




from typing import Any, Dict, Optional

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

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class 						_A (  nn.Module				):






  def __init__(  self		:     Union[str, Any]				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     Union[str, Any]=0.0				,						_A		:     Optional[int] = None				,						_A		:     str = "geglu"				,						_A		:     Optional[int] = None				,						_A		:     bool = False				,						_A		:     bool = False				,						_A		:     bool = False				,						_A		:     bool = False				,						_A		:     bool = True				,						_A		:     str = "layer_norm"				,						_A		:     bool = False				,						)		->   Tuple:






       """simple docstring"""







       super().__init__()
       lowercase :       List[str]       =			only_cross_attention

       lowercase :       Union[str, Any]       =			(num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
       lowercase :       Dict       =			(num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''

       if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
            raise ValueError(
                f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
                f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""				)

       # Define 3 blocks. Each block has its own normalization layer.
       # 1. Self-Attn
       if self.use_ada_layer_norm:
            lowercase :       List[Any]       =			AdaLayerNorm(_A				,						_A				)
       elif self.use_ada_layer_norm_zero:
            lowercase :       Union[str, Any]       =			AdaLayerNormZero(_A				,						_A				)
       else:
            lowercase :       Optional[Any]       =			nn.LayerNorm(_A				,						elementwise_affine=_A				)
       lowercase :       List[Any]       =			Attention(
           query_dim=_A				,						heads=_A				,						dim_head=_A				,						dropout=_A				,						bias=_A				,						cross_attention_dim=cross_attention_dim if only_cross_attention else None				,						upcast_attention=_A				,						)

       # 2. Cross-Attn
       if cross_attention_dim is not None or double_self_attention:
            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
            # the second cross attention block.
            lowercase :       List[str]       =			(
                AdaLayerNorm(_A				,						_A				)
                if self.use_ada_layer_norm
                else nn.LayerNorm(_A				,						elementwise_affine=_A				)
            )
            lowercase :       Dict       =			Attention(
                query_dim=_A				,						cross_attention_dim=cross_attention_dim if not double_self_attention else None				,						heads=_A				,						dim_head=_A				,						dropout=_A				,						bias=_A				,						upcast_attention=_A				,						)  # is self-attn if encoder_hidden_states is none
       else:
            lowercase :       Tuple       =			None
            lowercase :       Optional[int]       =			None

       # 3. Feed-forward
       lowercase :       int       =			nn.LayerNorm(_A				,						elementwise_affine=_A				)
       lowercase :       Union[str, Any]       =			FeedForward(_A				,						dropout=_A				,						activation_fn=_A				,						final_dropout=_A				)

       # let chunk size default to None
       lowercase :       Union[str, Any]       =			None
       lowercase :       Dict       =			0






  def  __a      (  self		:     Any				,						_A		:     Optional[int]				,						_A		:     int				)		->   int:






       """simple docstring"""







       lowercase :       Tuple       =			chunk_size
       lowercase :       Optional[int]       =			dim






  def  __a      (  self		:     List[str]				,						_A		:     torch.FloatTensor				,						_A		:     Optional[torch.FloatTensor] = None				,						_A		:     Optional[torch.FloatTensor] = None				,						_A		:     Optional[torch.FloatTensor] = None				,						_A		:     Optional[torch.LongTensor] = None				,						_A		:     Dict[str, Any] = None				,						_A		:     Optional[torch.LongTensor] = None				,						)		->   str:






       """simple docstring"""







       if self.use_ada_layer_norm:
            lowercase :       Tuple       =			self.norma(_A				,						_A				)
       elif self.use_ada_layer_norm_zero:
            lowercase      ,	lowercase      ,	lowercase      ,	lowercase      ,	lowercase :       str       =			self.norma(
                _A				,						_A				,						_A				,						hidden_dtype=hidden_states.dtype				)
       else:
            lowercase :       Union[str, Any]       =			self.norma(_A				)

       lowercase :       Any       =			cross_attention_kwargs if cross_attention_kwargs is not None else {}

       lowercase :       Optional[int]       =			self.attna(
           _A				,						encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None				,						attention_mask=_A				,						**_A				,						)
       if self.use_ada_layer_norm_zero:
            lowercase :       str       =			gate_msa.unsqueeze(1				) * attn_output
       lowercase :       Dict       =			attn_output + hidden_states

       # 2. Cross-Attention
       if self.attna is not None:
            lowercase :       Optional[int]       =			(
                self.norma(_A				,						_A				) if self.use_ada_layer_norm else self.norma(_A				)
            )

            lowercase :       List[Any]       =			self.attna(
                _A				,						encoder_hidden_states=_A				,						attention_mask=_A				,						**_A				,						)
            lowercase :       Union[str, Any]       =			attn_output + hidden_states

       # 3. Feed-forward
       lowercase :       str       =			self.norma(_A				)

       if self.use_ada_layer_norm_zero:
            lowercase :       List[str]       =			norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

       if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                 raise ValueError(
                     f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""				)

            lowercase :       int       =			norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
            lowercase :       Dict       =			torch.cat(
                [self.ff(_A				) for hid_slice in norm_hidden_states.chunk(_A				,						dim=self._chunk_dim				)]				,						dim=self._chunk_dim				,						)
       else:
            lowercase :       List[Any]       =			self.ff(_A				)

       if self.use_ada_layer_norm_zero:
            lowercase :       str       =			gate_mlp.unsqueeze(1				) * ff_output

       lowercase :       List[Any]       =			ff_output + hidden_states

       return hidden_states


class 						_A (  nn.Module				):






  def __init__(  self		:     List[str]				,						_A		:     int				,						_A		:     Optional[int] = None				,						_A		:     int = 4				,						_A		:     float = 0.0				,						_A		:     str = "geglu"				,						_A		:     bool = False				,						)		->   str:






       """simple docstring"""







       super().__init__()
       lowercase :       Union[str, Any]       =			int(dim * mult				)
       lowercase :       Optional[int]       =			dim_out if dim_out is not None else dim

       if activation_fn == "gelu":
            lowercase :       List[Any]       =			GELU(_A				,						_A				)
       if activation_fn == "gelu-approximate":
            lowercase :       Dict       =			GELU(_A				,						_A				,						approximate='''tanh'''				)
       elif activation_fn == "geglu":
            lowercase :       int       =			GEGLU(_A				,						_A				)
       elif activation_fn == "geglu-approximate":
            lowercase :       List[str]       =			ApproximateGELU(_A				,						_A				)

       lowercase :       List[str]       =			nn.ModuleList([]				)
       # project in
       self.net.append(_A				)
       # project dropout
       self.net.append(nn.Dropout(_A				)				)
       # project out
       self.net.append(nn.Linear(_A				,						_A				)				)
       # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
       if final_dropout:
            self.net.append(nn.Dropout(_A				)				)






  def  __a      (  self		:     Tuple				,						_A		:     int				)		->   List[str]:






       """simple docstring"""







       for module in self.net:
            lowercase :       int       =			module(_A				)
       return hidden_states


class 						_A (  nn.Module				):






  def __init__(  self		:     str				,						_A		:     int				,						_A		:     int				,						_A		:     str = "none"				)		->   Tuple:






       """simple docstring"""







       super().__init__()
       lowercase :       Any       =			nn.Linear(_A				,						_A				)
       lowercase :       List[Any]       =			approximate






  def  __a      (  self		:     List[Any]				,						_A		:     Optional[Any]				)		->   Tuple:






       """simple docstring"""







       if gate.device.type != "mps":
            return F.gelu(_A				,						approximate=self.approximate				)
       # mps: gelu is not implemented for float16
       return F.gelu(gate.to(dtype=torch.floataa				)				,						approximate=self.approximate				).to(dtype=gate.dtype				)






  def  __a      (  self		:     List[Any]				,						_A		:     Union[str, Any]				)		->   Dict:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.proj(_A				)
       lowercase :       Optional[Any]       =			self.gelu(_A				)
       return hidden_states


class 						_A (  nn.Module				):






  def __init__(  self		:     List[Any]				,						_A		:     int				,						_A		:     int				)		->   Optional[Any]:






       """simple docstring"""







       super().__init__()
       lowercase :       List[str]       =			nn.Linear(_A				,						dim_out * 2				)






  def  __a      (  self		:     Any				,						_A		:     Union[str, Any]				)		->   Any:






       """simple docstring"""







       if gate.device.type != "mps":
            return F.gelu(_A				)
       # mps: gelu is not implemented for float16
       return F.gelu(gate.to(dtype=torch.floataa				)				).to(dtype=gate.dtype				)






  def  __a      (  self		:     Any				,						_A		:     Tuple				)		->   List[Any]:






       """simple docstring"""







       lowercase      ,	lowercase :       List[Any]       =			self.proj(_A				).chunk(2				,						dim=-1				)
       return hidden_states * self.gelu(_A				)


class 						_A (  nn.Module				):






  def __init__(  self		:     Union[str, Any]				,						_A		:     int				,						_A		:     int				)		->   Optional[int]:






       """simple docstring"""







       super().__init__()
       lowercase :       Tuple       =			nn.Linear(_A				,						_A				)






  def  __a      (  self		:     Tuple				,						_A		:     Optional[int]				)		->   int:






       """simple docstring"""







       lowercase :       Any       =			self.proj(_A				)
       return x * torch.sigmoid(1.702 * x				)


class 						_A (  nn.Module				):






  def __init__(  self		:     str				,						_A		:     str				,						_A		:     int				)		->   str:






       """simple docstring"""







       super().__init__()
       lowercase :       str       =			nn.Embedding(_A				,						_A				)
       lowercase :       Union[str, Any]       =			nn.SiLU()
       lowercase :       List[Any]       =			nn.Linear(_A				,						embedding_dim * 2				)
       lowercase :       Union[str, Any]       =			nn.LayerNorm(_A				,						elementwise_affine=_A				)






  def  __a      (  self		:     str				,						_A		:     List[Any]				,						_A		:     Dict				)		->   str:






       """simple docstring"""







       lowercase :       Any       =			self.linear(self.silu(self.emb(_A				)				)				)
       lowercase      ,	lowercase :       List[Any]       =			torch.chunk(_A				,						2				)
       lowercase :       Tuple       =			self.norm(_A				) * (1 + scale) + shift
       return x


class 						_A (  nn.Module				):






  def __init__(  self		:     Tuple				,						_A		:     Dict				,						_A		:     List[str]				)		->   List[Any]:






       """simple docstring"""







       super().__init__()

       lowercase :       List[str]       =			CombinedTimestepLabelEmbeddings(_A				,						_A				)

       lowercase :       List[Any]       =			nn.SiLU()
       lowercase :       str       =			nn.Linear(_A				,						6 * embedding_dim				,						bias=_A				)
       lowercase :       str       =			nn.LayerNorm(_A				,						elementwise_affine=_A				,						eps=1E-6				)






  def  __a      (  self		:     int				,						_A		:     str				,						_A		:     List[Any]				,						_A		:     str				,						_A		:     str=None				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Tuple       =			self.linear(self.silu(self.emb(_A				,						_A				,						hidden_dtype=_A				)				)				)
       lowercase      ,	lowercase      ,	lowercase      ,	lowercase      ,	lowercase      ,	lowercase :       Tuple       =			emb.chunk(6				,						dim=1				)
       lowercase :       Union[str, Any]       =			self.norm(_A				) * (1 + scale_msa[:, None]) + shift_msa[:, None]
       return x, gate_msa, shift_mlp, scale_mlp, gate_mlp


class 						_A (  nn.Module				):






  def __init__(  self		:     Union[str, Any]				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     Optional[str] = None				,						_A		:     float = 1E-5				)		->   List[Any]:






       """simple docstring"""







       super().__init__()
       lowercase :       Union[str, Any]       =			num_groups
       lowercase :       Optional[int]       =			eps

       if act_fn is None:
            lowercase :       List[str]       =			None
       else:
            lowercase :       List[str]       =			get_activation(_A				)

       lowercase :       Any       =			nn.Linear(_A				,						out_dim * 2				)






  def  __a      (  self		:     Dict				,						_A		:     Tuple				,						_A		:     Union[str, Any]				)		->   Dict:






       """simple docstring"""







       if self.act:
            lowercase :       int       =			self.act(_A				)
       lowercase :       Optional[int]       =			self.linear(_A				)
       lowercase :       str       =			emb[:, :, None, None]
       lowercase      ,	lowercase :       List[Any]       =			emb.chunk(2				,						dim=1				)

       lowercase :       str       =			F.group_norm(_A				,						self.num_groups				,						eps=self.eps				)
       lowercase :       Optional[int]       =			x * (1 + scale) + shift
       return x





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


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_						=			{
    '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'
        ),
    },
}

lowerCAmelCase_						=			{
    'moussaKam/mbarthez': 10_24,
    'moussaKam/barthez': 10_24,
    'moussaKam/barthez-orangesum-title': 10_24,
}

lowerCAmelCase_						=			'▁'
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Optional[Any]			=      VOCAB_FILES_NAMES
  _UpperCamelCase       :      Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
  _UpperCamelCase       :      Optional[Any]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCamelCase       :      List[Any]			=      ['''input_ids''', '''attention_mask''']






  def __init__(  self		:     Any				,						_A		:     Optional[int]				,						_A		:     List[str]="<s>"				,						_A		:     Tuple="</s>"				,						_A		:     Dict="</s>"				,						_A		:     Dict="<s>"				,						_A		:     List[str]="<unk>"				,						_A		:     str="<pad>"				,						_A		:     Any="<mask>"				,						_A		:     Optional[Dict[str, Any]] = None				,						**_A		:     Union[str, Any]				,						)		->   None:






       """simple docstring"""







       lowercase :       List[str]       =			AddedToken(_A				,						lstrip=_A				,						rstrip=_A				) if isinstance(_A				,						_A				) else mask_token

       lowercase :       List[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				,						sp_model_kwargs=self.sp_model_kwargs				,						**_A				,						)

       lowercase :       Any       =			vocab_file
       lowercase :       Any       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(str(_A				)				)

       lowercase :       Optional[Any]       =			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

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






  def  __a      (  self		:     Tuple				,						_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]
       lowercase :       Any       =			[self.cls_token_id]
       lowercase :       int       =			[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






  def  __a      (  self		:     Optional[int]				,						_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		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







       lowercase :       List[str]       =			[self.sep_token_id]
       lowercase :       List[str]       =			[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		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return len(self.sp_model				)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       lowercase :       int       =			{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		:     Union[str, Any]				,						_A		:     str				)		->   List[str]:






       """simple docstring"""







       return self.sp_model.encode(_A				,						out_type=_A				)






  def  __a      (  self		:     Optional[int]				,						_A		:     str				)		->   Dict:






       """simple docstring"""







       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       lowercase :       Optional[int]       =			self.sp_model.PieceToId(_A				)

       return spm_id if spm_id else self.unk_token_id






  def  __a      (  self		:     Any				,						_A		:     List[str]				)		->   List[str]:






       """simple docstring"""







       if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(_A				)






  def  __a      (  self		:     Any				,						_A		:     Tuple				)		->   Tuple:






       """simple docstring"""







       lowercase :       Dict       =			[]
       lowercase :       Any       =			''''''
       lowercase :       str       =			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
                 lowercase :       int       =			True
                 lowercase :       Optional[Any]       =			[]
            else:
                 current_sub_tokens.append(_A				)
                 lowercase :       List[str]       =			False
       out_string += self.sp_model.decode(_A				)
       return out_string.strip()






  def __getstate__(  self		:     int				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       str       =			self.__dict__.copy()
       lowercase :       Any       =			None
       return state






  def __setstate__(  self		:     Optional[int]				,						_A		:     Tuple				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			d

       # for backward compatibility
       if not hasattr(self				,						'''sp_model_kwargs'''				):
            lowercase :       Union[str, Any]       =			{}

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






  def  __a      (  self		:     str				,						_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
       lowercase :       Any       =			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:
                 lowercase :       Optional[Any]       =			self.sp_model.serialized_model_proto()
                 fi.write(_A				)

       return (out_vocab_file,)