

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


a		=logging.get_logger(__name__)

a		="""▁"""

a		={"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}

a		={
    """sentencepiece_model_file""": """sentencepiece.bpe.model""",
    """vocab_file""": """vocab.txt""",
}

a		={
    """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""",
    },
}

a		={
    """ernie-m-base""": 514,
    """ernie-m-large""": 514,
}

a		={
    """ernie-m-base""": {"""do_lower_case""": False},
    """ernie-m-large""": {"""do_lower_case""": False},
}






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  List[str]						      =      ["input_ids"]

							_UpperCAmelCase    :  Dict						      =      VOCAB_FILES_NAMES
							_UpperCAmelCase    :  Any						      =      PRETRAINED_INIT_CONFIGURATION
							_UpperCAmelCase    :  Optional[Any]						      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase    :  Optional[int]						      =      PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase    :  List[Any]						      =      RESOURCE_FILES_NAMES
							def __init__(		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=None   ,SCREAMING_SNAKE_CASE__		:			int=False   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]="utf8"   ,SCREAMING_SNAKE_CASE__		:			List[Any]="[UNK]"   ,SCREAMING_SNAKE_CASE__		:			Any="[SEP]"   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]="[PAD]"   ,SCREAMING_SNAKE_CASE__		:			List[Any]="[CLS]"   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]="[MASK]"   ,SCREAMING_SNAKE_CASE__		:			Optional[Dict[str, Any]] = None   ,**SCREAMING_SNAKE_CASE__		:			List[Any]   ,):
								# 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.

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

								# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
								if vocab_file is not None:
									__lowerCamelCase				:				Union[str, Any]					       = self.load_vocab(filepath=SCREAMING_SNAKE_CASE__)
								else:
									__lowerCamelCase				:				List[Any]					       = {self.sp_model.id_to_piece(SCREAMING_SNAKE_CASE__): id for id in range(self.sp_model.get_piece_size())}
								__lowerCamelCase				:				Any					       = {v: k for k, v in self.vocab.items()}
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								if text is None:
									return None

								__lowerCamelCase				:				Optional[int]					       = self.tokenize(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = '', []

								for i, ch in enumerate(SCREAMING_SNAKE_CASE__):
									if ch in self.SP_CHAR_MAPPING:
										__lowerCamelCase				:				Union[str, Any]					       = self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE__)
									else:
										__lowerCamelCase				:				Optional[int]					       = unicodedata.normalize('NFKC'   ,SCREAMING_SNAKE_CASE__)
									if self.is_whitespace(SCREAMING_SNAKE_CASE__):
										continue
									normalized_text += ch
									char_mapping.extend([i] * len(SCREAMING_SNAKE_CASE__))

								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				int					       = normalized_text, [], 0

								if self.do_lower_case:
									__lowerCamelCase				:				Optional[Any]					       = text.lower()

								for token in split_tokens:
									if token[:1] == "▁":
										__lowerCamelCase				:				Any					       = token[1:]
									__lowerCamelCase				:				Union[str, Any]					       = text[offset:].index(SCREAMING_SNAKE_CASE__) + offset
									__lowerCamelCase				:				Tuple					       = start + len(SCREAMING_SNAKE_CASE__)

									token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
									__lowerCamelCase				:				List[Any]					       = end
								return token_mapping
							@property
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								return len(self.vocab)
							def     lowerCAmelCase     (		self		:			Any):
								return dict(self.vocab   ,**self.added_tokens_encoder)
							def __getstate__(		self		:			int):
								__lowerCamelCase				:				List[str]					       = self.__dict__.copy()
								__lowerCamelCase				:				List[str]					       = None
								return state
							def __setstate__(		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			List[Any]):
								__lowerCamelCase				:				int					       = d

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

								__lowerCamelCase				:				Optional[int]					       = spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.sentencepiece_model_ckpt)
							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			List[Any]):
								return "".join((self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for c in text))
							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=False   ,SCREAMING_SNAKE_CASE__		:			Dict=6_4   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=0.1):

								if self.sp_model_kwargs.get('enable_sampling') is True:
									__lowerCamelCase				:				Optional[int]					       = True
								if self.sp_model_kwargs.get('alpha') is not None:
									__lowerCamelCase				:				Any					       = self.sp_model_kwargs.get('alpha')
								if self.sp_model_kwargs.get('nbest_size') is not None:
									__lowerCamelCase				:				Tuple					       = self.sp_model_kwargs.get('nbest_size')

								if not enable_sampling:
									__lowerCamelCase				:				Dict					       = self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE__)
								else:
									__lowerCamelCase				:				Optional[int]					       = self.sp_model.SampleEncodeAsPieces(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = []
								for pi, piece in enumerate(SCREAMING_SNAKE_CASE__):
									if piece == SPIECE_UNDERLINE:
										if not pieces[pi + 1].startswith(SCREAMING_SNAKE_CASE__) and pi != 0:
											new_pieces.append(SCREAMING_SNAKE_CASE__)
											continue
										else:
											continue
									__lowerCamelCase				:				List[Any]					       = 0
									for i, chunk in enumerate(SCREAMING_SNAKE_CASE__):
										if chunk == SPIECE_UNDERLINE:
											continue
										if self.is_ch_char(SCREAMING_SNAKE_CASE__) or self.is_punct(SCREAMING_SNAKE_CASE__):
											if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
												new_pieces.append(piece[lst_i:i])
											new_pieces.append(SCREAMING_SNAKE_CASE__)
											__lowerCamelCase				:				Optional[int]					       = 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])
											__lowerCamelCase				:				Any					       = 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])
											__lowerCamelCase				:				Dict					       = i
									if len(SCREAMING_SNAKE_CASE__) > lst_i:
										new_pieces.append(piece[lst_i:])
								return new_pieces
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				List[str]					       = ''.join(SCREAMING_SNAKE_CASE__).replace(SCREAMING_SNAKE_CASE__   ,' ').strip()
								return out_string
							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				str					       = self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				str					       = ''.join(SCREAMING_SNAKE_CASE__).replace(SCREAMING_SNAKE_CASE__   ,' ').strip()
								return out_string
							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			List[Any]):
								return self.vocab.get(SCREAMING_SNAKE_CASE__   ,self.vocab.get(self.unk_token))
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								return self.reverse_vocab.get(SCREAMING_SNAKE_CASE__   ,self.unk_token)
							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=None):
								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				:				Dict					       = [self.sep_token_id]
								return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			int=None):
								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     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=None   ,SCREAMING_SNAKE_CASE__		:			List[Any]=False):

								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(SCREAMING_SNAKE_CASE__)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__)) + [1]
								return [1] + ([0] * len(SCREAMING_SNAKE_CASE__)) + [1]
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			List[int]   ,SCREAMING_SNAKE_CASE__		:			Optional[List[int]] = None):
								# 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(SCREAMING_SNAKE_CASE__) + 2) * [0]

								# [CLS] A [SEP] [SEP] B [SEP]
								return [0] * (len(SCREAMING_SNAKE_CASE__) + 1) + [1] * (len(SCREAMING_SNAKE_CASE__) + 3)
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]):
								if "\u4e00" <= char <= "\u9fff":
									return True
								return False
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]):
								if ("a" <= char <= "z") or ("A" <= char <= "Z"):
									return True
								return False
							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Any):
								if char in ",;:.?!~，；：。？！《》【】":
									return True
								return False
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			str):
								if char == " " or char == "\t" or char == "\n" or char == "\r":
									return True
								if len(SCREAMING_SNAKE_CASE__) == 1:
									__lowerCamelCase				:				int					       = unicodedata.category(SCREAMING_SNAKE_CASE__)
									if cat == "Zs":
										return True
								return False
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]):
								__lowerCamelCase				:				Any					       = {}
								with io.open(SCREAMING_SNAKE_CASE__   ,'r'   ,encoding='utf-8') as f:
									for index, line in enumerate(SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				int					       = line.rstrip('\n')
										__lowerCamelCase				:				str					       = int(SCREAMING_SNAKE_CASE__)

								return token_to_idx


							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Optional[str] = None):
								__lowerCamelCase				:				List[Any]					       = 0
								if os.path.isdir(SCREAMING_SNAKE_CASE__):
									__lowerCamelCase				:				Union[str, Any]					       = os.path.join(
									    SCREAMING_SNAKE_CASE__   ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
								else:
									__lowerCamelCase				:				str					       = (filename_prefix + '-' if filename_prefix else '') + save_directory
								with open(SCREAMING_SNAKE_CASE__   ,'w'   ,encoding='utf-8') as writer:
									for token, token_index in sorted(self.vocab.items()   ,key=lambda SCREAMING_SNAKE_CASE__: 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!')
											__lowerCamelCase				:				Dict					       = token_index
										writer.write(token + '\n')
										index += 1

								__lowerCamelCase				:				Any					       = os.path.join(SCREAMING_SNAKE_CASE__   ,'sentencepiece.bpe.model')
								with open(SCREAMING_SNAKE_CASE__   ,'wb') as fi:
									__lowerCamelCase				:				Dict					       = self.sp_model.serialized_model_proto()
									fi.write(SCREAMING_SNAKE_CASE__)

								return (vocab_file,)






import os

import numpy
import onnx







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     int       	=      a.name
       lowercase							:     Any       	=      b.name

       lowercase							:     Optional[Any]       	=      """"""
       lowercase							:     Dict       	=      """"""

       lowercase							:     int       	=      a == b

       lowercase							:     int       	=      name_a
       lowercase							:     List[str]       	=      name_b

       return res







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       for i, input_name in enumerate(node_proto.input				):
              if input_name == name:
                     node_proto.input.insert(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                     node_proto.input.pop(i + 1				)

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
              _graph_replace_input_with(node_proto.attribute[1].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       for n in graph_proto.node:
              _node_replace_input_with(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Any       	=      list(model.graph.initializer				)
       lowercase							:     Dict       	=      list(model_without_ext.graph.initializer				)
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              lowercase							:     Union[str, Any]       	=      inits[i].name
              lowercase							:     Dict       	=      inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]				)

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Union[str, Any]       	=      os.path.dirname(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      os.path.basename(SCREAMING_SNAKE_CASE__				)

       lowercase							:     str       	=      onnx.load(os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)				)

       lowercase							:     List[str]       	=      list(model.graph.initializer				)

       lowercase							:     Tuple       	=      set()
       lowercase							:     int       	=      {}
       lowercase							:     Optional[Any]       	=      []

       lowercase							:     Dict       	=      0

       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              if i in dup_set:
                     continue

              for j in range(i + 1					,	len(SCREAMING_SNAKE_CASE__				)				):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]					,	inits[j]				):
                            dup_set.add(SCREAMING_SNAKE_CASE__				)
                            dup_set.add(SCREAMING_SNAKE_CASE__				)

                            lowercase							:     int       	=      inits[j].data_type
                            lowercase							:     Optional[int]       	=      numpy.prod(inits[j].dims				)
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print("""unexpected data type: """					,	SCREAMING_SNAKE_CASE__				)
                            total_reduced_size += mem_size

                            lowercase							:     Tuple       	=      inits[i].name
                            lowercase							:     int       	=      inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(SCREAMING_SNAKE_CASE__				)
                            else:
                                   lowercase							:     List[str]       	=      [name_j]
                            ind_to_replace.append((j, i)				)

       print("""total reduced size: """					,	total_reduced_size / 1_024 / 1_024 / 1_024					,	"""GB"""				)

       lowercase							:     str       	=      sorted(SCREAMING_SNAKE_CASE__				)
       _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      """optimized_""" + model_file_name
       lowercase							:     Dict       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       onnx.save(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return new_model
