
import os
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 SPIECE_UNDERLINE, logging


lowercase__		:Tuple			  =   logging.get_logger(__name__)

lowercase__		:List[Any]			  =   {"vocab_file": "spiece.model"}

lowercase__		:Optional[int]			  =   {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

lowercase__		:Optional[int]			  =   {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
lowercase__		:int			  =   0
lowercase__		:Optional[Any]			  =   1
lowercase__		:List[Any]			  =   2
lowercase__		:str			  =   3
lowercase__		:Optional[int]			  =   4







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[str]	 =VOCAB_FILES_NAMES
							lowercase_	:	List[str]	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	str	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	Union[str, Any]	 ='''left'''




							def __init__(    self       ,A__       ,A__=False       ,A__=True       ,A__=False       ,A__="<s>"       ,A__="</s>"       ,A__="<unk>"       ,A__="<sep>"       ,A__="<pad>"       ,A__="<cls>"       ,A__="<mask>"       ,A__=["<eop>", "<eod>"]       ,A__ = None       ,**A__       ,):
												# Mask token behave like a normal word, i.e. include the space before it
												lowercase						     =						AddedToken(A__       ,lstrip=A__       ,rstrip=A__) if isinstance(A__       ,A__) else mask_token

												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    do_lower_case=A__       ,remove_space=A__       ,keep_accents=A__       ,bos_token=A__       ,eos_token=A__       ,unk_token=A__       ,sep_token=A__       ,pad_token=A__       ,cls_token=A__       ,mask_token=A__       ,additional_special_tokens=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

												lowercase						     =						3

												lowercase						     =						do_lower_case
												lowercase						     =						remove_space
												lowercase						     =						keep_accents
												lowercase						     =						vocab_file

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(A__)




							@property
							def 			A__				(    self):
												return len(self.sp_model)




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d

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

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




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

												if not self.keep_accents:
																	lowercase						     =						unicodedata.normalize('''NFKD'''       ,A__)
																	lowercase						     =						''''''.join([c for c in outputs if not unicodedata.combining(A__)])
												if self.do_lower_case:
																	lowercase						     =						outputs.lower()

												return outputs




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

												return new_pieces




							def 			A__				(    self       ,A__):
												return self.sp_model.PieceToId(A__)




							def 			A__				(    self       ,A__):
												return self.sp_model.IdToPiece(A__)




							def 			A__				(    self       ,A__):
												lowercase						     =						''''''.join(A__).replace(A__       ,''' ''').strip()
												return out_string




							def 			A__				(    self       ,A__       ,A__ = False       ,A__ = None       ,A__ = True       ,**A__       ,):
												lowercase						     =						kwargs.pop('''use_source_tokenizer'''       ,A__)

												lowercase						     =						self.convert_ids_to_tokens(A__       ,skip_special_tokens=A__)

												# 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
												lowercase						     =						[]
												lowercase						     =						[]
												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(A__))
																											lowercase						     =						[]
																						sub_texts.append(A__)
																	else:
																						current_sub_text.append(A__)
												if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(A__))

												# Mimic the behavior of the Rust tokenizer:
												# By default, there are no spaces between special tokens
												lowercase						     =						''''''.join(A__)

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




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]
												lowercase						     =						[self.cls_token_id]
												if token_ids_a is None:
																	return token_ids_a + sep + cls
												return token_ids_a + sep + token_ids_a + sep + cls




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):

												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 not None:
																	return ([0] * len(A__)) + [1] + ([0] * len(A__)) + [1, 1]
												return ([0] * len(A__)) + [1, 1]




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]
												lowercase						     =						[2]

												if token_ids_a is None:
																	return len(token_ids_a + sep) * [0] + cls_segment_id
												return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id





							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						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						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)






from collections import OrderedDict
from typing import Mapping

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


lowercase__		:Optional[int]			  =   logging.get_logger(__name__)

lowercase__		:List[Any]			  =   {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[Any]	 ='''camembert'''




							def __init__(    self       ,A__=3_0_5_2_2       ,A__=7_6_8       ,A__=1_2       ,A__=1_2       ,A__=3_0_7_2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=2       ,A__=0.02       ,A__=1E-12       ,A__=1       ,A__=0       ,A__=2       ,A__="absolute"       ,A__=True       ,A__=None       ,**A__       ,):
												super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__)

												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						hidden_act
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						type_vocab_size
												lowercase						     =						initializer_range
												lowercase						     =						layer_norm_eps
												lowercase						     =						position_embedding_type
												lowercase						     =						use_cache
												lowercase						     =						classifier_dropout







class     lowercase		( SCREAMING_SNAKE_CASE__		):




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




