


"""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(  lowerCamelCase__		):

		"""simple docstring"""





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

					"""simple docstring"""



					super().__init__(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	eval_examples
					_UpperCamelCase					:Tuple					     =	post_process_function
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "eval"					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Dict[str, float]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	gen_kwargs.copy()
					_UpperCamelCase					:Tuple					     =	(
					    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''   ) is not None else self.args.generation_max_length
					)
					_UpperCamelCase					:List[Any]					     =	(
					    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''   ) is not None else self.args.generation_num_beams
					)
					_UpperCamelCase					:List[str]					     =	gen_kwargs

					_UpperCamelCase					:Tuple					     =	self.eval_dataset if eval_dataset is None else eval_dataset
					_UpperCamelCase					:Tuple					     =	self.get_eval_dataloader(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	self.eval_examples if eval_examples is None else eval_examples

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

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()   ):
											if not key.startswith(f"{metric_key_prefix}_"   ):
														_UpperCamelCase					:List[Any]					     =	metrics.pop(SCREAMING_SNAKE_CASE__   )

								metrics.update(output.metrics   )
					else:
								_UpperCamelCase					:Any					     =	output.metrics

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

					_UpperCamelCase					:List[Any]					     =	self.callback_handler.on_evaluate(self.args					,				self.state					,				self.control					,				SCREAMING_SNAKE_CASE__   )
					return metrics





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__ = "test"					,				**SCREAMING_SNAKE_CASE__   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	gen_kwargs.copy()

					_UpperCamelCase					:int					     =	self.get_test_dataloader(SCREAMING_SNAKE_CASE__   )

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

					_UpperCamelCase					:Optional[int]					     =	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(
					        SCREAMING_SNAKE_CASE__					,				SCREAMING_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

					_UpperCamelCase					:Any					     =	self.post_process_function(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				'''predict'''   )
					_UpperCamelCase					:List[str]					     =	self.compute_metrics(SCREAMING_SNAKE_CASE__   )

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





"""simple docstring"""






from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	float:

					"""simple docstring"""



					return 0.0





def 				A_       ( snake_case__       ,					snake_case__		)					->  tuple[int | float, int | float]:
			_UpperCamelCase					:Union[str, Any]					     =	min([-20, np.min(fft_results[1 : samplerate // 2 - 1]		)]		)
			_UpperCamelCase					:Optional[int]					     =	max([20, np.max(fft_results[1 : samplerate // 2 - 1]		)]		)
			return lowest, highest





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:List[str]					     =	5_12
			_UpperCamelCase					:int					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:Union[str, Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:Any					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:int					     =	np.abs(np.fft.fft(snake_case__		)		)
			_UpperCamelCase					:str					     =	20 * np.logaa(snake_case__		)

			# 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
			_UpperCamelCase					:Tuple					     =	get_bounds(snake_case__       ,					snake_case__		)
			plt.ylim(max([-80, bounds[0]]		)       ,					min([80, bounds[1]]		)		)
			plt.ylabel('''Gain (dB)'''		)

			plt.plot(snake_case__		)
			plt.show()





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:Tuple					     =	5_12
			_UpperCamelCase					:Union[str, Any]					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:List[Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:str					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:Tuple					     =	np.angle(np.fft.fft(snake_case__		)		)

			# 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(snake_case__       ,					-2 * pi		)		)
			plt.show()

