

'''simple docstring'''






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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  torch.FloatTensor






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__      ):




  """simple docstring"""




  @register_to_config
  def __init__(      self   :     List[str]					,lowercase__   :     int = 6_5_5_3_6					,lowercase__   :     Optional[int] = None					,lowercase__   :     int = 2					,lowercase__   :     int = 2					,lowercase__   :     int = 0					,lowercase__   :     str = "fourier"					,lowercase__   :     bool = True					,lowercase__   :     bool = False					,lowercase__   :     float = 0.0					,lowercase__   :     Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,lowercase__   :     Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,lowercase__   :     Tuple[str] = "UNetMidBlock1D"					,lowercase__   :     str = None					,lowercase__   :     Tuple[int] = (3_2, 3_2, 6_4)					,lowercase__   :     str = None					,lowercase__   :     int = 8					,lowercase__   :     int = 1					,lowercase__   :     bool = False					,):
   super().__init__()
   __lowercase							  = sample_size

   # time
   if time_embedding_type == "fourier":
    __lowercase							  = GaussianFourierProjection(
        embedding_size=8					,set_W_to_weight=lowercase__					,log=lowercase__					,flip_sin_to_cos=lowercase__			)
    __lowercase							  = 2 * block_out_channels[0]
   elif time_embedding_type == "positional":
    __lowercase							  = Timesteps(
        block_out_channels[0]					,flip_sin_to_cos=lowercase__					,downscale_freq_shift=lowercase__			)
    __lowercase							  = block_out_channels[0]

   if use_timestep_embedding:
    __lowercase							  = block_out_channels[0] * 4
    __lowercase							  = TimestepEmbedding(
        in_channels=lowercase__					,time_embed_dim=lowercase__					,act_fn=lowercase__					,out_dim=block_out_channels[0]					,)

   __lowercase							  = nn.ModuleList([]			)
   __lowercase							  = None
   __lowercase							  = nn.ModuleList([]			)
   __lowercase							  = None

   # down
   __lowercase							  = in_channels
   for i, down_block_type in enumerate(lowercase__			):
    __lowercase							  = output_channel
    __lowercase							  = block_out_channels[i]

    if i == 0:
     input_channel += extra_in_channels

    __lowercase							  = i == len(lowercase__			) - 1

    __lowercase							  = get_down_block(
        lowercase__					,num_layers=lowercase__					,in_channels=lowercase__					,out_channels=lowercase__					,temb_channels=block_out_channels[0]					,add_downsample=not is_final_block or downsample_each_block					,)
    self.down_blocks.append(lowercase__			)

   # mid
   __lowercase							  = get_mid_block(
       lowercase__					,in_channels=block_out_channels[-1]					,mid_channels=block_out_channels[-1]					,out_channels=block_out_channels[-1]					,embed_dim=block_out_channels[0]					,num_layers=lowercase__					,add_downsample=lowercase__					,)

   # up
   __lowercase							  = list(reversed(lowercase__			)			)
   __lowercase							  = reversed_block_out_channels[0]
   if out_block_type is None:
    __lowercase							  = out_channels
   else:
    __lowercase							  = block_out_channels[0]

   for i, up_block_type in enumerate(lowercase__			):
    __lowercase							  = output_channel
    __lowercase							  = (
        reversed_block_out_channels[i + 1] if i < len(lowercase__			) - 1 else final_upsample_channels
    )

    __lowercase							  = i == len(lowercase__			) - 1

    __lowercase							  = get_up_block(
        lowercase__					,num_layers=lowercase__					,in_channels=lowercase__					,out_channels=lowercase__					,temb_channels=block_out_channels[0]					,add_upsample=not is_final_block					,)
    self.up_blocks.append(lowercase__			)
    __lowercase							  = output_channel

   # out
   __lowercase							  = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,3_2			)
   __lowercase							  = get_out_block(
       out_block_type=lowercase__					,num_groups_out=lowercase__					,embed_dim=block_out_channels[0]					,out_channels=lowercase__					,act_fn=lowercase__					,fc_dim=block_out_channels[-1] // 4					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     torch.FloatTensor					,lowercase__   :     Union[torch.Tensor, float, int]					,lowercase__   :     bool = True					,):
   __lowercase							  = timestep
   if not torch.is_tensor(lowercase__			):
    __lowercase							  = torch.tensor([timesteps]					,dtype=torch.long					,device=sample.device			)
   elif torch.is_tensor(lowercase__			) and len(timesteps.shape			) == 0:
    __lowercase							  = timesteps[None].to(sample.device			)

   __lowercase							  = self.time_proj(lowercase__			)
   if self.config.use_timestep_embedding:
    __lowercase							  = self.time_mlp(lowercase__			)
   else:
    __lowercase							  = timestep_embed[..., None]
    __lowercase							  = timestep_embed.repeat([1, 1, sample.shape[2]]			).to(sample.dtype			)
    __lowercase							  = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])			)

   # 2. down
   __lowercase							  = ()
   for downsample_block in self.down_blocks:
    __lowercase  ,	__lowercase							  = downsample_block(hidden_states=lowercase__					,temb=lowercase__			)
    down_block_res_samples += res_samples

   # 3. mid
   if self.mid_block:
    __lowercase							  = self.mid_block(lowercase__					,lowercase__			)

   # 4. up
   for i, upsample_block in enumerate(self.up_blocks			):
    __lowercase							  = down_block_res_samples[-1:]
    __lowercase							  = down_block_res_samples[:-1]
    __lowercase							  = upsample_block(lowercase__					,res_hidden_states_tuple=lowercase__					,temb=lowercase__			)

   # 5. post-process
   if self.out_block:
    __lowercase							  = self.out_block(lowercase__					,lowercase__			)

   if not return_dict:
    return (sample,)

   return UNetaDOutput(sample=lowercase__			)




'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase__											=	{
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase__											=	{'''bert_for_seq_generation''': 512}






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[Any]							=			VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE    :  Dict							=			PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE    :  List[int]							=			[]
  SCREAMING_SNAKE_CASE    :  Any							=			['input_ids', 'attention_mask']






  def __init__(      self   :     Optional[Any]					,lowercase__   :     Tuple					,lowercase__   :     Tuple="<s>"					,lowercase__   :     Union[str, Any]="</s>"					,lowercase__   :     str="<unk>"					,lowercase__   :     Tuple="<pad>"					,lowercase__   :     Union[str, Any]="<::::>"					,lowercase__   :     Optional[Dict[str, Any]] = None					,**lowercase__   :     Any					,):
   __lowercase							  = {} if sp_model_kwargs is None else sp_model_kwargs

   # Add extra_ids to the special token list
   super().__init__(
       bos_token=lowercase__					,eos_token=lowercase__					,unk_token=lowercase__					,pad_token=lowercase__					,sep_token=lowercase__					,sp_model_kwargs=self.sp_model_kwargs					,**lowercase__					,)

   __lowercase							  = vocab_file

   __lowercase							  = spm.SentencePieceProcessor(**self.sp_model_kwargs			)
   self.sp_model.Load(lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   return self.sp_model.get_piece_size()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = {self.convert_ids_to_tokens(lowercase__			): i for i in range(self.vocab_size			)}
   vocab.update(self.added_tokens_encoder			)
   return vocab






  def __getstate__(      self   :     str			):
   __lowercase							  = self.__dict__.copy()
   __lowercase							  = None
   return state






  def __setstate__(      self   :     Optional[int]					,lowercase__   :     Optional[Any]			):
   __lowercase							  = d

   # for backward compatibility
   if not hasattr(self					,'''sp_model_kwargs'''			):
    __lowercase							  = {}

   __lowercase							  = spm.SentencePieceProcessor(**self.sp_model_kwargs			)
   self.sp_model.Load(self.vocab_file			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     str			):
   return self.sp_model.encode(lowercase__					,out_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     Union[str, Any]			):
   return self.sp_model.piece_to_id(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Tuple			):
   __lowercase							  = self.sp_model.IdToPiece(lowercase__			)
   return token






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     int			):
   __lowercase							  = []
   __lowercase							  = ''''''
   for token in tokens:
    # make sure that special tokens are not decoded using sentencepiece model
    if token in self.all_special_tokens:
     out_string += self.sp_model.decode(lowercase__			) + token
     __lowercase							  = []
    else:
     current_sub_tokens.append(lowercase__			)
   out_string += self.sp_model.decode(lowercase__			)
   return out_string.strip()






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     str					,lowercase__   :     Optional[str] = None			):
   if not os.path.isdir(lowercase__			):
    logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
    return
   __lowercase							  = os.path.join(
       lowercase__					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

   if os.path.abspath(self.vocab_file			) != os.path.abspath(lowercase__			) and os.path.isfile(self.vocab_file			):
    copyfile(self.vocab_file					,lowercase__			)
   elif not os.path.isfile(self.vocab_file			):
    with open(lowercase__					,'''wb'''			) as fi:
     __lowercase							  = self.sp_model.serialized_model_proto()
     fi.write(lowercase__			)

   return (out_vocab_file,)

