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 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   42






class 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ):
    @register_to_config
    def __init__(	self			,			lowerCAmelCase = 65536			,			lowerCAmelCase = None			,			lowerCAmelCase = 2			,			lowerCAmelCase = 2			,			lowerCAmelCase = 0			,			lowerCAmelCase = "fourier"			,			lowerCAmelCase = True			,			lowerCAmelCase = False			,			lowerCAmelCase = 0.0			,			lowerCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,			lowerCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,			lowerCAmelCase = "UNetMidBlock1D"			,			lowerCAmelCase = None			,			lowerCAmelCase = (32, 32, 64)			,			lowerCAmelCase = None			,			lowerCAmelCase = 8			,			lowerCAmelCase = 1			,			lowerCAmelCase = False			,			)							->       str:
     super().__init__()
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   sample_size

     # time
     if time_embedding_type == "fourier":
      SCREAMING_SNAKE_CASE__: List[Any]=   GaussianFourierProjection(
          embedding_size=8			,			set_W_to_weight=lowerCAmelCase			,			log=lowerCAmelCase			,			flip_sin_to_cos=lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: int=   2 * block_out_channels[0]
     elif time_embedding_type == "positional":
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   Timesteps(
          block_out_channels[0]			,			flip_sin_to_cos=lowerCAmelCase			,			downscale_freq_shift=lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: List[str]=   block_out_channels[0]

     if use_timestep_embedding:
      SCREAMING_SNAKE_CASE__: Optional[int]=   block_out_channels[0] * 4
      SCREAMING_SNAKE_CASE__: Tuple=   TimestepEmbedding(
          in_channels=lowerCAmelCase			,			time_embed_dim=lowerCAmelCase			,			act_fn=lowerCAmelCase			,			out_dim=block_out_channels[0]			,			)

     SCREAMING_SNAKE_CASE__: Any=   nn.ModuleList([] )
     SCREAMING_SNAKE_CASE__: int=   None
     SCREAMING_SNAKE_CASE__: Optional[Any]=   nn.ModuleList([] )
     SCREAMING_SNAKE_CASE__: List[str]=   None

     # down
     SCREAMING_SNAKE_CASE__: Optional[Any]=   in_channels
     for i, down_block_type in enumerate(lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Dict=   output_channel
      SCREAMING_SNAKE_CASE__: str=   block_out_channels[i]

      if i == 0:
       input_channel += extra_in_channels

      SCREAMING_SNAKE_CASE__: Any=   i == len(lowerCAmelCase ) - 1

      SCREAMING_SNAKE_CASE__: Tuple=   get_down_block(
          lowerCAmelCase			,			num_layers=lowerCAmelCase			,			in_channels=lowerCAmelCase			,			out_channels=lowerCAmelCase			,			temb_channels=block_out_channels[0]			,			add_downsample=not is_final_block or downsample_each_block			,			)
      self.down_blocks.append(lowerCAmelCase )

     # mid
     SCREAMING_SNAKE_CASE__: Optional[int]=   get_mid_block(
         lowerCAmelCase			,			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=lowerCAmelCase			,			add_downsample=lowerCAmelCase			,			)

     # up
     SCREAMING_SNAKE_CASE__: Any=   list(reversed(lowerCAmelCase ) )
     SCREAMING_SNAKE_CASE__: List[str]=   reversed_block_out_channels[0]
     if out_block_type is None:
      SCREAMING_SNAKE_CASE__: str=   out_channels
     else:
      SCREAMING_SNAKE_CASE__: str=   block_out_channels[0]

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

      SCREAMING_SNAKE_CASE__: Union[str, Any]=   i == len(lowerCAmelCase ) - 1

      SCREAMING_SNAKE_CASE__: List[str]=   get_up_block(
          lowerCAmelCase			,			num_layers=lowerCAmelCase			,			in_channels=lowerCAmelCase			,			out_channels=lowerCAmelCase			,			temb_channels=block_out_channels[0]			,			add_upsample=not is_final_block			,			)
      self.up_blocks.append(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: List[str]=   output_channel

     # out
     SCREAMING_SNAKE_CASE__: Any=   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,			32 )
     SCREAMING_SNAKE_CASE__: int=   get_out_block(
         out_block_type=lowerCAmelCase			,			num_groups_out=lowerCAmelCase			,			embed_dim=block_out_channels[0]			,			out_channels=lowerCAmelCase			,			act_fn=lowerCAmelCase			,			fc_dim=block_out_channels[-1] // 4			,			)



    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = True			,			)							->       Union[UNetaDOutput, Tuple]:
     SCREAMING_SNAKE_CASE__: Dict=   timestep
     if not torch.is_tensor(lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Dict=   torch.tensor([timesteps]			,			dtype=torch.long			,			device=sample.device )
     elif torch.is_tensor(lowerCAmelCase ) and len(timesteps.shape ) == 0:
      SCREAMING_SNAKE_CASE__: Optional[int]=   timesteps[None].to(sample.device )

     SCREAMING_SNAKE_CASE__: Dict=   self.time_proj(lowerCAmelCase )
     if self.config.use_timestep_embedding:
      SCREAMING_SNAKE_CASE__: Any=   self.time_mlp(lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   timestep_embed[..., None]
      SCREAMING_SNAKE_CASE__: Dict=   timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
      SCREAMING_SNAKE_CASE__: Tuple=   timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

     # 2. down
     SCREAMING_SNAKE_CASE__: Optional[Any]=   ()
     for downsample_block in self.down_blocks:
      SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[Any]=   downsample_block(hidden_states=lowerCAmelCase			,			temb=lowerCAmelCase )
      down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
      SCREAMING_SNAKE_CASE__: List[Any]=   self.mid_block(lowerCAmelCase			,			lowerCAmelCase )

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks ):
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   down_block_res_samples[-1:]
      SCREAMING_SNAKE_CASE__: Optional[Any]=   down_block_res_samples[:-1]
      SCREAMING_SNAKE_CASE__: int=   upsample_block(lowerCAmelCase			,			res_hidden_states_tuple=lowerCAmelCase			,			temb=lowerCAmelCase )

     # 5. post-process
     if self.out_block:
      SCREAMING_SNAKE_CASE__: Any=   self.out_block(lowerCAmelCase			,			lowerCAmelCase )

     if not return_dict:
      return (sample,)

     return UNetaDOutput(sample=lowerCAmelCase )






'''simple docstring'''





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A__    :   Tuple           =					logging.get_logger(__name__)

A__    :   int           =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

A__    :   int           =					{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

A__    :   List[str]           =					{
    '''camembert-base''': 5_1_2,
}

A__    :   str           =					'''▁'''



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= ['''input_ids''', '''attention_mask''']
 def __init__(   self						:							List[Any]      ,     __a						:							int      ,     __a						:							str="<s>"      ,     __a						:							Dict="</s>"      ,     __a						:							Union[str, Any]="</s>"      ,     __a						:							List[str]="<s>"      ,     __a						:							Optional[int]="<unk>"      ,     __a						:							Optional[Any]="<pad>"      ,     __a						:							str="<mask>"      ,     __a						:							Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]      ,     __a						:							Optional[Dict[str, Any]] = None      ,     **__a						:							Any      ,     )      ->   None:







       '''simple docstring'''
       # Mask token behave like a normal word, i.e. include the space before it
       __snake_case :   Optional[int]													=    AddedToken(__a      ,     lstrip=__a      ,     rstrip=__a  ) if isinstance(__a      ,     __a  ) else mask_token

       __snake_case :   Optional[Any]													=    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__a      ,     eos_token=__a      ,     unk_token=__a      ,     sep_token=__a      ,     cls_token=__a      ,     pad_token=__a      ,     mask_token=__a      ,     additional_special_tokens=__a      ,     sp_model_kwargs=self.sp_model_kwargs      ,     **__a      ,     )
       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(__a  )  )
       __snake_case :   str													=    vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       __snake_case :   Any													=    {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       __snake_case :   Optional[int]													=    len(self.fairseq_tokens_to_ids  )
       __snake_case :   str													=    len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
       __snake_case :   Dict													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def A_   (   self						:							List[str]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''

       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __snake_case :   Dict													=    [self.cls_token_id]
       __snake_case :   Optional[int]													=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None      ,     __a						:							bool = False  )      ->   List[int]:







       '''simple docstring'''
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__a      ,     token_ids_a=__a      ,     already_has_special_tokens=__a  )

       if token_ids_a is None:
             return [1] + ([0] * len(__a  )) + [1]
       return [1] + ([0] * len(__a  )) + [1, 1] + ([0] * len(__a  )) + [1]
 def A_   (   self						:							int      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    [self.sep_token_id]
       __snake_case :   Optional[int]													=    [self.cls_token_id]

       if token_ids_a is None:
             return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]
 @property
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )
 def A_   (   self						:							List[str]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Any													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def A_   (   self						:							Dict      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[str]  )      ->   List[Any]:







       '''simple docstring'''
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(__a  ) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset  )
 def A_   (   self						:							Dict      ,     __a						:							Union[str, Any]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    []
       __snake_case :   List[Any]													=    ''
       __snake_case :   Optional[int]													=    False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(__a  ) + token
                   __snake_case :   Optional[int]													=    True
                   __snake_case :   Any													=    []
             else:
                   current_sub_tokens.append(__a  )
                   __snake_case :   Dict													=    False
       out_string += self.sp_model.decode(__a  )
       return out_string.strip()
 def __getstate__(   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    self.__dict__.copy()
       __snake_case :   int													=    None
       return state
 def __setstate__(   self						:							List[str]      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[str]													=    d

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

       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )





 def A_   (   self						:							Tuple      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







       '''simple docstring'''
       if not os.path.isdir(__a  ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
       __snake_case :   Dict													=    os.path.join(
           __a      ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(__a  ) and os.path.isfile(self.vocab_file  ):
             copyfile(self.vocab_file      ,     __a  )
       elif not os.path.isfile(self.vocab_file  ):
             with open(__a      ,     'wb'  ) as fi:
                   __snake_case :   Union[str, Any]													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (out_vocab_file,)



