

from __future__ import annotations

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

_lowerCAmelCase  :      Dict			 =(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowerCAmelCase  :      List[Any]			 =[ord(letter) for letter in string.ascii_lowercase]
_lowerCAmelCase  :      Union[str, Any]			 ={ord(char) for char in VALID_CHARS}

_lowerCAmelCase  :      Any			 =["""the""", """be""", """to""", """of""", """and""", """in""", """that""", """have"""]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= ""
	UpperCAmelCase__:	Optional[Any]									= 4_2
	UpperCAmelCase__:	List[str]									= 4_2
	UpperCAmelCase__:	int									= 4_2

	for keychar, cipherchar in zip(cycle(UpperCAmelCase_      )	,UpperCAmelCase_      ):
		UpperCAmelCase__:	Tuple									= cipherchar ^ keychar
		if decodedchar not in VALID_INTS:
			return None
		decoded += chr(UpperCAmelCase_      )

	return decoded



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Union[str, Any]									= []
	for key in product(UpperCAmelCase_	,repeat=3      ):
		UpperCAmelCase__:	Tuple									= try_key(UpperCAmelCase_	,UpperCAmelCase_      )
		if encoded is not None:
			possibles.append(UpperCAmelCase_      )
	return possibles



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	return [possible for possible in possibles if common_word in possible.lower()]



def 			_A	(      SCREAMING_SNAKE_CASE = "p059_cipher.txt"      ):
	UpperCAmelCase__:	int									= 4_2
	UpperCAmelCase__:	int									= 4_2
	UpperCAmelCase__:	str									= 4_2
	UpperCAmelCase__:	List[str]									= 4_2
	UpperCAmelCase__:	str									= Path(UpperCAmelCase_      ).parent.joinpath(UpperCAmelCase_      ).read_text(encoding="utf-8"      )

	UpperCAmelCase__:	Dict									= [int(UpperCAmelCase_      ) for number in data.strip().split(","      )]

	UpperCAmelCase__:	int									= filter_valid_chars(UpperCAmelCase_      )
	for common_word in COMMON_WORDS:
		UpperCAmelCase__:	List[Any]									= filter_common_word(UpperCAmelCase_	,UpperCAmelCase_      )
		if len(UpperCAmelCase_      ) == 1:
			break

	UpperCAmelCase__:	List[Any]									= possibles[0]
	return sum(ord(UpperCAmelCase_      ) for char in decoded_text      )


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




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase     =							{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							["""PerceiverFeatureExtractor"""]
			UpperCamelCase     =							["""PerceiverImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PerceiverForImageClassificationConvProcessing""",
			    """PerceiverForImageClassificationFourier""",
			    """PerceiverForImageClassificationLearned""",
			    """PerceiverForMaskedLM""",
			    """PerceiverForMultimodalAutoencoding""",
			    """PerceiverForOpticalFlow""",
			    """PerceiverForSequenceClassification""",
			    """PerceiverLayer""",
			    """PerceiverModel""",
			    """PerceiverPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
			from .tokenization_perceiver import PerceiverTokenizer

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_perceiver import PerceiverFeatureExtractor
						from .image_processing_perceiver import PerceiverImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_perceiver import (
						    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PerceiverForImageClassificationConvProcessing,
						    PerceiverForImageClassificationFourier,
						    PerceiverForImageClassificationLearned,
						    PerceiverForMaskedLM,
						    PerceiverForMultimodalAutoencoding,
						    PerceiverForOpticalFlow,
						    PerceiverForSequenceClassification,
						    PerceiverLayer,
						    PerceiverModel,
						    PerceiverPreTrainedModel,
						)

else:
			import sys

			UpperCamelCase     =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




