




'''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 a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     Optional[int]				,							*lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Dict=None				,							lowerCamelCase__ :     Optional[Any]=None				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													super().__init__(*lowerCamelCase__				,							**lowerCamelCase__				)
													__lowercase      =    eval_examples
													__lowercase      =    post_process_function






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     Optional[Dataset] = None				,							lowerCamelCase__ :     Optional[int]=None				,							lowerCamelCase__ :     Optional[List[str]] = None				,							lowerCamelCase__ :     str = "eval"				,							**lowerCamelCase__ :     Dict				,							)    ->						Dict[str, float]:
													"""simple docstring"""


													__lowercase      =    gen_kwargs.copy()
													__lowercase      =    (
													    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''				) is not None else self.args.generation_max_length
													)
													__lowercase      =    (
													    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''				) is not None else self.args.generation_num_beams
													)
													__lowercase      =    gen_kwargs

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

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

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

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






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Optional[int]=None				,							lowerCamelCase__ :     str = "test"				,							**lowerCamelCase__ :     Optional[Any]				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    gen_kwargs.copy()

													__lowercase      =    self.get_test_dataloader(lowerCamelCase__				)

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

													__lowercase      =    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

													__lowercase      =    self.post_process_function(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							'''predict'''				)
													__lowercase      =    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}_'				):
																											__lowercase      =    metrics.pop(lowerCamelCase__				)
													metrics.update(output.metrics				)
													return PredictionOutput(predictions=predictions.predictions				,							label_ids=predictions.label_ids				,							metrics=lowerCamelCase__				)












from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     float				)    ->						float:
													"""simple docstring"""


													return 0.0







def 		_A(       UpperCamelCase__							:			np.ndarray				,       UpperCamelCase__							:			int				)    -> tuple[int | float, int | float]:
							'''simple docstring'''


							__lowercase      =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
							__lowercase      =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
							return lowest, highest







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.abs(np.fft.fft(UpperCamelCase__				)				)
							__lowercase      =    20 * np.logaa(UpperCamelCase__				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							# Display within reasonable bounds
							__lowercase      =    get_bounds(UpperCamelCase__				,       UpperCamelCase__				)
							plt.ylim(max([-80, bounds[0]]				)				,       min([80, bounds[1]]				)				)
							plt.ylabel('''Gain (dB)'''				)

							plt.plot(UpperCamelCase__				)
							plt.show()







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.angle(np.fft.fft(UpperCamelCase__				)				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							plt.ylim(-2 * pi				,       2 * pi				)
							plt.ylabel('''Phase shift (Radians)'''				)
							plt.plot(np.unwrap(UpperCamelCase__				,       -2 * pi				)				)
							plt.show()






