

'''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 lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	42



@flax_register_to_config
class lowerCamelCase			(  nn.Module	,						lowercase_	,						lowercase_						):



			'''simple docstring'''






			__snake_case        =	32
			__snake_case        =	4
			__snake_case        =	4
			__snake_case        =	(
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			__snake_case        =	("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
			__snake_case        =	False
			__snake_case        =	(320, 640, 1280, 1280)
			__snake_case        =	2
			__snake_case        =	8
			__snake_case        =	None
			__snake_case        =	1280
			__snake_case        =	0.0
			__snake_case        =	False
			__snake_case        =	jnp.floataa
			__snake_case        =	True
			__snake_case        =	0
			__snake_case        =	False


			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						jax.random.KeyArray			)      ->		FrozenDict:





									'''simple docstring'''
									# init input tensors
									A__					:       Union[str, Any]     =(1, self.in_channels, self.sample_size, self.sample_size)
									A__					:       str     =jnp.zeros(lowerCAmelCase_		,      dtype=jnp.floataa			)
									A__					:       Tuple     =jnp.ones((1,)		,      dtype=jnp.intaa			)
									A__					:       Union[str, Any]     =jnp.zeros((1, 1, self.cross_attention_dim)		,      dtype=jnp.floataa			)

									A__   ,						A__					:       Optional[Any]     =jax.random.split(lowerCAmelCase_			)
									A__					:       Dict     ={"""params""": params_rng, """dropout""": dropout_rng}

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


			def lowercase__						(				self	:						Any			)      ->		str:





									'''simple docstring'''
									A__					:       Tuple     =self.block_out_channels
									A__					:       Dict     =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__					:       Union[str, Any]     =self.num_attention_heads or self.attention_head_dim

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

									# time
									A__					:       Dict     =FlaxTimesteps(
									    block_out_channels[0]		,      flip_sin_to_cos=self.flip_sin_to_cos		,      freq_shift=self.config.freq_shift			)
									A__					:       Union[str, Any]     =FlaxTimestepEmbedding(lowerCAmelCase_		,      dtype=self.dtype			)

									A__					:       Dict     =self.only_cross_attention
									if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			):
															A__					:       Optional[Any]     =(only_cross_attention,) * len(self.down_block_types			)

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

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

															if down_block_type == "CrossAttnDownBlock2D":
																					A__					:       Optional[Any]     =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__					:       List[Any]     =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__					:       List[Any]     =down_blocks

									# mid
									A__					:       List[str]     =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__					:       Dict     =[]
									A__					:       Union[str, Any]     =list(reversed(lowerCAmelCase_			)			)
									A__					:       List[Any]     =list(reversed(lowerCAmelCase_			)			)
									A__					:       Dict     =list(reversed(lowerCAmelCase_			)			)
									A__					:       Optional[Any]     =reversed_block_out_channels[0]
									for i, up_block_type in enumerate(self.up_block_types			):
															A__					:       int     =output_channel
															A__					:       Dict     =reversed_block_out_channels[i]
															A__					:       Dict     =reversed_block_out_channels[min(i + 1		,      len(lowerCAmelCase_			) - 1			)]

															A__					:       str     =i == len(lowerCAmelCase_			) - 1

															if up_block_type == "CrossAttnUpBlock2D":
																					A__					:       int     =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__					:       Dict     =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__					:       Any     =output_channel
									A__					:       Any     =up_blocks

									# out
									A__					:       Optional[int]     =nn.GroupNorm(num_groups=32		,      epsilon=1e-5			)
									A__					:       Union[str, Any]     =nn.Conv(
									    self.out_channels		,      kernel_size=(3, 3)		,      strides=(1, 1)		,      padding=((1, 1), (1, 1))		,      dtype=self.dtype		,      )



			def __call__(				self	:						int		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Any		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						List[Any]=None		,      lowerCAmelCase_	:						Tuple=None		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						bool = False		,      )      ->		Union[FlaxUNetaDConditionOutput, Tuple]:





									'''simple docstring'''
									# 1. time
									if not isinstance(lowerCAmelCase_		,      jnp.ndarray			):
															A__					:       Union[str, Any]     =jnp.array([timesteps]		,      dtype=jnp.intaa			)
									elif isinstance(lowerCAmelCase_		,      jnp.ndarray			) and len(timesteps.shape			) == 0:
															A__					:       int     =timesteps.astype(dtype=jnp.floataa			)
															A__					:       Union[str, Any]     =jnp.expand_dims(lowerCAmelCase_		,      0			)

									A__					:       Optional[Any]     =self.time_proj(lowerCAmelCase_			)
									A__					:       Tuple     =self.time_embedding(lowerCAmelCase_			)

									# 2. pre-process
									A__					:       Optional[int]     =jnp.transpose(lowerCAmelCase_		,      (0, 2, 3, 1)			)
									A__					:       Dict     =self.conv_in(lowerCAmelCase_			)

									# 3. down
									A__					:       Union[str, Any]     =(sample,)
									for down_block in self.down_blocks:
															if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			):
																					A__   ,						A__					:       List[Any]     =down_block(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      deterministic=not train			)
															else:
																					A__   ,						A__					:       Tuple     =down_block(lowerCAmelCase_		,      lowerCAmelCase_		,      deterministic=not train			)
															down_block_res_samples += res_samples

									if down_block_additional_residuals is not None:
															A__					:       Tuple     =()

															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__					:       Union[str, Any]     =new_down_block_res_samples

									# 4. mid
									A__					:       Any     =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__					:       Optional[Any]     =down_block_res_samples[-(self.layers_per_block + 1) :]
															A__					:       List[str]     =down_block_res_samples[: -(self.layers_per_block + 1)]
															if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			):
																					A__					:       List[str]     =up_block(
																					    lowerCAmelCase_		,      temb=lowerCAmelCase_		,      encoder_hidden_states=lowerCAmelCase_		,      res_hidden_states_tuple=lowerCAmelCase_		,      deterministic=not train		,      )
															else:
																					A__					:       Optional[Any]     =up_block(lowerCAmelCase_		,      temb=lowerCAmelCase_		,      res_hidden_states_tuple=lowerCAmelCase_		,      deterministic=not train			)

        # 6. post-process
									A__					:       List[Any]     =self.conv_norm_out(lowerCAmelCase_			)
									A__					:       List[Any]     =nn.silu(lowerCAmelCase_			)
									A__					:       List[str]     =self.conv_out(lowerCAmelCase_			)
									A__					:       List[Any]     =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 lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						float			)      ->		float:





									'''simple docstring'''
									return 0.0


def 						__lowerCamelCase	(       __snake_case					:		np.ndarray,		__snake_case					:		int	)	->							tuple[int | float, int | float]:






						"""simple docstring"""





						A__					:       Tuple     =min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
						A__					:       str     =max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
						return lowest, highest
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





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

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       List[Any]     =np.abs(np.fft.fft(__snake_case	)	)
						A__					:       int     =20 * np.logaa(__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
						A__					:       Union[str, Any]     =get_bounds(__snake_case,		__snake_case	)
						plt.ylim(max([-80, bounds[0]]	),		min([80, bounds[1]]	)	)
						plt.ylabel("""Gain (dB)"""	)

						plt.plot(__snake_case	)
						plt.show()
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       List[Any]     =512
						A__					:       List[Any]     =[1] + [0] * (size - 1)
						A__					:       Dict     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       str     =np.angle(np.fft.fft(__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(__snake_case,		-2 * pi	)	)
						plt.show()

