

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			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_=0.0  ,   A_ = None  ,   A_ = "geglu"  ,   A_ = None  ,   A_ = False  ,   A_ = False  ,   A_ = False  ,   A_ = False  ,   A_ = True  ,   A_ = "layer_norm"  ,   A_ = False  ,   ):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						List[str]							=      only_cross_attention

										_UpperCAmelCase    :						int							=      (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
										_UpperCAmelCase    :						Optional[int]							=      (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:
														_UpperCAmelCase    :						Dict							=      AdaLayerNorm(A_  ,   A_		)
										elif self.use_ada_layer_norm_zero:
														_UpperCAmelCase    :						Optional[int]							=      AdaLayerNormZero(A_  ,   A_		)
										else:
														_UpperCAmelCase    :						Union[str, Any]							=      nn.LayerNorm(A_  ,   elementwise_affine=A_		)
										_UpperCAmelCase    :						Optional[Any]							=      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.
														_UpperCAmelCase    :						Optional[Any]							=      (
														    AdaLayerNorm(A_  ,   A_		)
														    if self.use_ada_layer_norm
														    else nn.LayerNorm(A_  ,   elementwise_affine=A_		)
														)
														_UpperCAmelCase    :						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:
														_UpperCAmelCase    :						int							=      None
														_UpperCAmelCase    :						Dict							=      None

										# 3. Feed-forward
										_UpperCAmelCase    :						int							=      nn.LayerNorm(A_  ,   elementwise_affine=A_		)
										_UpperCAmelCase    :						str							=      FeedForward(A_  ,   dropout=A_  ,   activation_fn=A_  ,   final_dropout=A_		)

										# let chunk size default to None
										_UpperCAmelCase    :						Optional[int]							=      None
										_UpperCAmelCase    :						Optional[int]							=      0


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      chunk_size
										_UpperCAmelCase    :						Tuple							=      dim





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   ):







										'''simple docstring'''


										if self.use_ada_layer_norm:
														_UpperCAmelCase    :						Tuple							=      self.norma(A_  ,   A_		)
										elif self.use_ada_layer_norm_zero:
														_UpperCAmelCase    :						Any							=      self.norma(
														    A_  ,   A_  ,   A_  ,   hidden_dtype=hidden_states.dtype		)
										else:
														_UpperCAmelCase    :						str							=      self.norma(A_		)

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

										_UpperCAmelCase    :						Tuple							=      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:
														_UpperCAmelCase    :						Union[str, Any]							=      gate_msa.unsqueeze(1		) * attn_output
										_UpperCAmelCase    :						Dict							=      attn_output + hidden_states

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

														_UpperCAmelCase    :						Union[str, Any]							=      self.attna(
														    A_  ,   encoder_hidden_states=A_  ,   attention_mask=A_  ,   **A_  ,   )
														_UpperCAmelCase    :						List[Any]							=      attn_output + hidden_states

										# 3. Feed-forward
										_UpperCAmelCase    :						Optional[int]							=      self.norma(A_		)

										if self.use_ada_layer_norm_zero:
														_UpperCAmelCase    :						Optional[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`.'		)

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

										if self.use_ada_layer_norm_zero:
														_UpperCAmelCase    :						str							=      gate_mlp.unsqueeze(1		) * ff_output

										_UpperCAmelCase    :						int							=      ff_output + hidden_states

										return hidden_states







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_ = None  ,   A_ = 4  ,   A_ = 0.0  ,   A_ = "geglu"  ,   A_ = False  ,   ):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						Tuple							=      int(dim * mult		)
										_UpperCAmelCase    :						Dict							=      dim_out if dim_out is not None else dim

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

										_UpperCAmelCase    :						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     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										for module in self.net:
														_UpperCAmelCase    :						int							=      module(A_		)
										return hidden_states







class 			a					(		nn.Module			):


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







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''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     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.proj(A_		)
										_UpperCAmelCase    :						List[Any]							=      self.gelu(A_		)
										return hidden_states







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						List[Any]							=      nn.Linear(A_  ,   dim_out * 2		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''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     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


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







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						int							=      nn.Linear(A_  ,   A_		)





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.proj(A_		)
										return x * torch.sigmoid(1.7_02 * x		)







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						Optional[int]							=      nn.Embedding(A_  ,   A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      nn.SiLU()
										_UpperCAmelCase    :						Optional[int]							=      nn.Linear(A_  ,   embedding_dim * 2		)
										_UpperCAmelCase    :						Optional[int]							=      nn.LayerNorm(A_  ,   elementwise_affine=A_		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


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







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase    :						str							=      CombinedTimestepLabelEmbeddings(A_  ,   A_		)

										_UpperCAmelCase    :						Any							=      nn.SiLU()
										_UpperCAmelCase    :						Union[str, Any]							=      nn.Linear(A_  ,   6 * embedding_dim  ,   bias=A_		)
										_UpperCAmelCase    :						Dict							=      nn.LayerNorm(A_  ,   elementwise_affine=A_  ,   eps=1e-6		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_=None		):







										'''simple docstring'''


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







class 			a					(		nn.Module			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_ = None  ,   A_ = 1e-5		):







										'''simple docstring'''


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

										if act_fn is None:
														_UpperCAmelCase    :						Optional[Any]							=      None
										else:
														_UpperCAmelCase    :						Optional[int]							=      get_activation(A_		)

										_UpperCAmelCase    :						Tuple							=      nn.Linear(A_  ,   out_dim * 2		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if self.act:
														_UpperCAmelCase    :						Any							=      self.act(A_		)
										_UpperCAmelCase    :						Tuple							=      self.linear(A_		)
										_UpperCAmelCase    :						str							=      emb[:, :, None, None]
										_UpperCAmelCase    :						Optional[int]							=      emb.chunk(2  ,   dim=1		)

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





from __future__ import annotations

from random import choice

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[int]:
				return choice(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      random_pivot(lowerCAmelCase	)

				# partition based on pivot
				# linear time
				_UpperCAmelCase    :						List[str]							=      [e for e in lst if e < pivot]
				_UpperCAmelCase    :						Any							=      [e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(lowerCAmelCase	) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(lowerCAmelCase	) < k - 1:
								return kth_number(lowerCAmelCase					, k - len(lowerCAmelCase	) - 1	)
				# pivot is in elements smaller than k
				else:
								return kth_number(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


