"""simple docstring"""




from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)
@flax.struct.dataclass
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Optional[int]													=		42






@flax_register_to_config
class     UpperCamelCase__(			nn.Module		,						__A		,						__A ):
						lowerCAmelCase__  :						Tuple													=		32
						lowerCAmelCase__  :						Tuple													=		4
						lowerCAmelCase__  :						Union[str, Any]													=		4
						lowerCAmelCase__  :						Union[str, Any]													=		(
						    'CrossAttnDownBlock2D',
						    'CrossAttnDownBlock2D',
						    'CrossAttnDownBlock2D',
						    'DownBlock2D',
						)
						lowerCAmelCase__  :						List[str]													=		('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D')
						lowerCAmelCase__  :						int													=		False
						lowerCAmelCase__  :						List[str]													=		(320, 640, 1280, 1280)
						lowerCAmelCase__  :						List[str]													=		2
						lowerCAmelCase__  :						Dict													=		8
						lowerCAmelCase__  :						Union[str, Any]													=		None
						lowerCAmelCase__  :						Optional[Any]													=		1280
						lowerCAmelCase__  :						str													=		0.0
						lowerCAmelCase__  :						List[str]													=		False
						lowerCAmelCase__  :						Union[str, Any]													=		jnp.floataa
						lowerCAmelCase__  :						Dict													=		True
						lowerCAmelCase__  :						Union[str, Any]													=		0
						lowerCAmelCase__  :						List[str]													=		False





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[Any]:
													A__	     =							(1, self.in_channels, self.sample_size, self.sample_size)
													A__	     =							jnp.zeros(__lowerCAmelCase		,dtype=jnp.floataa     )
													A__	     =							jnp.ones((1,)		,dtype=jnp.intaa     )
													A__	     =							jnp.zeros((1, 1, self.cross_attention_dim)		,dtype=jnp.floataa     )

													A__     ,  A__	     =							jax.random.split(__lowerCAmelCase     )
													A__	     =							{'params': params_rng, 'dropout': dropout_rng}

													return self.init(__lowerCAmelCase		,__lowerCAmelCase		,__lowerCAmelCase		,__lowerCAmelCase     )["params"]





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							self.block_out_channels
													A__	     =							block_out_channels[0] * 4

													if self.num_attention_heads is not None:
																				raise ValueError(
																				    'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'     )

													# If `num_attention_heads` is not defined (which is the case for most models)
													# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
													# The reason for this behavior is to correct for incorrectly named variables that were introduced
													# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
													# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
													# which is why we correct for the naming here.
													A__	     =							self.num_attention_heads or self.attention_head_dim

													# input
													A__	     =							nn.Conv(
													    block_out_channels[0]		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)

													# time
													A__	     =							FlaxTimesteps(
													    block_out_channels[0]		,flip_sin_to_cos=self.flip_sin_to_cos		,freq_shift=self.config.freq_shift     )
													A__	     =							FlaxTimestepEmbedding(__lowerCAmelCase		,dtype=self.dtype     )

													A__	     =							self.only_cross_attention
													if isinstance(__lowerCAmelCase		,__lowerCAmelCase     ):
																				A__	     =							(only_cross_attention,) * len(self.down_block_types     )

													if isinstance(__lowerCAmelCase		,__lowerCAmelCase     ):
																				A__	     =							(num_attention_heads,) * len(self.down_block_types     )

													# down
													A__	     =							[]
													A__	     =							block_out_channels[0]
													for i, down_block_type in enumerate(self.down_block_types     ):
																				A__	     =							output_channel
																				A__	     =							block_out_channels[i]
																				A__	     =							i == len(__lowerCAmelCase     ) - 1

																				if down_block_type == "CrossAttnDownBlock2D":
																											A__	     =							FlaxCrossAttnDownBlockaD(
																											    in_channels=__lowerCAmelCase		,out_channels=__lowerCAmelCase		,dropout=self.dropout		,num_layers=self.layers_per_block		,num_attention_heads=num_attention_heads[i]		,add_downsample=not is_final_block		,use_linear_projection=self.use_linear_projection		,only_cross_attention=only_cross_attention[i]		,use_memory_efficient_attention=self.use_memory_efficient_attention		,dtype=self.dtype		,)
																				else:
																											A__	     =							FlaxDownBlockaD(
																											    in_channels=__lowerCAmelCase		,out_channels=__lowerCAmelCase		,dropout=self.dropout		,num_layers=self.layers_per_block		,add_downsample=not is_final_block		,dtype=self.dtype		,)

																				down_blocks.append(__lowerCAmelCase     )
													A__	     =							down_blocks

													# mid
													A__	     =							FlaxUNetMidBlockaDCrossAttn(
													    in_channels=block_out_channels[-1]		,dropout=self.dropout		,num_attention_heads=num_attention_heads[-1]		,use_linear_projection=self.use_linear_projection		,use_memory_efficient_attention=self.use_memory_efficient_attention		,dtype=self.dtype		,)

													# up
													A__	     =							[]
													A__	     =							list(reversed(__lowerCAmelCase     )     )
													A__	     =							list(reversed(__lowerCAmelCase     )     )
													A__	     =							list(reversed(__lowerCAmelCase     )     )
													A__	     =							reversed_block_out_channels[0]
													for i, up_block_type in enumerate(self.up_block_types     ):
																				A__	     =							output_channel
																				A__	     =							reversed_block_out_channels[i]
																				A__	     =							reversed_block_out_channels[min(i + 1		,len(__lowerCAmelCase     ) - 1     )]

																				A__	     =							i == len(__lowerCAmelCase     ) - 1

																				if up_block_type == "CrossAttnUpBlock2D":
																											A__	     =							FlaxCrossAttnUpBlockaD(
																											    in_channels=__lowerCAmelCase		,out_channels=__lowerCAmelCase		,prev_output_channel=__lowerCAmelCase		,num_layers=self.layers_per_block + 1		,num_attention_heads=reversed_num_attention_heads[i]		,add_upsample=not is_final_block		,dropout=self.dropout		,use_linear_projection=self.use_linear_projection		,only_cross_attention=only_cross_attention[i]		,use_memory_efficient_attention=self.use_memory_efficient_attention		,dtype=self.dtype		,)
																				else:
																											A__	     =							FlaxUpBlockaD(
																											    in_channels=__lowerCAmelCase		,out_channels=__lowerCAmelCase		,prev_output_channel=__lowerCAmelCase		,num_layers=self.layers_per_block + 1		,add_upsample=not is_final_block		,dropout=self.dropout		,dtype=self.dtype		,)

																				up_blocks.append(__lowerCAmelCase     )
																				A__	     =							output_channel
													A__	     =							up_blocks

													# out
													A__	     =							nn.GroupNorm(num_groups=32		,epsilon=1e-5     )
													A__	     =							nn.Conv(
													    self.out_channels		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)





						def __call__(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase = True		,__UpperCAmelCase = False		,)					->				Dict:







													if not isinstance(__lowerCAmelCase		,jnp.ndarray     ):
																				A__	     =							jnp.array([timesteps]		,dtype=jnp.intaa     )
													elif isinstance(__lowerCAmelCase		,jnp.ndarray     ) and len(timesteps.shape     ) == 0:
																				A__	     =							timesteps.astype(dtype=jnp.floataa     )
																				A__	     =							jnp.expand_dims(__lowerCAmelCase		,0     )

													A__	     =							self.time_proj(__lowerCAmelCase     )
													A__	     =							self.time_embedding(__lowerCAmelCase     )

													# 2. pre-process
													A__	     =							jnp.transpose(__lowerCAmelCase		,(0, 2, 3, 1)     )
													A__	     =							self.conv_in(__lowerCAmelCase     )

													# 3. down
													A__	     =							(sample,)
													for down_block in self.down_blocks:
																				if isinstance(__lowerCAmelCase		,__lowerCAmelCase     ):
																											A__     ,  A__	     =							down_block(__lowerCAmelCase		,__lowerCAmelCase		,__lowerCAmelCase		,deterministic=not train     )
																				else:
																											A__     ,  A__	     =							down_block(__lowerCAmelCase		,__lowerCAmelCase		,deterministic=not train     )
																				down_block_res_samples += res_samples

													if down_block_additional_residuals is not None:
																				A__	     =							()

																				for down_block_res_sample, down_block_additional_residual in zip(
																				    __lowerCAmelCase		,__lowerCAmelCase     ):
																											down_block_res_sample += down_block_additional_residual
																											new_down_block_res_samples += (down_block_res_sample,)

																				A__	     =							new_down_block_res_samples

													# 4. mid
													A__	     =							self.mid_block(__lowerCAmelCase		,__lowerCAmelCase		,__lowerCAmelCase		,deterministic=not train     )

													if mid_block_additional_residual is not None:
																				sample += mid_block_additional_residual

													# 5. up
													for up_block in self.up_blocks:
																				A__	     =							down_block_res_samples[-(self.layers_per_block + 1) :]
																				A__	     =							down_block_res_samples[: -(self.layers_per_block + 1)]
																				if isinstance(__lowerCAmelCase		,__lowerCAmelCase     ):
																											A__	     =							up_block(
																											    __lowerCAmelCase		,temb=__lowerCAmelCase		,encoder_hidden_states=__lowerCAmelCase		,res_hidden_states_tuple=__lowerCAmelCase		,deterministic=not train		,)
																				else:
																											A__	     =							up_block(__lowerCAmelCase		,temb=__lowerCAmelCase		,res_hidden_states_tuple=__lowerCAmelCase		,deterministic=not train     )

        # 6. post-process
													A__	     =							self.conv_norm_out(__lowerCAmelCase     )
													A__	     =							nn.silu(__lowerCAmelCase     )
													A__	     =							self.conv_out(__lowerCAmelCase     )
													A__	     =							jnp.transpose(__lowerCAmelCase		,(0, 3, 1, 2)     )

													if not return_dict:
																				return (sample,)

													return FlaxUNetaDConditionOutput(sample=__lowerCAmelCase     )






"""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()





