
from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class       __A(							_a					):

  """simple docstring"""







  def 			UpperCAmelCase_   (self			,     SCREAMING_SNAKE_CASE_		):
       return 0.0






def 		__magic_name__					(       __a     :			np.ndarray			,						__a     :			int		):



     '''simple docstring'''




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






def 		__magic_name__					(       __a     :			FilterType			,						__a     :			int		):



     '''simple docstring'''




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

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

     plt.plot(a__		)
     plt.show()






def 		__magic_name__					(       __a     :			FilterType			,						__a     :			int		):



     '''simple docstring'''




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

     UpperCamelCase__												=							[0] * (samplerate - size)  # zero-padding
     outputs += filler
     UpperCamelCase__												=							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()












from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[torch.FloatTensor]      =							None
							snake_case  :   torch.FloatTensor      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None






class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def __init__(    self	,			__lowerCAmelCase=1	,			__lowerCAmelCase=0	,			__lowerCAmelCase=2	,			__lowerCAmelCase=512	,			__lowerCAmelCase="cls"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			**__lowerCAmelCase	,			):
														super().__init__(pad_token_id=__lowerCAmelCase	,			bos_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      project_dim
														UpperCamelCase__					  =      pooler_fn
														UpperCamelCase__					  =      learn_encoder
														UpperCamelCase__					  =      use_attention_mask






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   int      =							[r"""pooler""", r"""logit_scale"""]
							snake_case  :   Tuple      =							[r"""position_ids""", r"""predictions.decoder.bias"""]
							snake_case  :   str      =							"""roberta"""
							snake_case  :   Dict      =							RobertaSeriesConfig






							def __init__(    self	,			__lowerCAmelCase	):
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      XLMRobertaModel(__lowerCAmelCase	)
														UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""has_pre_transformation"""	,			__lowerCAmelCase	)
														if self.has_pre_transformation:
																					UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
																					UpperCamelCase__					  =      nn.LayerNorm(config.hidden_size	,			eps=config.layer_norm_eps	)
														self.post_init()







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict

														UpperCamelCase__					  =      self.base_model(
														    input_ids=__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			token_type_ids=__lowerCAmelCase	,			position_ids=__lowerCAmelCase	,			head_mask=__lowerCAmelCase	,			inputs_embeds=__lowerCAmelCase	,			encoder_hidden_states=__lowerCAmelCase	,			encoder_attention_mask=__lowerCAmelCase	,			output_attentions=__lowerCAmelCase	,			output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,			return_dict=__lowerCAmelCase	,			)

														if self.has_pre_transformation:
																					UpperCamelCase__					  =      outputs["""hidden_states"""][-2]
																					UpperCamelCase__					  =      self.pre_LN(__lowerCAmelCase	)
																					UpperCamelCase__					  =      self.transformation_pre(__lowerCAmelCase	)

																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)
														else:
																					UpperCamelCase__					  =      self.transformation(outputs.last_hidden_state	)
																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)




