


"""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


UpperCAmelCase				=						logging.get_logger(__name__)

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

UpperCAmelCase				=						{
    """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"""
        ),
    },
}

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

UpperCAmelCase				=						"""▁"""




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					VOCAB_FILES_NAMES
   __UpperCAmelCase							       =					PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase							       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase							       =					['''input_ids''', '''attention_mask''']





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE="<s>"      ,   SCREAMING_SNAKE_CASE="</s>"      ,   SCREAMING_SNAKE_CASE="</s>"      ,   SCREAMING_SNAKE_CASE="<s>"      ,   SCREAMING_SNAKE_CASE="<unk>"      ,   SCREAMING_SNAKE_CASE="<pad>"      ,   SCREAMING_SNAKE_CASE="<mask>"      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  None:
      # Mask token behave like a normal word, i.e. include the space before it
      _lowerCamelCase	:					List[str]   =		AddedToken(SCREAMING_SNAKE_CASE      ,   lstrip=SCREAMING_SNAKE_CASE      ,   rstrip=SCREAMING_SNAKE_CASE) if isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE) else mask_token

      _lowerCamelCase	:					List[Any]   =		{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=SCREAMING_SNAKE_CASE      ,   eos_token=SCREAMING_SNAKE_CASE      ,   unk_token=SCREAMING_SNAKE_CASE      ,   sep_token=SCREAMING_SNAKE_CASE      ,   cls_token=SCREAMING_SNAKE_CASE      ,   pad_token=SCREAMING_SNAKE_CASE      ,   mask_token=SCREAMING_SNAKE_CASE      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **SCREAMING_SNAKE_CASE      ,   )

      _lowerCamelCase	:					Optional[int]   =		vocab_file
      _lowerCamelCase	:					Tuple   =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(str(SCREAMING_SNAKE_CASE))

      _lowerCamelCase	:					Union[str, Any]   =		{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

      _lowerCamelCase	:					Optional[int]   =		len(self.sp_model) - 1
      _lowerCamelCase	:					List[Any]   =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}





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

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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = False)  ->  List[int]:
      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=SCREAMING_SNAKE_CASE      ,   token_ids_a=SCREAMING_SNAKE_CASE      ,   already_has_special_tokens=SCREAMING_SNAKE_CASE)

      if token_ids_a is None:
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE)) + [1]
      return [1] + ([0] * len(SCREAMING_SNAKE_CASE)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE)) + [1]





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  List[int]:
      _lowerCamelCase	:					Union[str, Any]   =		[self.sep_token_id]
      _lowerCamelCase	:					Tuple   =		[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]:
      return len(self.sp_model)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Tuple   =		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      return self.sp_model.encode(SCREAMING_SNAKE_CASE      ,   out_type=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  str:
      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      _lowerCamelCase	:					Optional[Any]   =		self.sp_model.PieceToId(SCREAMING_SNAKE_CASE)

      return spm_id if spm_id else self.unk_token_id





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					Any   =		[]
      _lowerCamelCase	:					List[str]   =		""""""
      _lowerCamelCase	:					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(SCREAMING_SNAKE_CASE) + token
            _lowerCamelCase	:					Any   =		True
            _lowerCamelCase	:					Union[str, Any]   =		[]
         else:
            current_sub_tokens.append(SCREAMING_SNAKE_CASE)
            _lowerCamelCase	:					str   =		False
      out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE)
      return out_string.strip()





   def __getstate__(							self)  ->  int:
      _lowerCamelCase	:					List[str]   =		self.__dict__.copy()
      _lowerCamelCase	:					int   =		None
      return state





   def __setstate__(							self      ,   SCREAMING_SNAKE_CASE)  ->  Dict:
      _lowerCamelCase	:					Optional[Any]   =		d

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

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





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

      if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
         copyfile(self.vocab_file      ,   SCREAMING_SNAKE_CASE)
      elif not os.path.isfile(self.vocab_file):
         with open(SCREAMING_SNAKE_CASE      ,   """wb""") as fi:
            _lowerCamelCase	:					List[str]   =		self.sp_model.serialized_model_proto()
            fi.write(SCREAMING_SNAKE_CASE)

      return (out_vocab_file,)









import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				Optional[Any]   =      []
						_A	:				str   =      []

						for i in range(self.num_layers		):
										_A	:				Union[str, Any]   =      self.in_channels if i == 0 else self.out_channels

										_A	:				str   =      FlaxResnetBlockaD(
										    in_channels=A__       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

										_A	:				List[str]   =      FlaxTransformeraDModel(
										    in_channels=self.out_channels       ,n_heads=self.num_attention_heads       ,d_head=self.out_channels // self.num_attention_heads       ,depth=1       ,use_linear_projection=self.use_linear_projection       ,only_cross_attention=self.only_cross_attention       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,dtype=self.dtype       ,)
										attentions.append(A__		)

						_A	:				List[str]   =      resnets
						_A	:				Any   =      attentions

						if self.add_downsample:
										_A	:				Any   =      FlaxDownsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__       ,A__=True		):
						_A	:				List[str]   =      ()

						for resnet, attn in zip(self.resnets       ,self.attentions		):
										_A	:				Optional[Any]   =      resnet(A__       ,A__       ,deterministic=A__		)
										_A	:				Tuple   =      attn(A__       ,A__       ,deterministic=A__		)
										output_states += (hidden_states,)

						if self.add_downsample:
										_A	:				Optional[int]   =      self.downsamplers_a(A__		)
										output_states += (hidden_states,)

						return hidden_states, output_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				List[Any]   =      []

						for i in range(self.num_layers		):
										_A	:				int   =      self.in_channels if i == 0 else self.out_channels

										_A	:				List[Any]   =      FlaxResnetBlockaD(
										    in_channels=A__       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)
						_A	:				Tuple   =      resnets

						if self.add_downsample:
										_A	:				Optional[Any]   =      FlaxDownsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__=True		):
						_A	:				List[Any]   =      ()

						for resnet in self.resnets:
										_A	:				Optional[int]   =      resnet(A__       ,A__       ,deterministic=A__		)
										output_states += (hidden_states,)

						if self.add_downsample:
										_A	:				List[Any]   =      self.downsamplers_a(A__		)
										output_states += (hidden_states,)

						return hidden_states, output_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				List[str]   =      []
						_A	:				Optional[Any]   =      []

						for i in range(self.num_layers		):
										_A	:				Dict   =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
										_A	:				List[str]   =      self.prev_output_channel if i == 0 else self.out_channels

										_A	:				Optional[int]   =      FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

										_A	:				Union[str, Any]   =      FlaxTransformeraDModel(
										    in_channels=self.out_channels       ,n_heads=self.num_attention_heads       ,d_head=self.out_channels // self.num_attention_heads       ,depth=1       ,use_linear_projection=self.use_linear_projection       ,only_cross_attention=self.only_cross_attention       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,dtype=self.dtype       ,)
										attentions.append(A__		)

						_A	:				Dict   =      resnets
						_A	:				int   =      attentions

						if self.add_upsample:
										_A	:				str   =      FlaxUpsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__       ,A__       ,A__=True		):
						for resnet, attn in zip(self.resnets       ,self.attentions		):
										# pop res hidden states
										_A	:				List[Any]   =      res_hidden_states_tuple[-1]
										_A	:				int   =      res_hidden_states_tuple[:-1]
										_A	:				List[Any]   =      jnp.concatenate((hidden_states, res_hidden_states)       ,axis=-1		)

										_A	:				str   =      resnet(A__       ,A__       ,deterministic=A__		)
										_A	:				Optional[int]   =      attn(A__       ,A__       ,deterministic=A__		)

						if self.add_upsample:
										_A	:				Union[str, Any]   =      self.upsamplers_a(A__		)

						return hidden_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				Optional[Any]   =      []

						for i in range(self.num_layers		):
										_A	:				Any   =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
										_A	:				Optional[Any]   =      self.prev_output_channel if i == 0 else self.out_channels

										_A	:				List[Any]   =      FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

						_A	:				int   =      resnets

						if self.add_upsample:
										_A	:				List[str]   =      FlaxUpsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__       ,A__=True		):
						for resnet in self.resnets:
										# pop res hidden states
										_A	:				Tuple   =      res_hidden_states_tuple[-1]
										_A	:				Optional[int]   =      res_hidden_states_tuple[:-1]
										_A	:				Any   =      jnp.concatenate((hidden_states, res_hidden_states)       ,axis=-1		)

										_A	:				Optional[Any]   =      resnet(A__       ,A__       ,deterministic=A__		)

						if self.add_upsample:
										_A	:				Tuple   =      self.upsamplers_a(A__		)

						return hidden_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						# there is always at least one resnet
						_A	:				List[str]   =      [
						    FlaxResnetBlockaD(
						        in_channels=self.in_channels       ,out_channels=self.in_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
						]

						_A	:				Union[str, Any]   =      []

						for _ in range(self.num_layers		):
										_A	:				Any   =      FlaxTransformeraDModel(
										    in_channels=self.in_channels       ,n_heads=self.num_attention_heads       ,d_head=self.in_channels // self.num_attention_heads       ,depth=1       ,use_linear_projection=self.use_linear_projection       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,dtype=self.dtype       ,)
										attentions.append(A__		)

										_A	:				Any   =      FlaxResnetBlockaD(
										    in_channels=self.in_channels       ,out_channels=self.in_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

						_A	:				List[str]   =      resnets
						_A	:				str   =      attentions







		def __call__(					self       ,A__       ,A__       ,A__       ,A__=True		):
						_A	:				Optional[int]   =      self.resnets[0](A__       ,A__		)
						for attn, resnet in zip(self.attentions       ,self.resnets[1:]		):
										_A	:				Dict   =      attn(A__       ,A__       ,deterministic=A__		)
										_A	:				Any   =      resnet(A__       ,A__       ,deterministic=A__		)

						return hidden_states



