import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase			:							Optional[int]     =logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       field(default=__lowerCAmelCase ,  metadata={'help': 'Whether to use SortishSampler or not.'}		)
      _snake_case    		=       field(
          default=__lowerCAmelCase ,  metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
      _snake_case    		=       field(
          default=__lowerCAmelCase ,  metadata={
              'help': (
                  'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                  'to the `max_length` value of the model configuration.'
              )
          } ,  )
      _snake_case    		=       field(
          default=__lowerCAmelCase ,  metadata={
              'help': (
                  'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                  'to the `num_beams` value of the model configuration.'
              )
          } ,  )
      _snake_case    		=       field(
          default=__lowerCAmelCase ,  metadata={
              'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
          } ,  )



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						super().to_dict()
          for k, v in d.items():
              if isinstance(_UpperCamelCase    ,  _UpperCamelCase):
                  _lowerCamelCase     :		List[Any] 						=						v.to_dict()
          return d





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				float)				->float:


          """simple docstring"""


          return 0.0







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
    _lowerCamelCase     :		Tuple 						=						max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
    return lowest, highest







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Tuple 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		Optional[Any] 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Tuple 						=						np.abs(np.fft.fft(__A	)	)
    _lowerCamelCase     :		List[Any] 						=						20 * np.logaa(__A	)

    # 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
    _lowerCamelCase     :		Any 						=						get_bounds(__A   , __A	)
    plt.ylim(max([-80, bounds[0]]	)   , min([80, bounds[1]]	)	)
    plt.ylabel("""Gain (dB)"""	)

    plt.plot(__A	)
    plt.show()







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Union[str, Any] 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		int 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Any 						=						np.angle(np.fft.fft(__A	)	)

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




