






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 						UpperCAmelCase__ (							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       ,):
						super().__init__()
						_A	:				Tuple   =      only_cross_attention

						_A	:				List[Any]   =      (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
						_A	:				Optional[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:
										_A	:				Tuple   =      AdaLayerNorm(A__       ,A__		)
						elif self.use_ada_layer_norm_zero:
										_A	:				Optional[Any]   =      AdaLayerNormZero(A__       ,A__		)
						else:
										_A	:				str   =      nn.LayerNorm(A__       ,elementwise_affine=A__		)
						_A	:				str   =      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.
										_A	:				Dict   =      (
										    AdaLayerNorm(A__       ,A__		)
										    if self.use_ada_layer_norm
										    else nn.LayerNorm(A__       ,elementwise_affine=A__		)
										)
										_A	:				Tuple   =      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:
										_A	:				Optional[int]   =      None
										_A	:				List[Any]   =      None

						# 3. Feed-forward
						_A	:				List[Any]   =      nn.LayerNorm(A__       ,elementwise_affine=A__		)
						_A	:				int   =      FeedForward(A__       ,dropout=A__       ,activation_fn=A__       ,final_dropout=A__		)

						# let chunk size default to None
						_A	:				List[Any]   =      None
						_A	:				List[Any]   =      0







		def  A__      (					self       ,A__       ,A__		):
						# Sets chunk feed-forward
						_A	:				Tuple   =      chunk_size
						_A	:				List[str]   =      dim







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,):
						# Notice that normalization is always applied before the real computation in the following blocks.
						# 1. Self-Attention
						if self.use_ada_layer_norm:
										_A	:				Optional[int]   =      self.norma(A__       ,A__		)
						elif self.use_ada_layer_norm_zero:
										_A  ,				_A  ,				_A  ,				_A  ,				_A	:				List[Any]   =      self.norma(
										    A__       ,A__       ,A__       ,hidden_dtype=hidden_states.dtype		)
						else:
										_A	:				Dict   =      self.norma(A__		)

						_A	:				Dict   =      cross_attention_kwargs if cross_attention_kwargs is not None else {}

						_A	:				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:
										_A	:				List[Any]   =      gate_msa.unsqueeze(1		) * attn_output
						_A	:				List[str]   =      attn_output + hidden_states

						# 2. Cross-Attention
						if self.attna is not None:
										_A	:				int   =      (
										    self.norma(A__       ,A__		) if self.use_ada_layer_norm else self.norma(A__		)
										)

										_A	:				Optional[Any]   =      self.attna(
										    A__       ,encoder_hidden_states=A__       ,attention_mask=A__       ,**A__       ,)
										_A	:				Any   =      attn_output + hidden_states

						# 3. Feed-forward
						_A	:				Tuple   =      self.norma(A__		)

						if self.use_ada_layer_norm_zero:
										_A	:				List[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`."""		)

										_A	:				int   =      norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
										_A	:				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:
										_A	:				Tuple   =      self.ff(A__		)

						if self.use_ada_layer_norm_zero:
										_A	:				Tuple   =      gate_mlp.unsqueeze(1		) * ff_output

						_A	:				Optional[Any]   =      ff_output + hidden_states

						return hidden_states






class 						UpperCAmelCase__ (							nn.Module							):







		def __init__(					self       ,A__       ,A__ = None       ,A__ = 4       ,A__ = 0.0       ,A__ = "geglu"       ,A__ = False       ,):
						super().__init__()
						_A	:				Tuple   =      int(dim * mult		)
						_A	:				Optional[int]   =      dim_out if dim_out is not None else dim

						if activation_fn == "gelu":
										_A	:				Tuple   =      GELU(A__       ,A__		)
						if activation_fn == "gelu-approximate":
										_A	:				Union[str, Any]   =      GELU(A__       ,A__       ,approximate='''tanh'''		)
						elif activation_fn == "geglu":
										_A	:				Optional[Any]   =      GEGLU(A__       ,A__		)
						elif activation_fn == "geglu-approximate":
										_A	:				List[Any]   =      ApproximateGELU(A__       ,A__		)

						_A	:				List[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  A__      (					self       ,A__		):
						for module in self.net:
										_A	:				Tuple   =      module(A__		)
						return hidden_states






class 						UpperCAmelCase__ (							nn.Module							):







		def __init__(					self       ,A__       ,A__       ,A__ = "none"		):
						super().__init__()
						_A	:				Optional[int]   =      nn.Linear(A__       ,A__		)
						_A	:				List[Any]   =      approximate







		def  A__      (					self       ,A__		):
						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  A__      (					self       ,A__		):
						_A	:				List[str]   =      self.proj(A__		)
						_A	:				int   =      self.gelu(A__		)
						return hidden_states






class 						UpperCAmelCase__ (							nn.Module							):







		def __init__(					self       ,A__       ,A__		):
						super().__init__()
						_A	:				Union[str, Any]   =      nn.Linear(A__       ,dim_out * 2		)







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






class 						UpperCAmelCase__ (							nn.Module							):







		def __init__(					self       ,A__       ,A__		):
						super().__init__()
						_A	:				List[str]   =      nn.Linear(A__       ,A__		)







		def  A__      (					self       ,A__		):
						_A	:				Union[str, Any]   =      self.proj(A__		)
						return x * torch.sigmoid(1.7_02 * x		)






class 						UpperCAmelCase__ (							nn.Module							):







		def __init__(					self       ,A__       ,A__		):
						super().__init__()
						_A	:				int   =      nn.Embedding(A__       ,A__		)
						_A	:				Any   =      nn.SiLU()
						_A	:				Dict   =      nn.Linear(A__       ,embedding_dim * 2		)
						_A	:				Tuple   =      nn.LayerNorm(A__       ,elementwise_affine=A__		)







		def  A__      (					self       ,A__       ,A__		):
						_A	:				int   =      self.linear(self.silu(self.emb(A__		)		)		)
						_A  ,				_A	:				Optional[Any]   =      torch.chunk(A__       ,2		)
						_A	:				Optional[int]   =      self.norm(A__		) * (1 + scale) + shift
						return x






class 						UpperCAmelCase__ (							nn.Module							):







		def __init__(					self       ,A__       ,A__		):
						super().__init__()

						_A	:				Optional[int]   =      CombinedTimestepLabelEmbeddings(A__       ,A__		)

						_A	:				Any   =      nn.SiLU()
						_A	:				Tuple   =      nn.Linear(A__       ,6 * embedding_dim       ,bias=A__		)
						_A	:				int   =      nn.LayerNorm(A__       ,elementwise_affine=A__       ,eps=1E-6		)







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






class 						UpperCAmelCase__ (							nn.Module							):







		def __init__(					self       ,A__       ,A__       ,A__       ,A__ = None       ,A__ = 1E-5		):
						super().__init__()
						_A	:				Tuple   =      num_groups
						_A	:				str   =      eps

						if act_fn is None:
										_A	:				Optional[Any]   =      None
						else:
										_A	:				Any   =      get_activation(A__		)

						_A	:				List[Any]   =      nn.Linear(A__       ,out_dim * 2		)







		def  A__      (					self       ,A__       ,A__		):
						if self.act:
										_A	:				int   =      self.act(A__		)
						_A	:				Optional[int]   =      self.linear(A__		)
						_A	:				Tuple   =      emb[:, :, None, None]
						_A  ,				_A	:				Union[str, Any]   =      emb.chunk(2       ,dim=1		)

						_A	:				Tuple   =      F.group_norm(A__       ,self.num_groups       ,eps=self.eps		)
						_A	:				Optional[Any]   =      x * (1 + scale) + shift
						return x











def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> float:

				def get_matched_characters(__lowercase							:str					,  __lowercase							:str   ) -> str:
								_A	:				Union[str, Any]   =      []
								_A	:				Dict   =      min(len(_stra   )					,  len(_stra   )   ) // 2
								for i, l in enumerate(_stra   ):
												_A	:				Tuple   =      int(max(0					,  i - limit   )   )
												_A	:				Union[str, Any]   =      int(min(i + limit + 1					,  len(_stra   )   )   )
												if l in _stra[left:right]:
																matched.append(__lowercase   )
																_A	:				List[Any]   =      f"""{_stra[0:_stra.index(__lowercase   )]} {_stra[_stra.index(__lowercase   ) + 1:]}"""

								return "".join(__lowercase   )

				# matching characters
				_A	:				str   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Optional[Any]   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Any   =      len(__lowercase   )

				# transposition
				_A	:				str   =      (
				    len([(ca, ca) for ca, ca in zip(__lowercase					,  __lowercase   ) if ca != ca]   ) // 2
				)

				if not match_count:
								_A	:				Optional[int]   =      0.0
				else:
								_A	:				str   =      (
								    1
								    / 3
								    * (
								        match_count / len(__lowercase   )
								        + match_count / len(__lowercase   )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				_A	:				Any   =      0
				for ca, ca in zip(stra[:4]					,  stra[:4]   ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(jaro_winkler('hello', 'world'))



