
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 a__    (       A_,     A_					):






  '''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 a__    (       A_					):






  '''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 a__    (       A_,     A_					):






  '''simple docstring'''





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

def a__    (       A_ = "p059_cipher.txt"					):






  '''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() = }''')



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf

    from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: int    )			->	str:







    """simple docstring"""






    for model_result in results.values():
      for batch_size, sequence_length in zip(model_result["""bs"""]			,							model_result["""ss"""]    ):
        __magic_name__				     =       model_result["""result"""][batch_size][sequence_length]
        self.assertIsNotNone(UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sgugger/tiny-distilbert-classification"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							only_pretrain_model=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Any    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Tuple    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Any    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: List[Any]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result    )
    self.check_results_dict_not_empty(results.memory_train_result    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result    )
    self.check_results_dict_not_empty(results.memory_train_result    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       """patrickvonplaten/t5-tiny-random"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							configs=[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""    )    ) == 0			,							"""Cannot do xla on CPU."""    )
  def 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							use_xla=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    with tempfile.TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       TensorFlowBenchmarkArguments(
          models=[MODEL_ID]			,							inference=UpperCamelCase__			,							save_to_csv=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							inference_time_csv_file=os.path.join(UpperCamelCase__			,							"""inf_time.csv"""    )			,							inference_memory_csv_file=os.path.join(UpperCamelCase__			,							"""inf_mem.csv"""    )			,							env_info_csv_file=os.path.join(UpperCamelCase__			,							"""env.csv"""    )			,							multi_process=UpperCamelCase__			,							)
      __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
      benchmark.run()
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""inf_time.csv"""    )    ).exists()    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""inf_mem.csv"""    )    ).exists()    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""env.csv"""    )    ).exists()    )





  def 							_lowercase							( self			: int    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""

    def _check_summary_is_not_empty(UpperCamelCase__			: Dict    ):
      self.assertTrue(hasattr(UpperCamelCase__			,							"""sequential"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""cumulative"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""current"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""total"""    )    )

    with tempfile.TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       TensorFlowBenchmarkArguments(
          models=[MODEL_ID]			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							log_filename=os.path.join(UpperCamelCase__			,							"""log.txt"""    )			,							log_print=UpperCamelCase__			,							trace_memory_line_by_line=UpperCamelCase__			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
      __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
      __magic_name__				     =       benchmark.run()
      _check_summary_is_not_empty(result.inference_summary    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""log.txt"""    )    ).exists()    )

