       					=    block(__a			)

										return hidden_states




