
'''simple docstring'''


from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin




@dataclass
class     __snake_case			(    _a	):
		a_          =					42







class     __snake_case			(    _a					,			_a	):




		@register_to_config
		def __init__(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:int = 1_6			,    _SCREAMING_SNAKE_CASE					:int = 8_8			,    _SCREAMING_SNAKE_CASE					:Optional[int] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    _SCREAMING_SNAKE_CASE					:float = 0.0			,    _SCREAMING_SNAKE_CASE					:int = 3_2			,    _SCREAMING_SNAKE_CASE					:Optional[int] = None			,    _SCREAMING_SNAKE_CASE					:bool = False			,    _SCREAMING_SNAKE_CASE					:Optional[int] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = None			,    _SCREAMING_SNAKE_CASE					:str = "geglu"			,    _SCREAMING_SNAKE_CASE					:Optional[int] = None			,    _SCREAMING_SNAKE_CASE					:bool = False			,    _SCREAMING_SNAKE_CASE					:bool = False			,    _SCREAMING_SNAKE_CASE					:bool = False			,    _SCREAMING_SNAKE_CASE					:str = "layer_norm"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    )					->			Union[str, Any]:






								'''simple docstring'''






								super().__init__()
								UpperCamelCase_     :	int					     =     use_linear_projection
								UpperCamelCase_     :	Dict					     =     num_attention_heads
								UpperCamelCase_     :	Tuple					     =     attention_head_dim
								UpperCamelCase_     :	str					     =     num_attention_heads * attention_head_dim

								# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
								# Define whether input is continuous or discrete depending on configuration
								UpperCamelCase_     :	Optional[int]					     =     (in_channels is not None) and (patch_size is None)
								UpperCamelCase_     :	Dict					     =     num_vector_embeds is not None
								UpperCamelCase_     :	Optional[Any]					     =     in_channels is not None and patch_size is not None

								if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
														UpperCamelCase_     :	Union[str, Any]					     =     (
														    F"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
														    """ incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."""
														    """ Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"""
														    """ results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"""
														    """ would be very nice if you could open a Pull request for the `transformer/config.json` file"""
														)
														deprecate("""norm_type!=num_embeds_ada_norm"""			,    """1.0.0"""			,    _SCREAMING_SNAKE_CASE			,    standard_warn=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Any					     =     """ada_norm"""

								if self.is_input_continuous and self.is_input_vectorized:
														raise ValueError(
														    F"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
														    """ sure that either `in_channels` or `num_vector_embeds` is None.""")
								elif self.is_input_vectorized and self.is_input_patches:
														raise ValueError(
														    F"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
														    """ sure that either `num_vector_embeds` or `num_patches` is None.""")
								elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
														raise ValueError(
														    F"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
														    F""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.""")

								# 2. Define input layers
								if self.is_input_continuous:
														UpperCamelCase_     :	str					     =     in_channels

														UpperCamelCase_     :	str					     =     torch.nn.GroupNorm(num_groups=_SCREAMING_SNAKE_CASE			,    num_channels=_SCREAMING_SNAKE_CASE			,    eps=1E-6			,    affine=_SCREAMING_SNAKE_CASE)
														if use_linear_projection:
																				UpperCamelCase_     :	Optional[int]					     =     nn.Linear(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)
														else:
																				UpperCamelCase_     :	List[Any]					     =     nn.Convad(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    kernel_size=1			,    stride=1			,    padding=0)
								elif self.is_input_vectorized:
														assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
														assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

														UpperCamelCase_     :	Dict					     =     sample_size
														UpperCamelCase_     :	Union[str, Any]					     =     sample_size
														UpperCamelCase_     :	List[str]					     =     num_vector_embeds
														UpperCamelCase_     :	Any					     =     self.height * self.width

														UpperCamelCase_     :	List[Any]					     =     ImagePositionalEmbeddings(
														    num_embed=_SCREAMING_SNAKE_CASE			,    embed_dim=_SCREAMING_SNAKE_CASE			,    height=self.height			,    width=self.width)
								elif self.is_input_patches:
														assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

														UpperCamelCase_     :	str					     =     sample_size
														UpperCamelCase_     :	Dict					     =     sample_size

														UpperCamelCase_     :	Optional[int]					     =     patch_size
														UpperCamelCase_     :	Dict					     =     PatchEmbed(
														    height=_SCREAMING_SNAKE_CASE			,    width=_SCREAMING_SNAKE_CASE			,    patch_size=_SCREAMING_SNAKE_CASE			,    in_channels=_SCREAMING_SNAKE_CASE			,    embed_dim=_SCREAMING_SNAKE_CASE			,    )

								# 3. Define transformers blocks
								UpperCamelCase_     :	Optional[Any]					     =     nn.ModuleList(
								    [
								        BasicTransformerBlock(
								            _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    dropout=_SCREAMING_SNAKE_CASE			,    cross_attention_dim=_SCREAMING_SNAKE_CASE			,    activation_fn=_SCREAMING_SNAKE_CASE			,    num_embeds_ada_norm=_SCREAMING_SNAKE_CASE			,    attention_bias=_SCREAMING_SNAKE_CASE			,    only_cross_attention=_SCREAMING_SNAKE_CASE			,    upcast_attention=_SCREAMING_SNAKE_CASE			,    norm_type=_SCREAMING_SNAKE_CASE			,    norm_elementwise_affine=_SCREAMING_SNAKE_CASE			,    )
								        for d in range(_SCREAMING_SNAKE_CASE)
								    ])

								# 4. Define output layers
								UpperCamelCase_     :	Tuple					     =     in_channels if out_channels is None else out_channels
								if self.is_input_continuous:
														# TODO: should use out_channels for continuous projections
														if use_linear_projection:
																				UpperCamelCase_     :	List[str]					     =     nn.Linear(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)
														else:
																				UpperCamelCase_     :	Tuple					     =     nn.Convad(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    kernel_size=1			,    stride=1			,    padding=0)
								elif self.is_input_vectorized:
														UpperCamelCase_     :	Dict					     =     nn.LayerNorm(_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	List[Any]					     =     nn.Linear(_SCREAMING_SNAKE_CASE			,    self.num_vector_embeds - 1)
								elif self.is_input_patches:
														UpperCamelCase_     :	Dict					     =     nn.LayerNorm(_SCREAMING_SNAKE_CASE			,    elementwise_affine=_SCREAMING_SNAKE_CASE			,    eps=1E-6)
														UpperCamelCase_     :	Optional[int]					     =     nn.Linear(_SCREAMING_SNAKE_CASE			,    2 * inner_dim)
														UpperCamelCase_     :	List[Any]					     =     nn.Linear(_SCREAMING_SNAKE_CASE			,    patch_size * patch_size * self.out_channels)





		def 	_snake_case	(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:torch.Tensor			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Tensor] = None			,    _SCREAMING_SNAKE_CASE					:Optional[torch.LongTensor] = None			,    _SCREAMING_SNAKE_CASE					:Optional[torch.LongTensor] = None			,    _SCREAMING_SNAKE_CASE					:Dict[str, Any] = None			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Tensor] = None			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Tensor] = None			,    _SCREAMING_SNAKE_CASE					:bool = True			,    )					->			int:






								'''simple docstring'''






								if attention_mask is not None and attention_mask.ndim == 2:
														# assume that mask is expressed as:
														#   (1 = keep,      0 = discard)
														# convert mask into a bias that can be added to attention scores:
														#       (keep = +0,     discard = -10000.0)
														UpperCamelCase_     :	Any					     =     (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
														UpperCamelCase_     :	Optional[int]					     =     attention_mask.unsqueeze(1)

								# convert encoder_attention_mask to a bias the same way we do for attention_mask
								if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
														UpperCamelCase_     :	List[Any]					     =     (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
														UpperCamelCase_     :	Dict					     =     encoder_attention_mask.unsqueeze(1)

								# 1. Input
								if self.is_input_continuous:
														UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     :	Tuple					     =     hidden_states.shape
														UpperCamelCase_     :	Tuple					     =     hidden_states

														UpperCamelCase_     :	Tuple					     =     self.norm(_SCREAMING_SNAKE_CASE)
														if not self.use_linear_projection:
																				UpperCamelCase_     :	Optional[int]					     =     self.proj_in(_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	Optional[int]					     =     hidden_states.shape[1]
																				UpperCamelCase_     :	str					     =     hidden_states.permute(0			,    2			,    3			,    1).reshape(_SCREAMING_SNAKE_CASE			,    height * width			,    _SCREAMING_SNAKE_CASE)
														else:
																				UpperCamelCase_     :	List[Any]					     =     hidden_states.shape[1]
																				UpperCamelCase_     :	Dict					     =     hidden_states.permute(0			,    2			,    3			,    1).reshape(_SCREAMING_SNAKE_CASE			,    height * width			,    _SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	Union[str, Any]					     =     self.proj_in(_SCREAMING_SNAKE_CASE)
								elif self.is_input_vectorized:
														UpperCamelCase_     :	Optional[int]					     =     self.latent_image_embedding(_SCREAMING_SNAKE_CASE)
								elif self.is_input_patches:
														UpperCamelCase_     :	Optional[int]					     =     self.pos_embed(_SCREAMING_SNAKE_CASE)

								# 2. Blocks
								for block in self.transformer_blocks:
														UpperCamelCase_     :	Any					     =     block(
														    _SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE			,    encoder_hidden_states=_SCREAMING_SNAKE_CASE			,    encoder_attention_mask=_SCREAMING_SNAKE_CASE			,    timestep=_SCREAMING_SNAKE_CASE			,    cross_attention_kwargs=_SCREAMING_SNAKE_CASE			,    class_labels=_SCREAMING_SNAKE_CASE			,    )

								# 3. Output
								if self.is_input_continuous:
														if not self.use_linear_projection:
																				UpperCamelCase_     :	List[Any]					     =     hidden_states.reshape(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE).permute(0			,    3			,    1			,    2).contiguous()
																				UpperCamelCase_     :	Optional[int]					     =     self.proj_out(_SCREAMING_SNAKE_CASE)
														else:
																				UpperCamelCase_     :	List[str]					     =     self.proj_out(_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	Tuple					     =     hidden_states.reshape(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE).permute(0			,    3			,    1			,    2).contiguous()

														UpperCamelCase_     :	Tuple					     =     hidden_states + residual
								elif self.is_input_vectorized:
														UpperCamelCase_     :	Dict					     =     self.norm_out(_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	List[str]					     =     self.out(_SCREAMING_SNAKE_CASE)
														# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
														UpperCamelCase_     :	List[Any]					     =     logits.permute(0			,    2			,    1)

														# log(p(x_0))
														UpperCamelCase_     :	Optional[Any]					     =     F.log_softmax(logits.double()			,    dim=1).float()
								elif self.is_input_patches:
														# TODO: cleanup!
														UpperCamelCase_     :	int					     =     self.transformer_blocks[0].norma.emb(
														    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    hidden_dtype=hidden_states.dtype)
														UpperCamelCase_     ,UpperCamelCase_     :	Union[str, Any]					     =     self.proj_out_a(F.silu(_SCREAMING_SNAKE_CASE)).chunk(2			,    dim=1)
														UpperCamelCase_     :	Union[str, Any]					     =     self.norm_out(_SCREAMING_SNAKE_CASE) * (1 + scale[:, None]) + shift[:, None]
														UpperCamelCase_     :	Tuple					     =     self.proj_out_a(_SCREAMING_SNAKE_CASE)

														# unpatchify
														UpperCamelCase_     :	int					     =     int(hidden_states.shape[1] ** 0.5)
														UpperCamelCase_     :	Union[str, Any]					     =     hidden_states.reshape(
														    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels))
														UpperCamelCase_     :	Dict					     =     torch.einsum("""nhwpqc->nchpwq"""			,    _SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	List[str]					     =     hidden_states.reshape(
														    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size))

								if not return_dict:
														return (output,)

								return TransformeraDModelOutput(sample=_SCREAMING_SNAKE_CASE)






'''simple docstring'''


__SCREAMING_SNAKE_CASE						=      [chr(i) for i in range(32, 126)]
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(reversed(range(len(alphabets))))
__SCREAMING_SNAKE_CASE						=      []
__SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      0
def 					__magic_name__(       ):
						global gear_one_pos
						global gear_two_pos
						global gear_three_pos
						UpperCamelCase_     :	List[Any]					     =     gear_one[0]
						gear_one.append(UpperCAmelCase__	)
						del gear_one[0]
						gear_one_pos += 1
						if gear_one_pos % int(len(UpperCAmelCase__	)	) == 0:
												UpperCamelCase_     :	List[str]					     =     gear_two[0]
												gear_two.append(UpperCAmelCase__	)
												del gear_two[0]
												gear_two_pos += 1
												if gear_two_pos % int(len(UpperCAmelCase__	)	) == 0:
																		UpperCamelCase_     :	List[Any]					     =     gear_three[0]
																		gear_three.append(UpperCAmelCase__	)
																		del gear_three[0]
																		gear_three_pos += 1
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Any					     =     alphabets.index(UpperCAmelCase__	)
						UpperCamelCase_     :	List[str]					     =     gear_one[target]
						UpperCamelCase_     :	str					     =     gear_two[target]
						UpperCamelCase_     :	int					     =     gear_three[target]
						UpperCamelCase_     :	Union[str, Any]					     =     reflector[target]
						UpperCamelCase_     :	Dict					     =     gear_three.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_two.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_one.index(UpperCAmelCase__	)
						code.append(alphabets[target]	)
						rotator()


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      list(input("Type your message:\n"))
	while True:
		try:
			__SCREAMING_SNAKE_CASE						=      int(input("Please set token:(must be only digits)\n"))
			break
		except Exception as error:
			print(error)
	for _ in range(token):
		rotator()
	for j in decode:
		engine(j)
	print("\n" + "".join(code))
	print(
	    f"\nYour Token is {token} please write it down.\nIf you want to decode "
	    "this message again you should input same digits as token!"
	)




