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


snake_case__				= logging.get_logger(__name__)

snake_case__				= {"vocab_file": "sentencepiece.bpe.model"}

snake_case__				= {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

snake_case__				= {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

snake_case__				= "▁"



class  UpperCamelCase				( _UpperCAmelCase ):

     '''simple docstring'''

     A_										=  VOCAB_FILES_NAMES
     A_										=  PRETRAINED_VOCAB_FILES_MAP
     A_										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_										=  ['''input_ids''', '''attention_mask''']



     def __init__(					self				,				A_				,				A_="<s>"				,				A_="</s>"				,				A_="</s>"				,				A_="<s>"				,				A_="<unk>"				,				A_="<pad>"				,				A_="<mask>"				,				A_ = None				,				**A_				,				) ->				None:






           """simple docstring"""
           # Mask token behave like a normal word, i.e. include the space before it
           _lowerCamelCase			    =				AddedToken(A_				,				lstrip=A_				,				rstrip=A_						) if isinstance(A_				,				A_						) else mask_token

           _lowerCamelCase			    =				{} 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_				,				sp_model_kwargs=self.sp_model_kwargs				,				**A_				,				)

           _lowerCamelCase			    =				vocab_file
           _lowerCamelCase			    =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
           self.sp_model.Load(str(A_						)						)

           _lowerCamelCase			    =				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

           _lowerCamelCase			    =				len(self.sp_model						) - 1
           _lowerCamelCase			    =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				List[int]:






           """simple docstring"""

           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _lowerCamelCase			    =				[self.cls_token_id]
           _lowerCamelCase			    =				[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None				,				A_ = 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 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				List[int]:






           """simple docstring"""
           _lowerCamelCase			    =				[self.sep_token_id]
           _lowerCamelCase			    =				[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 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           return len(self.sp_model						)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				{self.convert_ids_to_tokens(A_						): i for i in range(self.vocab_size						)}
           vocab.update(self.added_tokens_encoder						)
           return vocab



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           return self.sp_model.encode(A_				,				out_type=A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           _lowerCamelCase			    =				self.sp_model.PieceToId(A_						)

           return spm_id if spm_id else self.unk_token_id



     def 		UpperCamelCase_						(					self				,				A_						) ->				Dict:






           """simple docstring"""
           if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				''''''
           _lowerCamelCase			    =				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
                       _lowerCamelCase			    =				True
                       _lowerCamelCase			    =				[]
                 else:
                       current_sub_tokens.append(A_						)
                       _lowerCamelCase			    =				False
           out_string += self.sp_model.decode(A_						)
           return out_string.strip()



     def __getstate__(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.__dict__.copy()
           _lowerCamelCase			    =				None
           return state



     def __setstate__(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				d

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

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





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






           """simple docstring"""
           if not os.path.isdir(A_						):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
                 return
           _lowerCamelCase			    =				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:
                       _lowerCamelCase			    =				self.sp_model.serialized_model_proto()
                       fi.write(A_						)

           return (out_vocab_file,)
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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     @register_to_config
     def __init__(					self				,				A_ = 6_55_36				,				A_ = None				,				A_ = 2				,				A_ = 2				,				A_ = 0				,				A_ = "fourier"				,				A_ = True				,				A_ = False				,				A_ = 0.0				,				A_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,				A_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,				A_ = "UNetMidBlock1D"				,				A_ = None				,				A_ = (32, 32, 64)				,				A_ = None				,				A_ = 8				,				A_ = 1				,				A_ = False				,				) ->				Dict:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				sample_size

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

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

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

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

                 if i == 0:
                       input_channel += extra_in_channels

                 _lowerCamelCase			    =				i == len(A_						) - 1

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

           # mid
           _lowerCamelCase			    =				get_mid_block(
               A_				,				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=A_				,				add_downsample=A_				,				)

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

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

                 _lowerCamelCase			    =				i == len(A_						) - 1

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

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





     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = True				,				) ->				Union[UNetaDOutput, Tuple]:






           """simple docstring"""
           _lowerCamelCase			    =				timestep
           if not torch.is_tensor(A_						):
                 _lowerCamelCase			    =				torch.tensor([timesteps]				,				dtype=torch.long				,				device=sample.device						)
           elif torch.is_tensor(A_						) and len(timesteps.shape						) == 0:
                 _lowerCamelCase			    =				timesteps[None].to(sample.device						)

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

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

           # 3. mid
           if self.mid_block:
                 _lowerCamelCase			    =				self.mid_block(A_				,				A_						)

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

           # 5. post-process
           if self.out_block:
                 _lowerCamelCase			    =				self.out_block(A_				,				A_						)

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=A_						)