


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   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		42






class   A__ (   snake_case__	, snake_case__     ):







    """simple docstring"""

    @register_to_config
    def __init__(	self      , __snake_case = 3_2      , __snake_case = 6_4      , __snake_case = 2_0      , __snake_case = 7_6_8      , __snake_case=7_7      , __snake_case=4      , __snake_case = 0.0      , __snake_case = "silu"      , __snake_case = None      , __snake_case = None      , __snake_case = "linear"      , __snake_case = "prd"      , __snake_case = None      , __snake_case = None      , __snake_case = None      , ):
         super().__init__()
         snake_case					=    num_attention_heads
         snake_case					=    attention_head_dim
         snake_case					=    num_attention_heads * attention_head_dim
         snake_case					=    additional_embeddings

         snake_case					=    time_embed_dim or inner_dim
         snake_case					=    embedding_proj_dim or embedding_dim
         snake_case					=    clip_embed_dim or embedding_dim

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

         snake_case					=    nn.Linear(__snake_case      , __snake_case   )

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

         snake_case					=    nn.Linear(__snake_case      , __snake_case   )

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

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

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

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

         snake_case					=    nn.LayerNorm(__snake_case   )

         snake_case					=    nn.Linear(__snake_case      , __snake_case   )

         snake_case					=    torch.full(
             [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]      , -1_0000.0   )
         causal_attention_mask.triu_(1   )
         snake_case					=    causal_attention_mask[None, ...]
         self.register_buffer('''causal_attention_mask'''      , __snake_case      , persistent=__snake_case   )

         snake_case					=    nn.Parameter(torch.zeros(1      , __snake_case   )   )
         snake_case					=    nn.Parameter(torch.zeros(1      , __snake_case   )   )
    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def 		a_      (	self   ):
         snake_case					=    {}

         def fn_recursive_add_processors(__snake_case      , __snake_case      , __snake_case   ):
              if hasattr(__snake_case      , '''set_processor'''   ):
                   snake_case					=    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 		a_      (	self      , __snake_case   ):
         snake_case					=    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      , __snake_case      , __snake_case   ):
              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 		a_      (	self   ):
         self.set_attn_processor(AttnProcessor()   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case = None      , __snake_case = None      , __snake_case = True      , ):
         snake_case					=    hidden_states.shape[0]

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

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

         snake_case					=    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.
         snake_case					=    timesteps_projected.to(dtype=self.dtype   )
         snake_case					=    self.time_embedding(__snake_case   )

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

         snake_case					=    self.embedding_proj(__snake_case   )
         if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
              snake_case					=    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'''   )

         snake_case					=    self.proj_in(__snake_case   )

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

         snake_case					=    []
         snake_case					=    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:
              snake_case					=    proj_embeddings[:, None, :]

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

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

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

         snake_case					=    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
         snake_case					=    additional_embeddings_len + proj_embeddings.shape[1] + 1
         if positional_embeddings.shape[1] < hidden_states.shape[1]:
              snake_case					=    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      , )

         snake_case					=    hidden_states + positional_embeddings

         if attention_mask is not None:
              snake_case					=    (1 - attention_mask.to(hidden_states.dtype   )) * -1_0000.0
              snake_case					=    F.pad(__snake_case      , (0, self.additional_embeddings)      , value=0.0   )
              snake_case					=    (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype   )
              snake_case					=    attention_mask.repeat_interleave(self.config.num_attention_heads      , dim=0   )

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

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

         snake_case					=    self.norm_out(__snake_case   )

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

         snake_case					=    self.proj_to_clip_embeddings(__snake_case   )

         if not return_dict:
              return (predicted_image_embedding,)

         return PriorTransformerOutput(predicted_image_embedding=__snake_case   )




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    (prior_latents * self.clip_std) + self.clip_mean
         return prior_latents








import re

from filelock import FileLock


try:
 import nltk

 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   True
except (ImportError, ModuleNotFoundError):
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   False

if NLTK_AVAILABLE:
 with FileLock(".lock") as lock:
  nltk.download("punkt", quiet=True)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     re.sub('''<n>'''							,''''''							,UpperCamelCase_    )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(UpperCamelCase_    )    )




