






'''simple docstring'''







import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm
							import torch_xla.debug.metrics as met




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		def __init__(      self   ,*_lowerCAmelCase   ,_lowerCAmelCase=None   ,_lowerCAmelCase=None   ,**_lowerCAmelCase       ):
								super().__init__(*_lowerCAmelCase   ,**_lowerCAmelCase       )
								lowerCamelCase__         =  eval_examples
								lowerCamelCase__         =  post_process_function






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase = None   ,_lowerCAmelCase=None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = "eval"   ,**_lowerCAmelCase   ,):
								lowerCamelCase__         =  gen_kwargs.copy()
								lowerCamelCase__         =  (
								    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""       ) is not None else self.args.generation_max_length
								)
								lowerCamelCase__         =  (
								    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""       ) is not None else self.args.generation_num_beams
								)
								lowerCamelCase__         =  gen_kwargs

								lowerCamelCase__         =  self.eval_dataset if eval_dataset is None else eval_dataset
								lowerCamelCase__         =  self.get_eval_dataloader(_lowerCAmelCase       )
								lowerCamelCase__         =  self.eval_examples if eval_examples is None else eval_examples

								# Temporarily disable metric computation, we will do it in the loop here.
								lowerCamelCase__         =  self.compute_metrics
								lowerCamelCase__         =  None
								lowerCamelCase__         =  time.time()
								lowerCamelCase__         =  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
														lowerCamelCase__         =  eval_loop(
														    _lowerCAmelCase   ,description="""Evaluation"""   ,prediction_loss_only=True if compute_metrics is None else None   ,ignore_keys=_lowerCAmelCase   ,metric_key_prefix=_lowerCAmelCase   ,)
								finally:
														lowerCamelCase__         =  compute_metrics
								lowerCamelCase__         =  self.args.eval_batch_size * self.args.world_size
								if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
														start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
								output.metrics.update(
								    speed_metrics(
								        _lowerCAmelCase   ,_lowerCAmelCase   ,num_samples=output.num_samples   ,num_steps=math.ceil(output.num_samples / total_batch_size       )   ,)       )

								if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
														# Only the main node write the results by default
														lowerCamelCase__         =  self.post_process_function(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       )
														lowerCamelCase__         =  self.compute_metrics(_lowerCAmelCase       )

														# Prefix all keys with metric_key_prefix + '_'
														for key in list(metrics.keys()       ):
																				if not key.startswith(F'''{metric_key_prefix}_'''       ):
																										lowerCamelCase__         =  metrics.pop(_lowerCAmelCase       )

														metrics.update(output.metrics       )
								else:
														lowerCamelCase__         =  output.metrics

								if self.args.should_log:
														# Only the main node log the results by default
														self.log(_lowerCAmelCase       )

								if self.args.tpu_metrics_debug or self.args.debug:
														# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
														xm.master_print(met.metrics_report()       )

								lowerCamelCase__         =  self.callback_handler.on_evaluate(self.args   ,self.state   ,self.control   ,_lowerCAmelCase       )
								return metrics






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase=None   ,_lowerCAmelCase = "test"   ,**_lowerCAmelCase       ):
								lowerCamelCase__         =  gen_kwargs.copy()

								lowerCamelCase__         =  self.get_test_dataloader(_lowerCAmelCase       )

								# Temporarily disable metric computation, we will do it in the loop here.
								lowerCamelCase__         =  self.compute_metrics
								lowerCamelCase__         =  None
								lowerCamelCase__         =  time.time()
								lowerCamelCase__         =  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
														lowerCamelCase__         =  eval_loop(
														    _lowerCAmelCase   ,description="""Prediction"""   ,prediction_loss_only=True if compute_metrics is None else None   ,ignore_keys=_lowerCAmelCase   ,metric_key_prefix=_lowerCAmelCase   ,)
								finally:
														lowerCamelCase__         =  compute_metrics

								lowerCamelCase__         =  self.args.eval_batch_size * self.args.world_size
								if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
														start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
								output.metrics.update(
								    speed_metrics(
								        _lowerCAmelCase   ,_lowerCAmelCase   ,num_samples=output.num_samples   ,num_steps=math.ceil(output.num_samples / total_batch_size       )   ,)       )
								if self.post_process_function is None or self.compute_metrics is None:
														return output

								lowerCamelCase__         =  self.post_process_function(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,"""predict"""       )
								lowerCamelCase__         =  self.compute_metrics(_lowerCAmelCase       )

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()       ):
														if not key.startswith(F'''{metric_key_prefix}_'''       ):
																				lowerCamelCase__         =  metrics.pop(_lowerCAmelCase       )
								metrics.update(output.metrics       )
								return PredictionOutput(predictions=predictions.predictions   ,label_ids=predictions.label_ids   ,metrics=_lowerCAmelCase       )









'''simple docstring'''







from __future__ import annotations

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

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

UpperCamelCase    :      list[str]  			=		["the", "be", "to", "of", "and", "in", "that", "have"]
def 						A__			(				__lowerCAmelCase		:			list[int]   ,    __lowerCAmelCase		:			tuple[int, ...]						):
						lowerCamelCase__         =  ""
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42

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

						return decoded
def 						A__			(				__lowerCAmelCase		:			list[int]						):
						lowerCamelCase__         =  []
						for key in product(__lowerCAmelCase   ,    repeat=3						):
												lowerCamelCase__         =  try_key(__lowerCAmelCase   ,    __lowerCAmelCase						)
												if encoded is not None:
																		possibles.append(__lowerCAmelCase						)
						return possibles
def 						A__			(				__lowerCAmelCase		:			list[str]   ,    __lowerCAmelCase		:			str						):
						return [possible for possible in possibles if common_word in possible.lower()]
def 						A__			(				__lowerCAmelCase		:			str = "p059_cipher.txt"						):
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  Path(__lowerCAmelCase						).parent.joinpath(__lowerCAmelCase						).read_text(encoding="""utf-8"""						)

						lowerCamelCase__         =  [int(__lowerCAmelCase						) for number in data.strip().split(""","""						)]

						lowerCamelCase__         =  filter_valid_chars(__lowerCAmelCase						)
						for common_word in COMMON_WORDS:
												lowerCamelCase__         =  filter_common_word(__lowerCAmelCase   ,    __lowerCAmelCase						)
												if len(__lowerCAmelCase						) == 1:
																		break

						lowerCamelCase__         =  possibles[0]
						return sum(ord(__lowerCAmelCase						) for char in decoded_text						)


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

