
"""simple docstring"""


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  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      List[str]          =    ["input_ids"]

				a__:      Dict          =    VOCAB_FILES_NAMES
				a__:      Any          =    PRETRAINED_INIT_CONFIGURATION
				a__:      Tuple          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__:      Optional[int]          =    PRETRAINED_VOCAB_FILES_MAP
				a__:      str          =    RESOURCE_FILES_NAMES




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=None							,						UpperCAmelCase=False							,						UpperCAmelCase="utf8"							,						UpperCAmelCase="[UNK]"							,						UpperCAmelCase="[SEP]"							,						UpperCAmelCase="[PAD]"							,						UpperCAmelCase="[CLS]"							,						UpperCAmelCase="[MASK]"							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							# 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_					=  {} if sp_model_kwargs is None else sp_model_kwargs
							super().__init__(
							    do_lower_case=UpperCAmelCase							,						unk_token=UpperCAmelCase							,						sep_token=UpperCAmelCase							,						pad_token=UpperCAmelCase							,						cls_token=UpperCAmelCase							,						mask_token=UpperCAmelCase							,						vocab_file=UpperCAmelCase							,						encoding=UpperCAmelCase							,						sp_model_kwargs=self.sp_model_kwargs							,						**UpperCAmelCase							,						)
							lowerCamelCase_					=  do_lower_case
							lowerCamelCase_					=  sentencepiece_model_ckpt
							lowerCamelCase_					=  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							if text is None:
										return None

							lowerCamelCase_					=  self.tokenize(UpperCAmelCase   )
							lowerCamelCase_		, lowerCamelCase_					=  '''''', []

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

							lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  normalized_text, [], 0

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

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

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




				@property
				def        UpperCAmelCase__						(				self   ):
							return len(self.vocab   )




				def        UpperCAmelCase__						(				self   ):
							return dict(self.vocab							,						**self.added_tokens_encoder   )




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




				def __setstate__(				self							,						UpperCAmelCase   ):
							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.sentencepiece_model_ckpt   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return "".join((self.SP_CHAR_MAPPING.get(UpperCAmelCase							,						UpperCAmelCase   ) for c in text)   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=False							,						UpperCAmelCase=64							,						UpperCAmelCase=0.1   ):

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

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  ''''''.join(UpperCAmelCase   ).replace(UpperCAmelCase							,						''' '''   ).strip()
							return out_string




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.convert_ids_to_tokens(UpperCAmelCase   )
							lowerCamelCase_					=  ''''''.join(UpperCAmelCase   ).replace(UpperCAmelCase							,						''' '''   ).strip()
							return out_string




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return self.vocab.get(UpperCAmelCase							,						self.vocab.get(self.unk_token   )   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return self.reverse_vocab.get(UpperCAmelCase							,						self.unk_token   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None   ):
							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowerCamelCase_					=  [self.cls_token_id]
							lowerCamelCase_					=  [self.sep_token_id]
							return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=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        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None							,						UpperCAmelCase=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(UpperCAmelCase   )) + [1, 1] + ([0] * len(UpperCAmelCase   )) + [1]
							return [1] + ([0] * len(UpperCAmelCase   )) + [1]




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = 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(UpperCAmelCase   ) + 2) * [0]

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							if "\u4e00" <= char <= "\u9fff":
										return True
							return False




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							if ("a" <= char <= "z") or ("A" <= char <= "Z"):
										return True
							return False




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							if char in ",;:.?!~，；：。？！《》【】":
										return True
							return False




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							if char == " " or char == "\t" or char == "\n" or char == "\r":
										return True
							if len(UpperCAmelCase   ) == 1:
										lowerCamelCase_					=  unicodedata.category(UpperCAmelCase   )
										if cat == "Zs":
													return True
							return False




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  {}
							with io.open(UpperCAmelCase							,						'''r'''							,						encoding='''utf-8'''   ) as f:
										for index, line in enumerate(UpperCAmelCase   ):
													lowerCamelCase_					=  line.rstrip('''\n'''   )
													lowerCamelCase_					=  int(UpperCAmelCase   )

							return token_to_idx





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None   ):
							lowerCamelCase_					=  0
							if os.path.isdir(UpperCAmelCase   ):
										lowerCamelCase_					=  os.path.join(
										    UpperCAmelCase							,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
							else:
										lowerCamelCase_					=  (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
							with open(UpperCAmelCase							,						'''w'''							,						encoding='''utf-8'''   ) as writer:
										for token, token_index in sorted(self.vocab.items()							,						key=lambda UpperCAmelCase   : 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_					=  token_index
													writer.write(token + '''\n'''   )
													index += 1

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

							return (vocab_file,)








'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




