

'''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


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= "▁"

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

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

lowerCAmelCase__										= {
    "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",
    },
}

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

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



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

							_lowerCamelCase		:      Union[str, Any]=   VOCAB_FILES_NAMES
							_lowerCamelCase		:      Dict=   PRETRAINED_INIT_CONFIGURATION
							_lowerCamelCase		:      Tuple=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:      Tuple=   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:      Optional[int]=   RESOURCE_FILES_NAMES


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

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


							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								if text is None:
									return None

								UpperCAmelCase_		:      Tuple       	=					self.tokenize(_snake_case)
								UpperCAmelCase_	,    UpperCAmelCase_		:      List[str]       	=					'', []

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

								UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					normalized_text, [], 0

								if self.do_lower_case:
									UpperCAmelCase_		:      str       	=					text.lower()

								for token in split_tokens:
									if token[:1] == "▁":
										UpperCAmelCase_		:      List[str]       	=					token[1:]
									UpperCAmelCase_		:      Optional[Any]       	=					text[offset:].index(_snake_case) + offset
									UpperCAmelCase_		:      Tuple       	=					start + len(_snake_case)

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


							@property
							def 						_snake_case							(			self)							->				Optional[int]:
								return len(self.vocab)


							def 						_snake_case							(			self)							->				int:
								return dict(self.vocab    ,			**self.added_tokens_encoder)


							def __getstate__(			self)							->				str:
								UpperCAmelCase_		:      List[Any]       	=					self.__dict__.copy()
								UpperCAmelCase_		:      Union[str, Any]       	=					None
								return state


							def __setstate__(			self    ,			_snake_case)							->				Any:
								UpperCAmelCase_		:      Optional[int]       	=					d

								# for backward compatibility
								if not hasattr(self    ,			'sp_model_kwargs'):
									UpperCAmelCase_		:      Optional[int]       	=					{}

								UpperCAmelCase_		:      Dict       	=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.sentencepiece_model_ckpt)


							def 						_snake_case							(			self    ,			_snake_case)							->				List[Any]:
								return "".join((self.SP_CHAR_MAPPING.get(_snake_case    ,			_snake_case) for c in text))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=False    ,			_snake_case=64    ,			_snake_case=0.1)							->				List[str]:

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

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


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      str       	=					''.join(_snake_case).replace(_snake_case    ,			' ').strip()
								return out_string


							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								UpperCAmelCase_		:      List[str]       	=					self.convert_ids_to_tokens(_snake_case)
								UpperCAmelCase_		:      Union[str, Any]       	=					''.join(_snake_case).replace(_snake_case    ,			' ').strip()
								return out_string


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[Any]:
								return self.vocab.get(_snake_case    ,			self.vocab.get(self.unk_token))


							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								return self.reverse_vocab.get(_snake_case    ,			self.unk_token)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=None)							->				Dict:
								if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase_		:      Tuple       	=					[self.cls_token_id]
								UpperCAmelCase_		:      Optional[int]       	=					[self.sep_token_id]
								return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=None)							->				Union[str, Any]:
								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    ,			_snake_case    ,			_snake_case=None    ,			_snake_case=False)							->				Any:

								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(_snake_case)) + [1, 1] + ([0] * len(_snake_case)) + [1]
								return [1] + ([0] * len(_snake_case)) + [1]


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				List[int]:
								# 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(_snake_case) + 2) * [0]

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


							def 						_snake_case							(			self    ,			_snake_case)							->				int:
								if "\u4e00" <= char <= "\u9fff":
									return True
								return False


							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								if ("a" <= char <= "z") or ("A" <= char <= "Z"):
									return True
								return False


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


							def 						_snake_case							(			self    ,			_snake_case)							->				Any:
								if char == " " or char == "\t" or char == "\n" or char == "\r":
									return True
								if len(_snake_case) == 1:
									UpperCAmelCase_		:      List[Any]       	=					unicodedata.category(_snake_case)
									if cat == "Zs":
										return True
								return False


							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      int       	=					{}
								with io.open(_snake_case    ,			'r'    ,			encoding='utf-8') as f:
									for index, line in enumerate(_snake_case):
										UpperCAmelCase_		:      int       	=					line.rstrip('\n')
										UpperCAmelCase_		:      Union[str, Any]       	=					int(_snake_case)

								return token_to_idx





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

								UpperCAmelCase_		:      Dict       	=					os.path.join(_snake_case    ,			'sentencepiece.bpe.model')
								with open(_snake_case    ,			'wb') as fi:
									UpperCAmelCase_		:      Optional[int]       	=					self.sp_model.serialized_model_proto()
									fi.write(_snake_case)

								return (vocab_file,)









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__										= {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__										= [
						    "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ClapModel",
						    "ClapPreTrainedModel",
						    "ClapTextModel",
						    "ClapTextModelWithProjection",
						    "ClapAudioModel",
						    "ClapAudioModelWithProjection",
						]
						lowerCAmelCase__										= ["ClapFeatureExtractor"]

if TYPE_CHECKING:
						from .configuration_clap import (
						    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ClapAudioConfig,
						    ClapConfig,
						    ClapTextConfig,
						)
						from .processing_clap import ClapProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_clap import ClapFeatureExtractor
												from .modeling_clap import (
												    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ClapAudioModel,
												    ClapAudioModelWithProjection,
												    ClapModel,
												    ClapPreTrainedModel,
												    ClapTextModel,
												    ClapTextModelWithProjection,
												)


else:
						import sys

						lowerCAmelCase__										= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






