
from __future__ import annotations

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

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

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):

      '''simple docstring'''





      UpperCamelCase__    :   Optional[int]					=				''''''
      UpperCamelCase__    :   Tuple					=				4_2
      UpperCamelCase__    :   Union[str, Any]					=				4_2
      UpperCamelCase__    :   List[str]					=				4_2

      for keychar, cipherchar in zip(cycle(UpperCamelCase__		) ,		UpperCamelCase__		):
            UpperCamelCase__    :   Optional[int]					=				cipherchar ^ keychar
            if decodedchar not in VALID_INTS:
                  return None
            decoded += chr(UpperCamelCase__		)

      return decoded


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):

      '''simple docstring'''





      UpperCamelCase__    :   int					=				[]
      for key in product(UpperCamelCase__ ,		repeat=3		):
            UpperCamelCase__    :   Any					=				try_key(UpperCamelCase__ ,		UpperCamelCase__		)
            if encoded is not None:
                  possibles.append(UpperCamelCase__		)
      return possibles


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):

      '''simple docstring'''





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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = "p059_cipher.txt"		):

      '''simple docstring'''





      UpperCamelCase__    :   Tuple					=				4_2
      UpperCamelCase__    :   str					=				4_2
      UpperCamelCase__    :   Optional[int]					=				4_2
      UpperCamelCase__    :   Any					=				4_2
      UpperCamelCase__    :   Tuple					=				Path(UpperCamelCase__		).parent.joinpath(UpperCamelCase__		).read_text(encoding='''utf-8'''		)

      UpperCamelCase__    :   Any					=				[int(UpperCamelCase__		) for number in data.strip().split(''','''		)]

      UpperCamelCase__    :   Any					=				filter_valid_chars(UpperCamelCase__		)
      for common_word in COMMON_WORDS:
            UpperCamelCase__    :   List[str]					=				filter_common_word(UpperCamelCase__ ,		UpperCamelCase__		)
            if len(UpperCamelCase__		) == 1:
                  break

      UpperCamelCase__    :   Optional[Any]					=				possibles[0]
      return sum(ord(UpperCamelCase__		) for char in decoded_text		)


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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase      ={
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =["BlenderbotTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BlenderbotForCausalLM",
        "BlenderbotForConditionalGeneration",
        "BlenderbotModel",
        "BlenderbotPreTrainedModel",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TFBlenderbotForConditionalGeneration",
        "TFBlenderbotModel",
        "TFBlenderbotPreTrainedModel",
    ]


try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "FlaxBlenderbotForConditionalGeneration",
        "FlaxBlenderbotModel",
        "FlaxBlenderbotPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_blenderbot import (
        BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BlenderbotConfig,
        BlenderbotOnnxConfig,
    )
    from .tokenization_blenderbot import BlenderbotTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_blenderbot import (
            BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BlenderbotForCausalLM,
            BlenderbotForConditionalGeneration,
            BlenderbotModel,
            BlenderbotPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_blenderbot import (
            TFBlenderbotForConditionalGeneration,
            TFBlenderbotModel,
            TFBlenderbotPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_blenderbot import (
            FlaxBlenderbotForConditionalGeneration,
            FlaxBlenderbotModel,
            FlaxBlenderbotPreTrainedModel,
        )

else:
    import sys

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


