
from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class    a_ (			_a    ):






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return 0.0







def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	np.ndarray		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     tuple[int | float, int | float]:
						_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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	FilterType		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     None:
						_lowercase					       =					5_12
						_lowercase					       =					[1] + [0] * (size - 1)
						_lowercase					       =					[filter_type.process(SCREAMING_SNAKE_CASE_       ) for item in inputs]

						_lowercase					       =					[0] * (samplerate - size)  # zero-padding
						outputs += filler
						_lowercase					       =					np.abs(np.fft.fft(SCREAMING_SNAKE_CASE_       )       )
						_lowercase					       =					20 * np.logaa(SCREAMING_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
						_lowercase					       =					get_bounds(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						plt.ylim(max([-80, bounds[0]]       )		,   min([80, bounds[1]]       )       )
						plt.ylabel("""Gain (dB)"""       )

						plt.plot(SCREAMING_SNAKE_CASE_       )
						plt.show()




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	FilterType		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     None:
						_lowercase					       =					5_12
						_lowercase					       =					[1] + [0] * (size - 1)
						_lowercase					       =					[filter_type.process(SCREAMING_SNAKE_CASE_       ) for item in inputs]

						_lowercase					       =					[0] * (samplerate - size)  # zero-padding
						outputs += filler
						_lowercase					       =					np.angle(np.fft.fft(SCREAMING_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(SCREAMING_SNAKE_CASE_		,   -2 * pi       )       )
						plt.show()

from ...processing_utils import ProcessorMixin



class    a_ (			_a    ):
			a	:			Optional[int]					   =							'''SpeechT5FeatureExtractor'''
			a	:			List[Any]					   =							'''SpeechT5Tokenizer'''






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )






			def __call__(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""audio"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""audio_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""sampling_rate"""				,			__UpperCamelCase  )

									if audio is not None and text is not None:
															raise ValueError(
															    """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""  )
									if audio_target is not None and text_target is not None:
															raise ValueError(
															    """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""  )
									if audio is None and audio_target is None and text is None and text_target is None:
															raise ValueError(
															    """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""  )

									if audio is not None:
															_lowercase					       =					self.feature_extractor(__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
									elif text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if audio_target is not None:
															_lowercase					       =					self.feature_extractor(audio_target=__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_values"""]
									elif text_target is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_ids"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""input_values"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""input_ids"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""labels"""				,			__UpperCamelCase  )

									if input_values is not None and input_ids is not None:
															raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""  )
									if input_values is None and input_ids is None and labels is None:
															raise ValueError(
															    """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""  )

									if input_values is not None:
															_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
									elif input_ids is not None:
															_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if labels is not None:
															if "input_ids" in labels or (isinstance(__UpperCamelCase				,			__UpperCamelCase  ) and "input_ids" in labels[0]):
																					_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					targets["""input_ids"""]
															else:
																					_lowercase					       =					self.feature_extractor.feature_size
																					_lowercase					       =					self.feature_extractor.num_mel_bins
																					_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					feature_size_hack
																					_lowercase					       =					targets["""input_values"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )