

from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings





@maybe_allow_in_graph
class A__    ( nn.Module ):



 """simple docstring"""







 def __init__(		self :	int     ,						A_ :	int     ,						A_ :	int     ,						A_ :	int     ,						A_ :	int=0.0     ,						A_ :	Optional[int] = None     ,						A_ :	str = "geglu"     ,						A_ :	Optional[int] = None     ,						A_ :	bool = False     ,						A_ :	bool = False     ,						A_ :	bool = False     ,						A_ :	bool = False     ,						A_ :	bool = True     ,						A_ :	str = "layer_norm"     ,						A_ :	bool = False     ,						):






      '''simple docstring'''




      super().__init__()
      _lowerCAmelCase   :					Tuple        =    only_cross_attention

      _lowerCAmelCase   :					str        =    (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
      _lowerCAmelCase   :					List[Any]        =    (num_embeds_ada_norm is not None) and norm_type == "ada_norm"

      if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
           raise ValueError(
               F'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
               F''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''       )

      # Define 3 blocks. Each block has its own normalization layer.
      # 1. Self-Attn
      if self.use_ada_layer_norm:
           _lowerCAmelCase   :					List[Any]        =    AdaLayerNorm(A_     ,						A_       )
      elif self.use_ada_layer_norm_zero:
           _lowerCAmelCase   :					List[Any]        =    AdaLayerNormZero(A_     ,						A_       )
      else:
           _lowerCAmelCase   :					Dict        =    nn.LayerNorm(A_     ,						elementwise_affine=A_       )
      _lowerCAmelCase   :					Dict        =    Attention(
          query_dim=A_     ,						heads=A_     ,						dim_head=A_     ,						dropout=A_     ,						bias=A_     ,						cross_attention_dim=cross_attention_dim if only_cross_attention else None     ,						upcast_attention=A_     ,						)

      # 2. Cross-Attn
      if cross_attention_dim is not None or double_self_attention:
           # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
           # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
           # the second cross attention block.
           _lowerCAmelCase   :					List[str]        =    (
               AdaLayerNorm(A_     ,						A_       )
               if self.use_ada_layer_norm
               else nn.LayerNorm(A_     ,						elementwise_affine=A_       )
           )
           _lowerCAmelCase   :					int        =    Attention(
               query_dim=A_     ,						cross_attention_dim=cross_attention_dim if not double_self_attention else None     ,						heads=A_     ,						dim_head=A_     ,						dropout=A_     ,						bias=A_     ,						upcast_attention=A_     ,						)  # is self-attn if encoder_hidden_states is none
      else:
           _lowerCAmelCase   :					List[str]        =    None
           _lowerCAmelCase   :					Optional[int]        =    None

      # 3. Feed-forward
      _lowerCAmelCase   :					str        =    nn.LayerNorm(A_     ,						elementwise_affine=A_       )
      _lowerCAmelCase   :					List[str]        =    FeedForward(A_     ,						dropout=A_     ,						activation_fn=A_     ,						final_dropout=A_       )

      # let chunk size default to None
      _lowerCAmelCase   :					List[str]        =    None
      _lowerCAmelCase   :					int        =    0

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	Optional[int]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    chunk_size
      _lowerCAmelCase   :					Dict        =    dim


 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	torch.FloatTensor     ,						A_ :	Optional[torch.FloatTensor] = None     ,						A_ :	Optional[torch.FloatTensor] = None     ,						A_ :	Optional[torch.FloatTensor] = None     ,						A_ :	Optional[torch.LongTensor] = None     ,						A_ :	Dict[str, Any] = None     ,						A_ :	Optional[torch.LongTensor] = None     ,						):






      '''simple docstring'''




      if self.use_ada_layer_norm:
           _lowerCAmelCase   :					List[Any]        =    self.norma(A_     ,						A_       )
      elif self.use_ada_layer_norm_zero:
           _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    self.norma(
               A_     ,						A_     ,						A_     ,						hidden_dtype=hidden_states.dtype       )
      else:
           _lowerCAmelCase   :					str        =    self.norma(A_       )

      _lowerCAmelCase   :					List[Any]        =    cross_attention_kwargs if cross_attention_kwargs is not None else {}

      _lowerCAmelCase   :					Optional[Any]        =    self.attna(
          A_     ,						encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None     ,						attention_mask=A_     ,						**A_     ,						)
      if self.use_ada_layer_norm_zero:
           _lowerCAmelCase   :					Dict        =    gate_msa.unsqueeze(1       ) * attn_output
      _lowerCAmelCase   :					Optional[Any]        =    attn_output + hidden_states

      # 2. Cross-Attention
      if self.attna is not None:
           _lowerCAmelCase   :					List[str]        =    (
               self.norma(A_     ,						A_       ) if self.use_ada_layer_norm else self.norma(A_       )
           )

           _lowerCAmelCase   :					Tuple        =    self.attna(
               A_     ,						encoder_hidden_states=A_     ,						attention_mask=A_     ,						**A_     ,						)
           _lowerCAmelCase   :					List[str]        =    attn_output + hidden_states

      # 3. Feed-forward
      _lowerCAmelCase   :					Union[str, Any]        =    self.norma(A_       )

      if self.use_ada_layer_norm_zero:
           _lowerCAmelCase   :					Any        =    norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

      if self._chunk_size is not None:
           # "feed_forward_chunk_size" can be used to save memory
           if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                raise ValueError(
                    F'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''       )

           _lowerCAmelCase   :					Optional[Any]        =    norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
           _lowerCAmelCase   :					Any        =    torch.cat(
               [self.ff(A_       ) for hid_slice in norm_hidden_states.chunk(A_     ,						dim=self._chunk_dim       )]     ,						dim=self._chunk_dim     ,						)
      else:
           _lowerCAmelCase   :					Dict        =    self.ff(A_       )

      if self.use_ada_layer_norm_zero:
           _lowerCAmelCase   :					Dict        =    gate_mlp.unsqueeze(1       ) * ff_output

      _lowerCAmelCase   :					Dict        =    ff_output + hidden_states

      return hidden_states





class A__    ( nn.Module ):



 """simple docstring"""







 def __init__(		self :	Union[str, Any]     ,						A_ :	int     ,						A_ :	Optional[int] = None     ,						A_ :	int = 4     ,						A_ :	float = 0.0     ,						A_ :	str = "geglu"     ,						A_ :	bool = False     ,						):






      '''simple docstring'''




      super().__init__()
      _lowerCAmelCase   :					Optional[Any]        =    int(dim * mult       )
      _lowerCAmelCase   :					Union[str, Any]        =    dim_out if dim_out is not None else dim

      if activation_fn == "gelu":
           _lowerCAmelCase   :					Optional[Any]        =    GELU(A_     ,						A_       )
      if activation_fn == "gelu-approximate":
           _lowerCAmelCase   :					List[str]        =    GELU(A_     ,						A_     ,						approximate="tanh"       )
      elif activation_fn == "geglu":
           _lowerCAmelCase   :					List[Any]        =    GEGLU(A_     ,						A_       )
      elif activation_fn == "geglu-approximate":
           _lowerCAmelCase   :					int        =    ApproximateGELU(A_     ,						A_       )

      _lowerCAmelCase   :					Union[str, Any]        =    nn.ModuleList([]       )
      # project in
      self.net.append(A_       )
      # project dropout
      self.net.append(nn.Dropout(A_       )       )
      # project out
      self.net.append(nn.Linear(A_     ,						A_       )       )
      # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
      if final_dropout:
           self.net.append(nn.Dropout(A_       )       )


 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	Dict       ):






      '''simple docstring'''




      for module in self.net:
           _lowerCAmelCase   :					List[Any]        =    module(A_       )
      return hidden_states





class A__    ( nn.Module ):



 """simple docstring"""







 def __init__(		self :	int     ,						A_ :	int     ,						A_ :	int     ,						A_ :	str = "none"       ):






      '''simple docstring'''




      super().__init__()
      _lowerCAmelCase   :					Union[str, Any]        =    nn.Linear(A_     ,						A_       )
      _lowerCAmelCase   :					List[str]        =    approximate

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Any       ):






      '''simple docstring'''




      if gate.device.type != "mps":
           return F.gelu(A_     ,						approximate=self.approximate       )
      # mps: gelu is not implemented for float16
      return F.gelu(gate.to(dtype=torch.floataa       )     ,						approximate=self.approximate       ).to(dtype=gate.dtype       )


 def    __magic_name__       (		self :	int     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.proj(A_       )
      _lowerCAmelCase   :					Dict        =    self.gelu(A_       )
      return hidden_states





class A__    ( nn.Module ):



 """simple docstring"""







 def __init__(		self :	List[Any]     ,						A_ :	int     ,						A_ :	int       ):






      '''simple docstring'''




      super().__init__()
      _lowerCAmelCase   :					Tuple        =    nn.Linear(A_     ,						dim_out * 2       )

 def    __magic_name__       (		self :	Any     ,						A_ :	Dict       ):






      '''simple docstring'''




      if gate.device.type != "mps":
           return F.gelu(A_       )
      # mps: gelu is not implemented for float16
      return F.gelu(gate.to(dtype=torch.floataa       )       ).to(dtype=gate.dtype       )


 def    __magic_name__       (		self :	str     ,						A_ :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    self.proj(A_       ).chunk(2     ,						dim=-1       )
      return hidden_states * self.gelu(A_       )





class A__    ( nn.Module ):



 """simple docstring"""







 def __init__(		self :	Optional[Any]     ,						A_ :	int     ,						A_ :	int       ):






      '''simple docstring'''




      super().__init__()
      _lowerCAmelCase   :					Optional[int]        =    nn.Linear(A_     ,						A_       )


 def    __magic_name__       (		self :	Dict     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.proj(A_       )
      return x * torch.sigmoid(1.702 * x       )





class A__    ( nn.Module ):



 """simple docstring"""







 def __init__(		self :	Dict     ,						A_ :	List[str]     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      super().__init__()
      _lowerCAmelCase   :					List[Any]        =    nn.Embedding(A_     ,						A_       )
      _lowerCAmelCase   :					Dict        =    nn.SiLU()
      _lowerCAmelCase   :					int        =    nn.Linear(A_     ,						embedding_dim * 2       )
      _lowerCAmelCase   :					Optional[int]        =    nn.LayerNorm(A_     ,						elementwise_affine=A_       )


 def    __magic_name__       (		self :	Tuple     ,						A_ :	List[Any]     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.linear(self.silu(self.emb(A_       )       )       )
      _lowerCAmelCase      ,			_lowerCAmelCase   :					Union[str, Any]        =    torch.chunk(A_     ,						2       )
      _lowerCAmelCase   :					Optional[Any]        =    self.norm(A_       ) * (1 + scale) + shift
      return x





class A__    ( nn.Module ):



 """simple docstring"""







 def __init__(		self :	Union[str, Any]     ,						A_ :	str     ,						A_ :	str       ):






      '''simple docstring'''




      super().__init__()

      _lowerCAmelCase   :					Union[str, Any]        =    CombinedTimestepLabelEmbeddings(A_     ,						A_       )

      _lowerCAmelCase   :					Optional[Any]        =    nn.SiLU()
      _lowerCAmelCase   :					Optional[int]        =    nn.Linear(A_     ,						6 * embedding_dim     ,						bias=A_       )
      _lowerCAmelCase   :					List[Any]        =    nn.LayerNorm(A_     ,						elementwise_affine=A_     ,						eps=1E-6       )


 def    __magic_name__       (		self :	str     ,						A_ :	Any     ,						A_ :	Dict     ,						A_ :	int     ,						A_ :	List[str]=None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.linear(self.silu(self.emb(A_     ,						A_     ,						hidden_dtype=A_       )       )       )
      _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					str        =    emb.chunk(6     ,						dim=1       )
      _lowerCAmelCase   :					Any        =    self.norm(A_       ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
      return x, gate_msa, shift_mlp, scale_mlp, gate_mlp





class A__    ( nn.Module ):



 """simple docstring"""







 def __init__(		self :	int     ,						A_ :	int     ,						A_ :	int     ,						A_ :	int     ,						A_ :	Optional[str] = None     ,						A_ :	float = 1E-5       ):






      '''simple docstring'''




      super().__init__()
      _lowerCAmelCase   :					List[Any]        =    num_groups
      _lowerCAmelCase   :					str        =    eps

      if act_fn is None:
           _lowerCAmelCase   :					str        =    None
      else:
           _lowerCAmelCase   :					List[str]        =    get_activation(A_       )

      _lowerCAmelCase   :					Dict        =    nn.Linear(A_     ,						out_dim * 2       )


 def    __magic_name__       (		self :	Dict     ,						A_ :	Any     ,						A_ :	Tuple       ):






      '''simple docstring'''




      if self.act:
           _lowerCAmelCase   :					str        =    self.act(A_       )
      _lowerCAmelCase   :					Dict        =    self.linear(A_       )
      _lowerCAmelCase   :					str        =    emb[:, :, None, None]
      _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    emb.chunk(2     ,						dim=1       )

      _lowerCAmelCase   :					Dict        =    F.group_norm(A_     ,						self.num_groups     ,						eps=self.eps       )
      _lowerCAmelCase   :					Optional[Any]        =    x * (1 + scale) + shift
      return x









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    __magic_name__       (		self :	List[Any]     ,						A_ :	float       ):






      '''simple docstring'''




      return 0.0







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[int | float, int | float]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     _lowerCAmelCase   :					Optional[Any]        =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Optional[Any]        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Dict        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Dict        =    np.abs(np.fft.fft(SCREAMING_SNAKE_CASE				)				)
     _lowerCAmelCase   :					str        =    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
     _lowerCAmelCase   :					Tuple        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Dict        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

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






