

'''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


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	'▁'

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

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

UpperCAmelCase					 =	{
    '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					 =	{
    'ernie-m-base': 514,
    'ernie-m-large': 514,
}

UpperCAmelCase					 =	{
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}


class 	lowerCAmelCase  (   _lowercase ):
       lowerCAmelCase_												=			["input_ids"]

       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			RESOURCE_FILES_NAMES







       def __init__(				self      :			Tuple    ,   __lowercase      :			str    ,   __lowercase      :			Optional[int]=None    ,   __lowercase      :			List[Any]=False    ,   __lowercase      :			Optional[Any]="utf8"    ,   __lowercase      :			int="[UNK]"    ,   __lowercase      :			Tuple="[SEP]"    ,   __lowercase      :			Dict="[PAD]"    ,   __lowercase      :			str="[CLS]"    ,   __lowercase      :			List[Any]="[MASK]"    ,   __lowercase      :			Dict = None    ,   **__lowercase      :			Union[str, Any]    ,   ):







            """simple docstring"""







            __lowercase      		={} 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    ,   )
            __lowercase      		=do_lower_case
            __lowercase      		=sentencepiece_model_ckpt
            __lowercase      		=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:
                 __lowercase      		=self.load_vocab(filepath=__SCREAMING_SNAKE_CASE   )
            else:
                 __lowercase      		={self.sp_model.id_to_piece(__SCREAMING_SNAKE_CASE   ): id for id in range(self.sp_model.get_piece_size()   )}
            __lowercase      		={v: k for k, v in self.vocab.items()}







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			Any   ):







            """simple docstring"""







            if text is None:
                 return None

            __lowercase      		=self.tokenize(__SCREAMING_SNAKE_CASE   )
            __lowercase		,  __lowercase      		='', []

            for i, ch in enumerate(__SCREAMING_SNAKE_CASE   ):
                 if ch in self.SP_CHAR_MAPPING:
                      __lowercase      		=self.SP_CHAR_MAPPING.get(__SCREAMING_SNAKE_CASE   )
                 else:
                      __lowercase      		=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   )   )

            __lowercase		,  __lowercase		,  __lowercase      		=normalized_text, [], 0

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

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

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







       @property
       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            return len(self.vocab   )







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            return dict(self.vocab    ,   **self.added_tokens_encoder   )







       def __getstate__(				self      :			Optional[int]   ):







            """simple docstring"""







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







       def __setstate__(				self      :			Dict    ,   __lowercase      :			str   ):







            """simple docstring"""







            __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.sentencepiece_model_ckpt   )







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			str   ):







            """simple docstring"""







            return "".join((self.SP_CHAR_MAPPING.get(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE   ) for c in text)   )







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			Any    ,   __lowercase      :			Tuple=False    ,   __lowercase      :			Optional[Any]=64    ,   __lowercase      :			int=0.1   ):







            """simple docstring"""







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

            if not enable_sampling:
                 __lowercase      		=self.sp_model.EncodeAsPieces(__SCREAMING_SNAKE_CASE   )
            else:
                 __lowercase      		=self.sp_model.SampleEncodeAsPieces(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE   )
            __lowercase      		=[]
            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
                 __lowercase      		=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   )
                           __lowercase      		=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]   )
                           __lowercase      		=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]   )
                           __lowercase      		=i
                 if len(__SCREAMING_SNAKE_CASE   ) > lst_i:
                      new_pieces.append(piece[lst_i:]   )
            return new_pieces







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=''.join(__SCREAMING_SNAKE_CASE   ).replace(__SCREAMING_SNAKE_CASE    ,   ' '   ).strip()
            return out_string







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE   )
            __lowercase      		=''.join(__SCREAMING_SNAKE_CASE   ).replace(__SCREAMING_SNAKE_CASE    ,   ' '   ).strip()
            return out_string







       def       snake_case (				self      :			int    ,   __lowercase      :			List[Any]   ):







            """simple docstring"""







            return self.vocab.get(__SCREAMING_SNAKE_CASE    ,   self.vocab.get(self.unk_token   )   )







       def       snake_case (				self      :			Dict    ,   __lowercase      :			List[str]   ):







            """simple docstring"""







            return self.reverse_vocab.get(__SCREAMING_SNAKE_CASE    ,   self.unk_token   )







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			Dict    ,   __lowercase      :			Tuple=None   ):







            """simple docstring"""







            if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            __lowercase      		=[self.cls_token_id]
            __lowercase      		=[self.sep_token_id]
            return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			List[str]    ,   __lowercase      :			Dict=None   ):







            """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       snake_case (				self      :			Any    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			Dict=None    ,   __lowercase      :			Tuple=False   ):







            """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(__SCREAMING_SNAKE_CASE   )) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE   )) + [1]
            return [1] + ([0] * len(__SCREAMING_SNAKE_CASE   )) + [1]







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			Any = None   ):







            """simple docstring"""







            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       snake_case (				self      :			Dict    ,   __lowercase      :			Tuple   ):







            """simple docstring"""







            if "\u4e00" <= char <= "\u9fff":
                 return True
            return False







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Dict   ):







            """simple docstring"""







            if ("a" <= char <= "z") or ("A" <= char <= "Z"):
                 return True
            return False







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			Any   ):







            """simple docstring"""







            if char in ",;:.?!~，；：。？！《》【】":
                 return True
            return False







       def       snake_case (				self      :			Dict    ,   __lowercase      :			List[Any]   ):







            """simple docstring"""







            if char == " " or char == "\t" or char == "\n" or char == "\r":
                 return True
            if len(__SCREAMING_SNAKE_CASE   ) == 1:
                 __lowercase      		=unicodedata.category(__SCREAMING_SNAKE_CASE   )
                 if cat == "Zs":
                      return True
            return False







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			Dict   ):







            """simple docstring"""







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

            return token_to_idx







       def       snake_case (				self      :			str    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			Any = None   ):







            """simple docstring"""







            __lowercase      		=0
            if os.path.isdir(__SCREAMING_SNAKE_CASE   ):
                 __lowercase      		=os.path.join(
                     __SCREAMING_SNAKE_CASE    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
            else:
                 __lowercase      		=(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 __lowercase   : 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!'   )
                           __lowercase      		=token_index
                      writer.write(token + '\n'   )
                      index += 1

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

            return (vocab_file,)




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		)     ->						float:
    a__		=			[redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters     ):
        raise ValueError('All input parameters must be positive'     )

    if any(p > 1 for p in parameters[1:4]     ):
        raise ValueError('Relative densities cannot be greater than one'     )
    else:
        a__		=			1 - (matter_density + radiation_density + dark_energy)

        a__		=			(
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        a__		=			hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo LCDM approximation
    a		:       Any     						=       0.3

    print(
        hubble_parameter(
            hubble_constant=68.3,
            radiation_density=1e-4,
            matter_density=matter_density,
            dark_energy=1 - matter_density,
            redshift=0,
        )
    )


