
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase :			Any              =	logging.get_logger(__name__)

_UpperCAmelCase :			Optional[Any]              =	'''▁'''

_UpperCAmelCase :			int              =	{'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

_UpperCAmelCase :			int              =	{
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

_UpperCAmelCase :			Any              =	{
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

_UpperCAmelCase :			Union[str, Any]              =	{
    '''ernie-m-base''': 514,
    '''ernie-m-large''': 514,
}

_UpperCAmelCase :			List[str]              =	{
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}


class     lowercase (		a_       ):
		__lowercase	:	str     					=    ["input_ids"]

		__lowercase	:	Tuple     					=    VOCAB_FILES_NAMES
		__lowercase	:	List[Any]     					=    PRETRAINED_INIT_CONFIGURATION
		__lowercase	:	Optional[Any]     					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase	:	Dict     					=    PRETRAINED_VOCAB_FILES_MAP
		__lowercase	:	Dict     					=    RESOURCE_FILES_NAMES



		def __init__(			self    ,				A_    ,				A_=None    ,				A_=False    ,				A_="utf8"    ,				A_="[UNK]"    ,				A_="[SEP]"    ,				A_="[PAD]"    ,				A_="[CLS]"    ,				A_="[MASK]"    ,				A_ = None    ,				**A_    ,				)  -> int:
				"""simple docstring"""
				# Mask token behave like a normal word, i.e. include the space before it and
				# is included in the raw text, there should be a match in a non-normalized sentence.

				UpperCamelCase          =					{} if sp_model_kwargs is None else sp_model_kwargs
				super().__init__(
				    do_lower_case=lowercase_    ,				unk_token=lowercase_    ,				sep_token=lowercase_    ,				pad_token=lowercase_    ,				cls_token=lowercase_    ,				mask_token=lowercase_    ,				vocab_file=lowercase_    ,				encoding=lowercase_    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**lowercase_    ,				)
				UpperCamelCase          =					do_lower_case
				UpperCamelCase          =					sentencepiece_model_ckpt
				UpperCamelCase          =					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.Load(lowercase_	)

				# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
				if vocab_file is not None:
						UpperCamelCase          =					self.load_vocab(filepath=lowercase_	)
				else:
						UpperCamelCase          =					{self.sp_model.id_to_piece(lowercase_	): id for id in range(self.sp_model.get_piece_size()	)}
				UpperCamelCase          =					{v: k for k, v in self.vocab.items()}



		def __UpperCamelCase			(			self    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				if text is None:
						return None

				UpperCamelCase          =					self.tokenize(lowercase_	)
				UpperCamelCase          =					'''''', []

				for i, ch in enumerate(lowercase_	):
						if ch in self.SP_CHAR_MAPPING:
								UpperCamelCase          =					self.SP_CHAR_MAPPING.get(lowercase_	)
						else:
								UpperCamelCase          =					unicodedata.normalize('NFKC'    ,				lowercase_	)
						if self.is_whitespace(lowercase_	):
								continue
						normalized_text += ch
						char_mapping.extend([i] * len(lowercase_	)	)

				UpperCamelCase          =					normalized_text, [], 0

				if self.do_lower_case:
						UpperCamelCase          =					text.lower()

				for token in split_tokens:
						if token[:1] == "▁":
								UpperCamelCase          =					token[1:]
						UpperCamelCase          =					text[offset:].index(lowercase_	) + offset
						UpperCamelCase          =					start + len(lowercase_	)

						token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)	)
						UpperCamelCase          =					end
				return token_mapping



		@property
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				return len(self.vocab	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return dict(self.vocab    ,				**self.added_tokens_encoder	)



		def __getstate__(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.__dict__.copy()
				UpperCamelCase          =					None
				return state



		def __setstate__(			self    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					d

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

				UpperCamelCase          =					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.Load(self.sentencepiece_model_ckpt	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				return "".join((self.SP_CHAR_MAPPING.get(lowercase_    ,				lowercase_	) for c in text)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_=False    ,				A_=64    ,				A_=0.1	)  -> List[str]:
				"""simple docstring"""

				if self.sp_model_kwargs.get('enable_sampling'	) is True:
						UpperCamelCase          =					True
				if self.sp_model_kwargs.get('alpha'	) is not None:
						UpperCamelCase          =					self.sp_model_kwargs.get('alpha'	)
				if self.sp_model_kwargs.get('nbest_size'	) is not None:
						UpperCamelCase          =					self.sp_model_kwargs.get('nbest_size'	)

				if not enable_sampling:
						UpperCamelCase          =					self.sp_model.EncodeAsPieces(lowercase_	)
				else:
						UpperCamelCase          =					self.sp_model.SampleEncodeAsPieces(lowercase_    ,				lowercase_    ,				lowercase_	)
				UpperCamelCase          =					[]
				for pi, piece in enumerate(lowercase_	):
						if piece == SPIECE_UNDERLINE:
								if not pieces[pi + 1].startswith(lowercase_	) and pi != 0:
										new_pieces.append(lowercase_	)
										continue
								else:
										continue
						UpperCamelCase          =					0
						for i, chunk in enumerate(lowercase_	):
								if chunk == SPIECE_UNDERLINE:
										continue
								if self.is_ch_char(lowercase_	) or self.is_punct(lowercase_	):
										if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
												new_pieces.append(piece[lst_i:i]	)
										new_pieces.append(lowercase_	)
										UpperCamelCase          =					i + 1
								elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
										if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
												new_pieces.append(piece[lst_i:i]	)
										UpperCamelCase          =					i
								elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
										if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
												new_pieces.append(piece[lst_i:i]	)
										UpperCamelCase          =					i
						if len(lowercase_	) > lst_i:
								new_pieces.append(piece[lst_i:]	)
				return new_pieces



		def __UpperCamelCase			(			self    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					''''''.join(lowercase_	).replace(lowercase_    ,				' '	).strip()
				return out_string



		def __UpperCamelCase			(			self    ,				A_	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.convert_ids_to_tokens(lowercase_	)
				UpperCamelCase          =					''''''.join(lowercase_	).replace(lowercase_    ,				' '	).strip()
				return out_string



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				return self.vocab.get(lowercase_    ,				self.vocab.get(self.unk_token	)	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				return self.reverse_vocab.get(lowercase_    ,				self.unk_token	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None	)  -> Tuple:
				"""simple docstring"""
				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase          =					[self.cls_token_id]
				UpperCamelCase          =					[self.sep_token_id]
				return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None	)  -> Optional[int]:
				"""simple docstring"""
				if offset_mapping_a is None:
						return [(0, 0)] + offset_mapping_a + [(0, 0)]

				return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]



		def __UpperCamelCase			(			self    ,				A_    ,				A_=None    ,				A_=False	)  -> Union[str, Any]:
				"""simple docstring"""

				if already_has_special_tokens:
						if token_ids_a is not None:
								raise ValueError(
								    'You should not supply a second sequence if the provided sequence of '
								    'ids is already formatted with special tokens for the model.'	)
						return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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



		def __UpperCamelCase			(			self    ,				A_    ,				A_ = None	)  -> List[Any]:
				"""simple docstring"""
				# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
				if token_ids_a is None:
						# [CLS] X [SEP]
						return (len(lowercase_	) + 2) * [0]

				# [CLS] A [SEP] [SEP] B [SEP]
				return [0] * (len(lowercase_	) + 1) + [1] * (len(lowercase_	) + 3)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				if "\u4e00" <= char <= "\u9fff":
						return True
				return False



		def __UpperCamelCase			(			self    ,				A_	)  -> Union[str, Any]:
				"""simple docstring"""
				if ("a" <= char <= "z") or ("A" <= char <= "Z"):
						return True
				return False



		def __UpperCamelCase			(			self    ,				A_	)  -> Tuple:
				"""simple docstring"""
				if char in ",;:.?!~，；：。？！《》【】":
						return True
				return False



		def __UpperCamelCase			(			self    ,				A_	)  -> str:
				"""simple docstring"""
				if char == " " or char == "\t" or char == "\n" or char == "\r":
						return True
				if len(lowercase_	) == 1:
						UpperCamelCase          =					unicodedata.category(lowercase_	)
						if cat == "Zs":
								return True
				return False



		def __UpperCamelCase			(			self    ,				A_	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					{}
				with io.open(lowercase_    ,				'r'    ,				encoding='utf-8'	) as f:
						for index, line in enumerate(lowercase_	):
								UpperCamelCase          =					line.rstrip('\n'	)
								UpperCamelCase          =					int(lowercase_	)

				return token_to_idx



		def __UpperCamelCase			(			self    ,				A_    ,				A_ = None	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					0
				if os.path.isdir(lowercase_	):
						UpperCamelCase          =					os.path.join(
						    lowercase_    ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
				else:
						UpperCamelCase          =					(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
				with open(lowercase_    ,				'w'    ,				encoding='utf-8'	) as writer:
						for token, token_index in sorted(self.vocab.items()    ,				key=lambda A_	: kv[1]	):
								if index != token_index:
										logger.warning(
										    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
										    ' Please check that the vocabulary is not corrupted!'	)
										UpperCamelCase          =					token_index
								writer.write(token + '\n'	)
								index += 1

				UpperCamelCase          =					os.path.join(lowercase_    ,				'sentencepiece.bpe.model'	)
				with open(lowercase_    ,				'wb'	) as fi:
						UpperCamelCase          =					self.sp_model.serialized_model_proto()
						fi.write(lowercase_	)

				return (vocab_file,)


import logging
import os

from .state import PartialState


class     lowercase (		logging.LoggerAdapter       ):



		@staticmethod
		def __UpperCamelCase			(			A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					PartialState()
				return not main_process_only or (main_process_only and state.is_main_process)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				*A_    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				if PartialState._shared_state == {}:
						raise RuntimeError(
						    'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'	)
				UpperCamelCase          =					kwargs.pop('main_process_only'    ,				A_	)
				UpperCamelCase          =					kwargs.pop('in_order'    ,				A_	)

				if self.isEnabledFor(A_	):
						if self._should_log(A_	):
								UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
								self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)

						elif in_order:
								UpperCamelCase          =					PartialState()
								for i in range(state.num_processes	):
										if i == state.process_index:
												UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
												self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)
										state.wait_for_everyone()






def 				A	( lowercase   ,	lowercase = None			)       ->  Dict:
		'''simple docstring'''

		if log_level is None:
				UpperCamelCase          =					os.environ.get('ACCELERATE_LOG_LEVEL'   ,	lowercase			)
		UpperCamelCase          =					logging.getLogger(lowercase			)
		if log_level is not None:
				logger.setLevel(log_level.upper()			)
				logger.root.setLevel(log_level.upper()			)
		return MultiProcessAdapter(lowercase   ,	{}			)
