



import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__A					( A_					, A_      ):





			@register_to_config
			def __init__(self							,    *,
			    __magic_name__ = 4							,    __magic_name__ = 768							,    __magic_name__							,    __magic_name__							,    ):
						super().__init__()

						lowerCamelCase__ :      str								=		nn.Parameter(torch.zeros(__magic_name__				)				)

						# parameters for additional clip time embeddings
						lowerCamelCase__ :      Tuple								=		nn.Linear(__magic_name__							,    __magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		nn.Linear(__magic_name__							,    __magic_name__				)

						# parameters for encoder hidden states
						lowerCamelCase__ :      Any								=		clip_extra_context_tokens
						lowerCamelCase__ :      List[str]								=		nn.Linear(
						    __magic_name__							,    self.clip_extra_context_tokens * cross_attention_dim				)
						lowerCamelCase__ :      str								=		nn.Linear(__magic_name__							,    __magic_name__				)
						lowerCamelCase__ :      str								=		nn.LayerNorm(__magic_name__				)





			def        _snake_case    (self							,    *, __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									lowerCamelCase__ :      Tuple								=		image_embeddings.shape[0]
									lowerCamelCase__ :      Tuple								=		self.learned_classifier_free_guidance_embeddings.unsqueeze(0				)
									lowerCamelCase__ :      int								=		classifier_free_guidance_embeddings.expand(
									    __magic_name__							,    -1				)
									lowerCamelCase__ :      Optional[int]								=		torch.cat([classifier_free_guidance_embeddings, image_embeddings]							,    dim=0				)

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						lowerCamelCase__ :      Dict								=		prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						lowerCamelCase__ :      Union[str, Any]								=		self.embedding_proj(__magic_name__				)
						lowerCamelCase__ :      Dict								=		self.clip_image_embeddings_project_to_time_embeddings(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						lowerCamelCase__ :      List[Any]								=		self.clip_extra_context_tokens_proj(__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		clip_extra_context_tokens.reshape(__magic_name__							,    -1							,    self.clip_extra_context_tokens				)
						lowerCamelCase__ :      Dict								=		clip_extra_context_tokens.permute(0							,    2							,    1				)

						lowerCamelCase__ :      List[Any]								=		self.encoder_hidden_states_proj(__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		self.text_encoder_hidden_states_norm(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,    dim=1				)

						return text_encoder_hidden_states, additive_clip_time_embeddings








from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

_lowercase     							=						(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowercase     							=						[ord(letter) for letter in string.ascii_lowercase]
_lowercase     							=						{ord(char) for char in VALID_CHARS}

_lowercase     							=						["the", "be", "to", "of", "and", "in", "that", "have"]






def      _A				(UpperCamelCase		:      list[int]      ,			UpperCamelCase		:      tuple[int, ...] )  ->str | None:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		""
			lowerCamelCase__ :      int
			lowerCamelCase__ :      int
			lowerCamelCase__ :      int

			for keychar, cipherchar in zip(cycle(UpperCamelCase )      ,			UpperCamelCase ):
						lowerCamelCase__ :      str								=		cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(UpperCamelCase )

			return decoded






def      _A				(UpperCamelCase		:      list[int] )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      list[str]								=		[]
			for key in product(UpperCamelCase      ,			repeat=3 ):
						lowerCamelCase__ :      Union[str, Any]								=		try_key(UpperCamelCase      ,			UpperCamelCase )
						if encoded is not None:
									possibles.append(UpperCamelCase )
			return possibles






def      _A				(UpperCamelCase		:      list[str]      ,			UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			return [possible for possible in possibles if common_word in possible.lower()]






def      _A				(UpperCamelCase		:      str = "p059_cipher.txt" )  ->int:



			'''simple docstring'''







			lowerCamelCase__ :      list[int]
			lowerCamelCase__ :      list[str]
			lowerCamelCase__ :      str
			lowerCamelCase__ :      str
			lowerCamelCase__ :      str								=		Path(UpperCamelCase ).parent.joinpath(UpperCamelCase ).read_text(encoding="""utf-8""" )

			lowerCamelCase__ :      str								=		[int(UpperCamelCase ) for number in data.strip().split(""",""" )]

			lowerCamelCase__ :      Optional[Any]								=		filter_valid_chars(UpperCamelCase )
			for common_word in COMMON_WORDS:
						lowerCamelCase__ :      Optional[int]								=		filter_common_word(UpperCamelCase      ,			UpperCamelCase )
						if len(UpperCamelCase ) == 1:
									break

			lowerCamelCase__ :      Union[str, Any]								=		possibles[0]
			return sum(ord(UpperCamelCase ) for char in decoded_text )


if __name__ == "__main__":
				print(F'''{solution() = }''')



