



"""simple docstring"""





from __future__ import annotations

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

__lowerCAmelCase       :					str      					=					(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__lowerCAmelCase       :					list[int]      					=					[ord(letter) for letter in string.ascii_lowercase]
__lowerCAmelCase       :					set[int]      					=					{ord(char) for char in VALID_CHARS}

__lowerCAmelCase       :					list[str]      					=					["the", "be", "to", "of", "and", "in", "that", "have"]
def 	__lowerCAmelCase						(		__UpperCamelCase      :			list[int]					, __UpperCamelCase      :			tuple[int, ...]    ):


						'''simple docstring'''






						snake_case_   : str						=			""
						snake_case_   : int
						snake_case_   : int
						snake_case_   : int

						for keychar, cipherchar in zip(cycle(__UpperCamelCase    )					, __UpperCamelCase    ):
												snake_case_   : List[str]						=			cipherchar ^ keychar
												if decodedchar not in VALID_INTS:
																		return None
												decoded += chr(__UpperCamelCase    )

						return decoded
def 	__lowerCAmelCase						(		__UpperCamelCase      :			list[int]    ):


						'''simple docstring'''






						snake_case_   : list[str]						=			[]
						for key in product(__UpperCamelCase					, repeat=3    ):
												snake_case_   : Dict						=			try_key(__UpperCamelCase					, __UpperCamelCase    )
												if encoded is not None:
																		possibles.append(__UpperCamelCase    )
						return possibles
def 	__lowerCAmelCase						(		__UpperCamelCase      :			list[str]					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






						return [possible for possible in possibles if common_word in possible.lower()]
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str = "p059_cipher.txt"    ):


						'''simple docstring'''






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

						snake_case_   : int						=			[int(__UpperCamelCase    ) for number in data.strip().split(""","""    )]

						snake_case_   : Tuple						=			filter_valid_chars(__UpperCamelCase    )
						for common_word in COMMON_WORDS:
												snake_case_   : Optional[int]						=			filter_common_word(__UpperCamelCase					, __UpperCamelCase    )
												if len(__UpperCamelCase    ) == 1:
																		break

						snake_case_   : Optional[int]						=			possibles[0]
						return sum(ord(__UpperCamelCase    ) for char in decoded_text    )


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






"""simple docstring"""





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
					import optax
					from flax.training.common_utils import onehot

					from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
					from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : int						=			FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""   )
								snake_case_   : List[Any]						=			AutoTokenizer.from_pretrained("""google/mt5-small"""   )

								snake_case_   : Dict						=			tokenizer("""Hello there"""							,    return_tensors="""np"""   ).input_ids
								snake_case_   : List[str]						=			tokenizer("""Hi I am"""							,    return_tensors="""np"""   ).input_ids

								snake_case_   : Optional[Any]						=			shift_tokens_right(_lowercase							,    model.config.pad_token_id							,    model.config.decoder_start_token_id   )

								snake_case_   : Tuple						=			model(_lowercase							,    decoder_input_ids=_lowercase   ).logits
								snake_case_   : Tuple						=			optax.softmax_cross_entropy(_lowercase							,    onehot(_lowercase							,    logits.shape[-1]   )   ).mean()

								snake_case_   : List[str]						=			-(labels.shape[-1] * loss.item())

								snake_case_   : Optional[int]						=			-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )

