'''simple docstring'''




import os
import re
import unicodedata
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


__lowercase				   =  logging.get_logger(__name__)
__lowercase				   =  {'''vocab_file''': '''spiece.model'''}

__lowercase				   =  {
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
}

__lowercase				   =  {
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

__lowercase				   =  '''▁'''






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					List[str]								=	VOCAB_FILES_NAMES
			UpperCAmelCase_      :					int								=	PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_      :					Optional[int]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_      :					Dict								=	['''input_ids''', '''token_type_ids''']



			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=False	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase="<unk>"	,   __lowerCAmelCase="[SEP]"	,   __lowerCAmelCase="<pad>"	,   __lowerCAmelCase="[CLS]"	,   __lowerCAmelCase="[MASK]"	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  (
					    AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase	,   normalized=__lowerCAmelCase)
					    if isinstance(__lowerCAmelCase	,   __lowerCAmelCase)
					    else mask_token
					)

					lowerCAmelCase        =  {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    do_lower_case=__lowerCAmelCase	,   remove_space=__lowerCAmelCase	,   keep_accents=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   sep_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   cls_token=__lowerCAmelCase	,   mask_token=__lowerCAmelCase	,   sp_model_kwargs=self.sp_model_kwargs	,   **__lowerCAmelCase	,   )

					lowerCAmelCase        =  do_lower_case
					lowerCAmelCase        =  remove_space
					lowerCAmelCase        =  keep_accents
					lowerCAmelCase        =  vocab_file

					lowerCAmelCase        =  spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(__lowerCAmelCase)



			@property
			def 							a_	(       self):




					"""simple docstring"""


					return len(self.sp_model)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  {self.convert_ids_to_tokens(__lowerCAmelCase): i for i in range(self.vocab_size)}
					vocab.update(self.added_tokens_encoder)
					return vocab



			def __getstate__(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.__dict__.copy()
					lowerCAmelCase        =  None
					return state



			def __setstate__(       self	,   __lowerCAmelCase):




					"""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 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					if self.remove_space:
							lowerCAmelCase        =  """ """.join(inputs.strip().split())
					else:
							lowerCAmelCase        =  inputs
					lowerCAmelCase        =  outputs.replace("""``"""	,   """\"""").replace("""''"""	,   """\"""")

					if not self.keep_accents:
							lowerCAmelCase        =  unicodedata.normalize("""NFKD"""	,   __lowerCAmelCase)
							lowerCAmelCase        =  """""".join([c for c in outputs if not unicodedata.combining(__lowerCAmelCase)])
					if self.do_lower_case:
							lowerCAmelCase        =  outputs.lower()

					return outputs



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  self.preprocess_text(__lowerCAmelCase)
					lowerCAmelCase        =  self.sp_model.encode(__lowerCAmelCase	,   out_type=__lowerCAmelCase)
					lowerCAmelCase        =  []
					for piece in pieces:
							if len(__lowerCAmelCase) > 1 and piece[-1] == str(""",""") and piece[-2].isdigit():
									lowerCAmelCase        =  self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCAmelCase	,   """"""))
									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
											if len(cur_pieces[0]) == 1:
													lowerCAmelCase        =  cur_pieces[1:]
											else:
													lowerCAmelCase        =  cur_pieces[0][1:]
									cur_pieces.append(piece[-1])
									new_pieces.extend(__lowerCAmelCase)
							else:
									new_pieces.append(__lowerCAmelCase)

					return new_pieces



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.sp_model.PieceToId(__lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.sp_model.IdToPiece(__lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""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(__lowerCAmelCase) + token
									lowerCAmelCase        =  True
									lowerCAmelCase        =  []
							else:
									current_sub_tokens.append(__lowerCAmelCase)
									lowerCAmelCase        =  False
					out_string += self.sp_model.decode(__lowerCAmelCase)
					return out_string.strip()



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = False	,   __lowerCAmelCase = None	,   __lowerCAmelCase = True	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  kwargs.pop("""use_source_tokenizer"""	,   __lowerCAmelCase)

					lowerCAmelCase        =  self.convert_ids_to_tokens(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase)

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					lowerCAmelCase        =  []
					lowerCAmelCase        =  []
					for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
									continue
							if token in self.added_tokens_encoder:
									if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase))
											lowerCAmelCase        =  []
									sub_texts.append(__lowerCAmelCase)
							else:
									current_sub_text.append(__lowerCAmelCase)
					if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase))

					# Mimic the behavior of the Rust tokenizer:
					# No space after <unk>
					if spaces_between_special_tokens:
							lowerCAmelCase        =  re.sub(r"""(<unk>) """	,   r"""\1"""	,   """ """.join(__lowerCAmelCase))
					else:
							lowerCAmelCase        =  """""".join(__lowerCAmelCase)

					lowerCAmelCase        =  (
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
							lowerCAmelCase        =  self.clean_up_tokenization(__lowerCAmelCase)
							return clean_text
					else:
							return text



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


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



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   __lowerCAmelCase = False):




					"""simple docstring"""


					if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__lowerCAmelCase	,   token_ids_a=__lowerCAmelCase	,   already_has_special_tokens=__lowerCAmelCase)

					if token_ids_a is not None:
							return [1] + ([0] * len(__lowerCAmelCase)) + [1] + ([0] * len(__lowerCAmelCase)) + [1]
					return [1] + ([0] * len(__lowerCAmelCase)) + [1]



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""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) * [0] + len(token_ids_a + sep) * [1]







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


					if not os.path.isdir(__lowerCAmelCase):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory")
							return
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

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

					return (out_vocab_file,)





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					int								=	'''flaubert'''
			UpperCAmelCase_      :					Optional[Any]								=	{
			    '''hidden_size''': '''emb_dim''',
			    '''num_attention_heads''': '''n_heads''',
			    '''num_hidden_layers''': '''n_layers''',
			    '''n_words''': '''vocab_size''',  # For backward compatibility
			}



			def __init__(       self	,   __lowerCAmelCase=False	,   __lowerCAmelCase=0.0	,   __lowerCAmelCase=30145	,   __lowerCAmelCase=2048	,   __lowerCAmelCase=12	,   __lowerCAmelCase=16	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=512	,   __lowerCAmelCase=2048**-0.5	,   __lowerCAmelCase=1E-1_2	,   __lowerCAmelCase=0.02	,   __lowerCAmelCase=0	,   __lowerCAmelCase=1	,   __lowerCAmelCase=2	,   __lowerCAmelCase=3	,   __lowerCAmelCase=5	,   __lowerCAmelCase=True	,   __lowerCAmelCase="first"	,   __lowerCAmelCase=True	,   __lowerCAmelCase=None	,   __lowerCAmelCase=True	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=5	,   __lowerCAmelCase=5	,   __lowerCAmelCase=0	,   __lowerCAmelCase=0	,   __lowerCAmelCase=2	,   __lowerCAmelCase=0	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  pre_norm
					lowerCAmelCase        =  layerdrop
					lowerCAmelCase        =  vocab_size
					lowerCAmelCase        =  emb_dim
					lowerCAmelCase        =  n_layers
					lowerCAmelCase        =  n_heads
					lowerCAmelCase        =  dropout
					lowerCAmelCase        =  attention_dropout
					lowerCAmelCase        =  gelu_activation
					lowerCAmelCase        =  sinusoidal_embeddings
					lowerCAmelCase        =  causal
					lowerCAmelCase        =  asm
					lowerCAmelCase        =  n_langs
					lowerCAmelCase        =  use_lang_emb
					lowerCAmelCase        =  layer_norm_eps
					lowerCAmelCase        =  bos_index
					lowerCAmelCase        =  eos_index
					lowerCAmelCase        =  pad_index
					lowerCAmelCase        =  unk_index
					lowerCAmelCase        =  mask_index
					lowerCAmelCase        =  is_encoder
					lowerCAmelCase        =  max_position_embeddings
					lowerCAmelCase        =  embed_init_std
					lowerCAmelCase        =  init_std
					lowerCAmelCase        =  summary_type
					lowerCAmelCase        =  summary_use_proj
					lowerCAmelCase        =  summary_activation
					lowerCAmelCase        =  summary_proj_to_labels
					lowerCAmelCase        =  summary_first_dropout
					lowerCAmelCase        =  start_n_top
					lowerCAmelCase        =  end_n_top
					lowerCAmelCase        =  mask_token_id
					lowerCAmelCase        =  lang_id

					if "n_words" in kwargs:
							lowerCAmelCase        =  kwargs["""n_words"""]

					super().__init__(pad_token_id=__lowerCAmelCase	,   bos_token_id=__lowerCAmelCase	,   **__lowerCAmelCase)







class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			@property
			def 							a_	(       self):




					"""simple docstring"""


					if self.task == "multiple-choice":
							lowerCAmelCase        =  {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							lowerCAmelCase        =  {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ])




