"""simple docstring"""




from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class 						__A							(			SCREAMING_SNAKE_CASE_		):
					_UpperCamelCase							:      torch.FloatTensor







class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_		):






					@register_to_config
					def __init__(		self    ,					a__ = 16    ,					a__ = 88    ,					a__ = None    ,					a__ = None    ,					a__ = 1    ,					a__ = 0.0    ,					a__ = 32    ,					a__ = None    ,					a__ = False    ,					a__ = None    ,					a__ = "geglu"    ,					a__ = True    ,					a__ = True    ,					):
							super().__init__()
							_lowerCAmelCase						:	Optional[Any]        =					num_attention_heads
							_lowerCAmelCase						:	Tuple        =					attention_head_dim
							_lowerCAmelCase						:	Tuple        =					num_attention_heads * attention_head_dim

							_lowerCAmelCase						:	List[str]        =					in_channels

							_lowerCAmelCase						:	Tuple        =					torch.nn.GroupNorm(num_groups=a__    ,					num_channels=a__    ,					eps=1e-6    ,					affine=a__  )
							_lowerCAmelCase						:	int        =					nn.Linear(a__    ,					a__  )

							# 3. Define transformers blocks
							_lowerCAmelCase						:	Tuple        =					nn.ModuleList(
							    [
							        BasicTransformerBlock(
							            a__    ,					a__    ,					a__    ,					dropout=a__    ,					cross_attention_dim=a__    ,					activation_fn=a__    ,					attention_bias=a__    ,					double_self_attention=a__    ,					norm_elementwise_affine=a__    ,					)
							        for d in range(a__  )
							    ]  )

							_lowerCAmelCase						:	Any        =					nn.Linear(a__    ,					a__  )






					def    __A  (		self    ,					a__    ,					a__=None    ,					a__=None    ,					a__=None    ,					a__=1    ,					a__=None    ,					a__ = True    ,					):
							_lowerCAmelCase						:	List[str]        =					hidden_states.shape
							_lowerCAmelCase						:	List[Any]        =					batch_frames // num_frames

							_lowerCAmelCase						:	str        =					hidden_states

							_lowerCAmelCase						:	Tuple        =					hidden_states[None, :].reshape(a__    ,					a__    ,					a__    ,					a__    ,					a__  )
							_lowerCAmelCase						:	Union[str, Any]        =					hidden_states.permute(0    ,					2    ,					1    ,					3    ,					4  )

							_lowerCAmelCase						:	List[Any]        =					self.norm(a__  )
							_lowerCAmelCase						:	Any        =					hidden_states.permute(0    ,					3    ,					4    ,					2    ,					1  ).reshape(batch_size * height * width    ,					a__    ,					a__  )

							_lowerCAmelCase						:	Any        =					self.proj_in(a__  )

							# 2. Blocks
							for block in self.transformer_blocks:
									_lowerCAmelCase						:	List[str]        =					block(
									    a__    ,					encoder_hidden_states=a__    ,					timestep=a__    ,					cross_attention_kwargs=a__    ,					class_labels=a__    ,					)

							# 3. Output
							_lowerCAmelCase						:	Dict        =					self.proj_out(a__  )
							_lowerCAmelCase						:	Optional[Any]        =					(
							    hidden_states[None, None, :]
							    .reshape(a__    ,					a__    ,					a__    ,					a__    ,					a__  )
							    .permute(0    ,					3    ,					4    ,					1    ,					2  )
							    .contiguous()
							)
							_lowerCAmelCase						:	int        =					hidden_states.reshape(a__    ,					a__    ,					a__    ,					a__  )

							_lowerCAmelCase						:	str        =					hidden_states + residual

							if not return_dict:
									return (output,)

							return TransformerTemporalModelOutput(sample=a__  )





"""simple docstring"""




import math





def 							SCREAMING_SNAKE_CASE					(      )	->				None:
  _lowerCAmelCase						:	Any        =					input("""Enter message: """     )
  _lowerCAmelCase						:	List[Any]        =					int(input(f"Enter key [2-{len(_lowerCamelCase     ) - 1}]: "     )     )
  _lowerCAmelCase						:	Optional[Any]        =					input("""Encryption/Decryption [e/d]: """     )

  if mode.lower().startswith("""e"""     ):
    _lowerCAmelCase						:	Tuple        =					encrypt_message(_lowerCamelCase      ,_lowerCamelCase     )
  elif mode.lower().startswith("""d"""     ):
    _lowerCAmelCase						:	Dict        =					decrypt_message(_lowerCamelCase      ,_lowerCamelCase     )

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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Dict        =					[""""""] * key
  for col in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					col
    while pointer < len(_lowerCamelCase     ):
      cipher_text[col] += message[pointer]
      pointer += key
  return "".join(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	str        =					math.ceil(len(_lowerCamelCase     ) / key     )
  _lowerCAmelCase						:	Union[str, Any]        =					key
  _lowerCAmelCase						:	Any        =					(num_cols * num_rows) - len(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[""""""] * num_cols
  _lowerCAmelCase						:	Optional[Any]        =					0
  _lowerCAmelCase						:	Dict        =					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)
    ):
      _lowerCAmelCase						:	str        =					0
      row += 1

  return "".join(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




