





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        _a(    __A    ):







		def __init__(    self ,						*__snake_case ,						__snake_case=None ,						__snake_case=None ,						**__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							super().__init__(*__snake_case ,						**__snake_case				)
							_snake_case       :					str				       =   eval_examples
							_snake_case       :					int				       =   post_process_function







		def        lowercase       (    self ,						__snake_case = None ,						__snake_case=None ,						__snake_case = None ,						__snake_case = "eval" ,						**__snake_case ,						)      ->      Dict[str, float]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   gen_kwargs.copy()
							_snake_case       :					Optional[int]				       =   (
							    gen_kwargs["max_length"] if gen_kwargs.get("max_length"				) is not None else self.args.generation_max_length
							)
							_snake_case       :					Optional[int]				       =   (
							    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"				) is not None else self.args.generation_num_beams
							)
							_snake_case       :					str				       =   gen_kwargs

							_snake_case       :					Union[str, Any]				       =   self.eval_dataset if eval_dataset is None else eval_dataset
							_snake_case       :					Any				       =   self.get_eval_dataloader(__snake_case				)
							_snake_case       :					str				       =   self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							_snake_case       :					List[str]				       =   self.compute_metrics
							_snake_case       :					Dict				       =   None
							_snake_case       :					Optional[Any]				       =   time.time()
							_snake_case       :					List[str]				       =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
												_snake_case       :					Any				       =   eval_loop(
												    __snake_case ,						description="Evaluation" ,						prediction_loss_only=True if compute_metrics is None else None ,						ignore_keys=__snake_case ,						metric_key_prefix=__snake_case ,						)
							finally:
												_snake_case       :					List[str]				       =   compute_metrics
							_snake_case       :					str				       =   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(
							        __snake_case ,						__snake_case ,						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
												_snake_case       :					int				       =   self.post_process_function(__snake_case ,						__snake_case ,						__snake_case				)
												_snake_case       :					Union[str, Any]				       =   self.compute_metrics(__snake_case				)

												# Prefix all keys with metric_key_prefix + '_'
												for key in list(metrics.keys()				):
																	if not key.startswith(f"""{metric_key_prefix}_"""				):
																						_snake_case       :					Tuple				       =   metrics.pop(__snake_case				)

												metrics.update(output.metrics				)
							else:
												_snake_case       :					Tuple				       =   output.metrics

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

							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()				)

							_snake_case       :					str				       =   self.callback_handler.on_evaluate(self.args ,						self.state ,						self.control ,						__snake_case				)
							return metrics







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case=None ,						__snake_case = "test" ,						**__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   gen_kwargs.copy()

							_snake_case       :					Tuple				       =   self.get_test_dataloader(__snake_case				)

							# Temporarily disable metric computation, we will do it in the loop here.
							_snake_case       :					List[Any]				       =   self.compute_metrics
							_snake_case       :					List[str]				       =   None
							_snake_case       :					Optional[int]				       =   time.time()
							_snake_case       :					Any				       =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
												_snake_case       :					Union[str, Any]				       =   eval_loop(
												    __snake_case ,						description="Prediction" ,						prediction_loss_only=True if compute_metrics is None else None ,						ignore_keys=__snake_case ,						metric_key_prefix=__snake_case ,						)
							finally:
												_snake_case       :					Optional[int]				       =   compute_metrics

							_snake_case       :					Any				       =   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(
							        __snake_case ,						__snake_case ,						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

							_snake_case       :					List[Any]				       =   self.post_process_function(__snake_case ,						__snake_case ,						__snake_case ,						"predict"				)
							_snake_case       :					Union[str, Any]				       =   self.compute_metrics(__snake_case				)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()				):
												if not key.startswith(f"""{metric_key_prefix}_"""				):
																	_snake_case       :					Optional[int]				       =   metrics.pop(__snake_case				)
							metrics.update(output.metrics				)
							return PredictionOutput(predictions=predictions.predictions ,						label_ids=predictions.label_ids ,						metrics=__snake_case				)






import numpy as np

__lowerCAmelCase :Dict     =    [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class        _a:







		def __init__(    self				)      ->      None:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   np.array(__snake_case				)







		def        lowercase       (    self ,						__snake_case				)      ->      np.ndarray:


							'''simple docstring'''

							_snake_case       , _snake_case       :					List[Any]				       =   np.where(letter == self.SQUARE				)
							_snake_case       :					Any				       =   np.concatenate([indexa + 1, indexa + 1]				)
							return indexes







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					int				       =   self.SQUARE[indexa - 1, indexa - 1]
							return letter







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   message.lower()
							_snake_case       :					List[Any]				       =   message.replace(" " ,						""				)
							_snake_case       :					Any				       =   message.replace("j" ,						"i"				)

							_snake_case       :					Tuple				       =   np.empty((2, len(__snake_case				))				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   self.letter_to_numbers(message[letter_index]				)

												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					List[Any]				       =   numbers[1]

							_snake_case       :					Any				       =   first_step.reshape(2 * len(__snake_case				)				)
							_snake_case       :					Optional[Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Optional[int]				       =   int(second_step[numbers_index * 2]				)
												_snake_case       :					List[str]				       =   int(second_step[(numbers_index * 2) + 1]				)
												_snake_case       :					Any				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					Any				       =   encoded_message + letter

							return encoded_message







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   message.lower()
							message.replace(" " ,						""				)
							_snake_case       :					Tuple				       =   np.empty(2 * len(__snake_case				)				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Union[str, Any]				       =   self.letter_to_numbers(message[letter_index]				)
												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					Any				       =   numbers[1]

							_snake_case       :					Optional[Any]				       =   first_step.reshape((2, len(__snake_case				))				)
							_snake_case       :					Union[str, Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   int(second_step[0, numbers_index]				)
												_snake_case       :					Optional[Any]				       =   int(second_step[1, numbers_index]				)
												_snake_case       :					Union[str, Any]				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					str				       =   decoded_message + letter

							return decoded_message