"""simple docstring"""




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class     UpperCamelCase__(			nn.Module ):





						def __init__(       self		,__UpperCAmelCase = 16		,__UpperCAmelCase = 88		,__UpperCAmelCase = None		,__UpperCAmelCase = 1		,__UpperCAmelCase = 0.0		,__UpperCAmelCase = 32		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = "geglu"		,__UpperCAmelCase = None		,)					->				List[str]:
													super().__init__()
													A__	     =							nn.ModuleList(
													    [
													        TransformeraDModel(
													            num_attention_heads=__UpperCamelCase		,attention_head_dim=__UpperCamelCase		,in_channels=__UpperCamelCase		,num_layers=__UpperCamelCase		,dropout=__UpperCamelCase		,norm_num_groups=__UpperCamelCase		,cross_attention_dim=__UpperCamelCase		,attention_bias=__UpperCamelCase		,sample_size=__UpperCamelCase		,num_vector_embeds=__UpperCamelCase		,activation_fn=__UpperCamelCase		,num_embeds_ada_norm=__UpperCamelCase		,)
													        for _ in range(2     )
													    ]     )

													# Variables that can be set by a pipeline:

													# The ratio of transformer1 to transformer2's output states to be combined during inference
													A__	     =							0.5

													# The shape of `encoder_hidden_states` is expected to be
													# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
													A__	     =							[77, 2_57]

													# Which transformer to use to encode which condition.
													# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
													A__	     =							[1, 0]





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase = True		,)					->				Optional[Any]:
													A__	     =							hidden_states

													A__	     =							[]
													A__	     =							0
													# attention_mask is not used yet
													for i in range(2     ):
																				# for each of the two transformers, pass the corresponding condition tokens
																				A__	     =							encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																				A__	     =							self.transformer_index_for_condition[i]
																				A__	     =							self.transformers[transformer_index](
																				    __UpperCamelCase		,encoder_hidden_states=__UpperCamelCase		,timestep=__UpperCamelCase		,cross_attention_kwargs=__UpperCamelCase		,return_dict=__UpperCamelCase		,)[0]
																				encoded_states.append(encoded_state - input_states     )
																				tokens_start += self.condition_lengths[i]

													A__	     =							encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
													A__	     =							output_states + input_states

													if not return_dict:
																				return (output_states,)

													return TransformeraDModelOutput(sample=__UpperCamelCase     )






"""simple docstring"""




from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np
class     UpperCamelCase__(			__A ):





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				float:
													return 0.0






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

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

							plt.plot(UpperCamelCase__				)
							plt.show()






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

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





