






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 		_snake_case  (  A__					):






			def __init__(       self		,					*a		,					a=None		,					a=None		,					**a)      ->       str:
						super().__init__(*a		,					**a)
						SCREAMING_SNAKE_CASE						= eval_examples
						SCREAMING_SNAKE_CASE						= post_process_function






			def    SCREAMING_SNAKE_CASE__    (       self		,					a = None		,					a=None		,					a = None		,					a = "eval"		,					**a		,					)      ->       Dict[str, float]:
						SCREAMING_SNAKE_CASE						= gen_kwargs.copy()
						SCREAMING_SNAKE_CASE						= (
						    gen_kwargs['max_length'] if gen_kwargs.get('max_length') is not None else self.args.generation_max_length
						)
						SCREAMING_SNAKE_CASE						= (
						    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams') is not None else self.args.generation_num_beams
						)
						SCREAMING_SNAKE_CASE						= gen_kwargs

						SCREAMING_SNAKE_CASE						= self.eval_dataset if eval_dataset is None else eval_dataset
						SCREAMING_SNAKE_CASE						= self.get_eval_dataloader(a)
						SCREAMING_SNAKE_CASE						= self.eval_examples if eval_examples is None else eval_examples

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

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

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

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

						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())

						SCREAMING_SNAKE_CASE						= self.callback_handler.on_evaluate(self.args		,					self.state		,					self.control		,					a)
						return metrics







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a=None		,					a = "test"		,					**a)      ->       int:
						SCREAMING_SNAKE_CASE						= gen_kwargs.copy()

						SCREAMING_SNAKE_CASE						= self.get_test_dataloader(a)

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

						SCREAMING_SNAKE_CASE						= 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(
						        a		,					a		,					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

						SCREAMING_SNAKE_CASE						= self.post_process_function(a		,					a		,					a		,					'predict')
						SCREAMING_SNAKE_CASE						= self.compute_metrics(a)

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












a_		:						Tuple             =    {
    'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
    'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
    'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
    'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
    '2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
    '8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
    ':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '"': '.-..-.',
    '?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
    '(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
a_		:						List[Any]             =    {value: key for key, value in MORSE_CODE_DICT.items()}






def 				lowerCamelCase__				(_UpperCAmelCase):
			return " ".join(MORSE_CODE_DICT[char] for char in message.upper())






def 				lowerCamelCase__				(_UpperCAmelCase):
			return "".join(REVERSE_DICT[char] for char in message.split())






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 'Morse code here!'
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= encrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= decrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)


if __name__ == "__main__":
				main()




