


from dataclasses import dataclass
from typing import Dict, Optional, Union

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    torch.FloatTensor







class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_      ):
					@register_to_config
					def __init__(      self   :List[str]			,__snake_case   :int = 32			,__snake_case   :int = 64			,__snake_case   :int = 20			,__snake_case   :int = 7_68			,__snake_case   :Optional[int]=77			,__snake_case   :Dict=4			,__snake_case   :float = 0.0			,__snake_case   :str = "silu"			,__snake_case   :Optional[str] = None			,__snake_case   :Optional[str] = None			,__snake_case   :Optional[str] = "linear"			,__snake_case   :Optional[str] = "prd"			,__snake_case   :Optional[int] = None			,__snake_case   :Optional[int] = None			,__snake_case   :Optional[int] = None			,)					->    str:
												super().__init__()
												a__             =     num_attention_heads
												a__             =     attention_head_dim
												a__             =     num_attention_heads * attention_head_dim
												a__             =     additional_embeddings

												a__             =     time_embed_dim or inner_dim
												a__             =     embedding_proj_dim or embedding_dim
												a__             =     clip_embed_dim or embedding_dim

												a__             =     Timesteps(__snake_case			,__snake_case			,0      )
												a__             =     TimestepEmbedding(__snake_case			,__snake_case			,out_dim=__snake_case			,act_fn=__snake_case      )

												a__             =     nn.Linear(__snake_case			,__snake_case      )

												if embedding_proj_norm_type is None:
																			a__             =     None
												elif embedding_proj_norm_type == "layer":
																			a__             =     nn.LayerNorm(__snake_case      )
												else:
																			raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'      )

												a__             =     nn.Linear(__snake_case			,__snake_case      )

												if encoder_hid_proj_type is None:
																			a__             =     None
												elif encoder_hid_proj_type == "linear":
																			a__             =     nn.Linear(__snake_case			,__snake_case      )
												else:
																			raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'      )

												a__             =     nn.Parameter(torch.zeros(1			,num_embeddings + additional_embeddings			,__snake_case      )      )

												if added_emb_type == "prd":
																			a__             =     nn.Parameter(torch.zeros(1			,1			,__snake_case      )      )
												elif added_emb_type is None:
																			a__             =     None
												else:
																			raise ValueError(
																			    F'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'      )

												a__             =     nn.ModuleList(
												    [
												        BasicTransformerBlock(
												            __snake_case			,__snake_case			,__snake_case			,dropout=__snake_case			,activation_fn='gelu'			,attention_bias=__snake_case			,)
												        for d in range(__snake_case      )
												    ]      )

												if norm_in_type == "layer":
																			a__             =     nn.LayerNorm(__snake_case      )
												elif norm_in_type is None:
																			a__             =     None
												else:
																			raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.'      )

												a__             =     nn.LayerNorm(__snake_case      )

												a__             =     nn.Linear(__snake_case			,__snake_case      )

												a__             =     torch.full(
												    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]			,-1_00_00.0      )
												causal_attention_mask.triu_(1      )
												a__             =     causal_attention_mask[None, ...]
												self.register_buffer('causal_attention_mask'			,__snake_case			,persistent=__snake_case      )

												a__             =     nn.Parameter(torch.zeros(1			,__snake_case      )      )
												a__             =     nn.Parameter(torch.zeros(1			,__snake_case      )      )
					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def  lowerCamelCase__(      self   :str      )					->    Dict[str, AttentionProcessor]:
												a__             =     {}

												def fn_recursive_add_processors(__snake_case   :str			,__snake_case   :torch.nn.Module			,__snake_case   :Dict[str, AttentionProcessor]      ):
																			if hasattr(__snake_case			,'set_processor'      ):
																										a__             =     module.processor

																			for sub_name, child in module.named_children():
																										fn_recursive_add_processors(F'{name}.{sub_name}'			,__snake_case			,__snake_case      )

																			return processors

												for name, module in self.named_children():
																			fn_recursive_add_processors(__snake_case			,__snake_case			,__snake_case      )

												return processors
					def  lowerCamelCase__(      self   :int			,__snake_case   :Union[AttentionProcessor, Dict[str, AttentionProcessor]]      )					->    Any:
												a__             =     len(self.attn_processors.keys()      )

												if isinstance(__snake_case			,__snake_case      ) and len(__snake_case      ) != count:
																			raise ValueError(
																			    F'A dict of processors was passed, but the number of processors {len(__snake_case      )} does not match the'
																			    F' number of attention layers: {count}. Please make sure to pass {count} processor classes.'      )

												def fn_recursive_attn_processor(__snake_case   :str			,__snake_case   :torch.nn.Module			,__snake_case   :int      ):
																			if hasattr(__snake_case			,'set_processor'      ):
																										if not isinstance(__snake_case			,__snake_case      ):
																																	module.set_processor(__snake_case      )
																										else:
																																	module.set_processor(processor.pop(F'{name}.processor'      )      )

																			for sub_name, child in module.named_children():
																										fn_recursive_attn_processor(F'{name}.{sub_name}'			,__snake_case			,__snake_case      )

												for name, module in self.named_children():
																			fn_recursive_attn_processor(__snake_case			,__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Optional[int]:
												self.set_attn_processor(AttnProcessor()      )
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :List[Any]			,__snake_case   :Union[torch.Tensor, float, int]			,__snake_case   :torch.FloatTensor			,__snake_case   :Optional[torch.FloatTensor] = None			,__snake_case   :Optional[torch.BoolTensor] = None			,__snake_case   :bool = True			,)					->    Tuple:
												a__             =     hidden_states.shape[0]

												a__             =     timestep
												if not torch.is_tensor(__snake_case      ):
																			a__             =     torch.tensor([timesteps]			,dtype=torch.long			,device=hidden_states.device      )
												elif torch.is_tensor(__snake_case      ) and len(timesteps.shape      ) == 0:
																			a__             =     timesteps[None].to(hidden_states.device      )

												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												a__             =     timesteps * torch.ones(__snake_case			,dtype=timesteps.dtype			,device=timesteps.device      )

												a__             =     self.time_proj(__snake_case      )

												# timesteps does not contain any weights and will always return f32 tensors
												# but time_embedding might be fp16, so we need to cast here.
												a__             =     timesteps_projected.to(dtype=self.dtype      )
												a__             =     self.time_embedding(__snake_case      )

												if self.embedding_proj_norm is not None:
																			a__             =     self.embedding_proj_norm(__snake_case      )

												a__             =     self.embedding_proj(__snake_case      )
												if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																			a__             =     self.encoder_hidden_states_proj(__snake_case      )
												elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
																			raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'      )

												a__             =     self.proj_in(__snake_case      )

												a__             =     self.positional_embedding.to(hidden_states.dtype      )

												a__             =     []
												a__             =     0

												if encoder_hidden_states is not None:
																			additional_embeds.append(__snake_case      )
																			additional_embeddings_len += encoder_hidden_states.shape[1]

												if len(proj_embeddings.shape      ) == 2:
																			a__             =     proj_embeddings[:, None, :]

												if len(hidden_states.shape      ) == 2:
																			a__             =     hidden_states[:, None, :]

												a__             =     additional_embeds + [
												    proj_embeddings,
												    time_embeddings[:, None, :],
												    hidden_states,
												]

												if self.prd_embedding is not None:
																			a__             =     self.prd_embedding.to(hidden_states.dtype      ).expand(__snake_case			,-1			,-1      )
																			additional_embeds.append(__snake_case      )

												a__             =     torch.cat(
												    __snake_case			,dim=1			,)

												# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
												a__             =     additional_embeddings_len + proj_embeddings.shape[1] + 1
												if positional_embeddings.shape[1] < hidden_states.shape[1]:
																			a__             =     F.pad(
																			    __snake_case			,(
																			        0,
																			        0,
																			        additional_embeddings_len,
																			        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
																			    )			,value=0.0			,)

												a__             =     hidden_states + positional_embeddings

												if attention_mask is not None:
																			a__             =     (1 - attention_mask.to(hidden_states.dtype      )) * -1_00_00.0
																			a__             =     F.pad(__snake_case			,(0, self.additional_embeddings)			,value=0.0      )
																			a__             =     (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
																			a__             =     attention_mask.repeat_interleave(self.config.num_attention_heads			,dim=0      )

												if self.norm_in is not None:
																			a__             =     self.norm_in(__snake_case      )

												for block in self.transformer_blocks:
																			a__             =     block(__snake_case			,attention_mask=__snake_case      )

												a__             =     self.norm_out(__snake_case      )

												if self.prd_embedding is not None:
																			a__             =     hidden_states[:, -1]
												else:
																			a__             =     hidden_states[:, additional_embeddings_len:]

												a__             =     self.proj_to_clip_embeddings(__snake_case      )

												if not return_dict:
																			return (predicted_image_embedding,)

												return PriorTransformerOutput(predicted_image_embedding=__snake_case      )



					def  lowerCamelCase__(      self   :int			,__snake_case   :Tuple      )					->    List[Any]:
												a__             =     (prior_latents * self.clip_std) + self.clip_mean
												return prior_latents








def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ):

							def get_matched_characters(__lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ) -> str:
														a__             =     []
														a__             =     min(len(_stra   )					,    len(_stra   )   ) // 2
														for i, l in enumerate(_stra   ):
																					a__             =     int(max(0					,    i - limit   )   )
																					a__             =     int(min(i + limit + 1					,    len(_stra   )   )   )
																					if l in _stra[left:right]:
																												matched.append(__lowerCAmelCase   )
																												a__             =     F'{_stra[0:_stra.index(__lowerCAmelCase   )]} {_stra[_stra.index(__lowerCAmelCase   ) + 1:]}'

														return "".join(__lowerCAmelCase   )

							# matching characters
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     len(__lowerCAmelCase   )

							# transposition
							a__             =     (
							    len([(ca, ca) for ca, ca in zip(__lowerCAmelCase					,    __lowerCAmelCase   ) if ca != ca]   ) // 2
							)

							if not match_count:
														a__             =     0.0
							else:
														a__             =     (
														    1
														    / 3
														    * (
														        match_count / len(__lowerCAmelCase   )
														        + match_count / len(__lowerCAmelCase   )
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							a__             =     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'''))




