






'''simple docstring'''




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 						UpperCamelCase	(			a		,						a			)				->     str | None:






     '''simple docstring'''
     __magic_name__						       =    ''''''
     __magic_name__						       =    42
     __magic_name__						       =    42
     __magic_name__						       =    42

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

     return decoded




def 						UpperCamelCase	(			a			)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     for key in product(a		,						repeat=3			):
          __magic_name__						       =    try_key(a		,						a			)
          if encoded is not None:
               possibles.append(a			)
     return possibles




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






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




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






     '''simple docstring'''
     __magic_name__						       =    42
     __magic_name__						       =    42
     __magic_name__						       =    42
     __magic_name__						       =    42
     __magic_name__						       =    Path(a			).parent.joinpath(a			).read_text(encoding='''utf-8'''			)

     __magic_name__						       =    [int(a			) for number in data.strip().split(''','''			)]

     __magic_name__						       =    filter_valid_chars(a			)
     for common_word in COMMON_WORDS:
          __magic_name__						       =    filter_common_word(a		,						a			)
          if len(a			) == 1:
               break

     __magic_name__						       =    possibles[0]
     return sum(ord(a			) for char in decoded_text			)


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









'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
      import torch






@is_pipeline_test
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 __SCREAMING_SNAKE_CASE					:Any      	=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Tuple			,				a__		:   int			,				a__		:   int      ):
      __magic_name__						       =    TextaTextGenerationPipeline(model=a__			,				tokenizer=a__      )
      return generator, ["Something to write", "Something else"]



 def 			snake_case__  (   self		:   List[str]			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    generator('''Something there'''      )
      self.assertEqual(a__			,				[{'''generated_text''': ANY(a__      )}]      )
      # These are encoder decoder, they don't just append to incoming string
      self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''      )      )

      __magic_name__						       =    generator(['''This is great !''', '''Something else''']			,				num_return_sequences=2			,				do_sample=a__      )
      self.assertEqual(
          a__			,				[
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
          ]			,				)

      __magic_name__						       =    generator(
          ['''This is great !''', '''Something else''']			,				num_return_sequences=2			,				batch_size=2			,				do_sample=a__      )
      self.assertEqual(
          a__			,				[
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
          ]			,				)

      with self.assertRaises(a__      ):
           generator(4      )



 @require_torch
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''pt'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )

      __magic_name__						       =    3
      __magic_name__						       =    generator(
          '''Something there'''			,				num_return_sequences=a__			,				num_beams=a__			,				)
      __magic_name__						       =    [
          {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
          {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
          {'''generated_text''': ''''''},
      ]
      self.assertEqual(a__			,				a__      )

      __magic_name__						       =    generator('''This is a test'''			,				do_sample=a__			,				num_return_sequences=2			,				return_tensors=a__      )
      self.assertEqual(
          a__			,				[
              {'''generated_token_ids''': ANY(torch.Tensor      )},
              {'''generated_token_ids''': ANY(torch.Tensor      )},
          ]			,				)
      __magic_name__						       =    generator.model.config.eos_token_id
      __magic_name__						       =    '''<pad>'''
      __magic_name__						       =    generator(
          ['''This is a test''', '''This is a second test''']			,				do_sample=a__			,				num_return_sequences=2			,				batch_size=2			,				return_tensors=a__			,				)
      self.assertEqual(
          a__			,				[
              [
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
              ],
              [
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
              ],
          ]			,				)



 @require_tf
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''tf'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )






