

import math

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				input("Enter message: ")
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				int(input(f"Enter key [2-{len(_a) - 1}]: "))
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				input("Encryption/Decryption [e/d]: ")

	if mode.lower().startswith("e"):
		SCREAMING_SNAKE_CASE      :					int    =				encrypt_message(_a							,	_a)
	elif mode.lower().startswith("d"):
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				decrypt_message(_a							,	_a)

	# Append pipe symbol (vertical bar) to identify spaces at the end.
	print(f"Output:\n{text + '|'}")

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					Tuple    =				[""] * key
	for col in range(_a):
		SCREAMING_SNAKE_CASE      :					Dict    =				col
		while pointer < len(_a):
			cipher_text[col] += message[pointer]
			pointer += key
	return "".join(_a)

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					Any    =				math.ceil(len(_a) / key)
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				key
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				(num_cols * num_rows) - len(_a)
	SCREAMING_SNAKE_CASE      :					int    =				[""] * num_cols
	SCREAMING_SNAKE_CASE      :					Dict    =				0
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				0

	for symbol in message:
		plain_text[col] += symbol
		col += 1

		if (
		    (col == num_cols)
		    or (col == num_cols - 1)
		    and (row >= num_rows - num_shaded_boxes)
		):
			SCREAMING_SNAKE_CASE      :					int    =				0
			row += 1

	return "".join(_a)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  lowercase	:    int = 16			,  lowercase	:    int = 88			,  lowercase	:    Optional[int] = None			,  lowercase	:    int = 1			,  lowercase	:    float = 0.0			,  lowercase	:    int = 32			,  lowercase	:    Optional[int] = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    str = "geglu"			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowercase			,  attention_head_dim=lowercase			,  in_channels=lowercase			,  num_layers=lowercase			,  dropout=lowercase			,  norm_num_groups=lowercase			,  cross_attention_dim=lowercase			,  attention_bias=lowercase			,  sample_size=lowercase			,  num_vector_embeds=lowercase			,  activation_fn=lowercase			,  num_embeds_ada_norm=lowercase			,  )
				        for _ in range(2	)
				    ]	)

				# Variables that can be set by a pipeline:

				# The ratio of transformer1 to transformer2's output states to be combined during inference
				_snake_case		=			0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				_snake_case		=			[77, 257]

				# Which transformer to use to encode which condition.
				# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
				_snake_case		=			[1, 0]





	def  A							(  self	:    Optional[int]			,  lowercase	:    Optional[int]			,  lowercase	:    List[Any]			,  lowercase	:    List[str]=None			,  lowercase	:    Tuple=None			,  lowercase	:    Dict=None			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			hidden_states

				_snake_case		=			[]
				_snake_case		=			0
				# attention_mask is not used yet
				for i in range(2	):
							# for each of the two transformers, pass the corresponding condition tokens
							_snake_case		=			encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
							_snake_case		=			self.transformer_index_for_condition[i]
							_snake_case		=			self.transformers[transformer_index](
							    lowercase			,  encoder_hidden_states=lowercase			,  timestep=lowercase			,  cross_attention_kwargs=lowercase			,  return_dict=lowercase			,  )[0]
							encoded_states.append(encoded_state - input_states	)
							tokens_start += self.condition_lengths[i]

				_snake_case		=			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				_snake_case		=			output_states + input_states

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowercase	)