
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


SCREAMING_SNAKE_CASE							:List[str]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:List[Any]										=    '▁'

SCREAMING_SNAKE_CASE							:List[str]										=    {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

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

SCREAMING_SNAKE_CASE							:str										=    {
    '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',
    },
}

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

SCREAMING_SNAKE_CASE							:Union[str, Any]										=    {
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					["input_ids"]

	snake_case_    =					VOCAB_FILES_NAMES
	snake_case_    =					PRETRAINED_INIT_CONFIGURATION
	snake_case_    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_    =					PRETRAINED_VOCAB_FILES_MAP
	snake_case_    =					RESOURCE_FILES_NAMES






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

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

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






	def     UpperCamelCase_  (	self :			List[Any]							,A :			Tuple			):
					if text is None:
									return None

					__A             =			self.tokenize(A			)
					__A      ,							__A             =			"", []

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

					__A      ,							__A      ,							__A             =			normalized_text, [], 0

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

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

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






	@property
	def     UpperCamelCase_  (	self :			List[str]			):
					return len(self.vocab			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return dict(self.vocab							,**self.added_tokens_encoder			)






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






	def __setstate__(	self :			Union[str, Any]							,A :			List[Any]			):
					__A             =			d

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

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






	def     UpperCamelCase_  (	self :			List[Any]							,A :			List[str]			):
					return "".join((self.SP_CHAR_MAPPING.get(A							,A			) for c in text)			)






	def     UpperCamelCase_  (	self :			str							,A :			Dict							,A :			Optional[Any]=False							,A :			Optional[Any]=64							,A :			Tuple=0.1			):

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

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






	def     UpperCamelCase_  (	self :			List[str]							,A :			Tuple			):
					__A             =			"".join(A			).replace(A							," "			).strip()
					return out_string






	def     UpperCamelCase_  (	self :			int							,A :			str			):
					__A             =			self.convert_ids_to_tokens(A			)
					__A             =			"".join(A			).replace(A							," "			).strip()
					return out_string






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			List[str]			):
					return self.vocab.get(A							,self.vocab.get(self.unk_token			)			)






	def     UpperCamelCase_  (	self :			str							,A :			Tuple			):
					return self.reverse_vocab.get(A							,self.unk_token			)






	def     UpperCamelCase_  (	self :			List[str]							,A :			Optional[int]							,A :			Union[str, Any]=None			):
					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__A             =			[self.cls_token_id]
					__A             =			[self.sep_token_id]
					return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep






	def     UpperCamelCase_  (	self :			List[Any]							,A :			Any							,A :			Union[str, Any]=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 :			Optional[int]							,A :			Union[str, Any]							,A :			Dict=None							,A :			Union[str, 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(A			)) + [1, 1] + ([0] * len(A			)) + [1]
					return [1] + ([0] * len(A			)) + [1]






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			List[int]							,A :			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(A			) + 2) * [0]

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






	def     UpperCamelCase_  (	self :			Tuple							,A :			int			):
					if "\u4e00" <= char <= "\u9fff":
									return True
					return False






	def     UpperCamelCase_  (	self :			Dict							,A :			List[str]			):
					if ("a" <= char <= "z") or ("A" <= char <= "Z"):
									return True
					return False






	def     UpperCamelCase_  (	self :			int							,A :			str			):
					if char in ",;:.?!~，；：。？！《》【】":
									return True
					return False






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			List[Any]			):
					if char == " " or char == "\t" or char == "\n" or char == "\r":
									return True
					if len(A			) == 1:
									__A             =			unicodedata.category(A			)
									if cat == "Zs":
													return True
					return False






	def     UpperCamelCase_  (	self :			Dict							,A :			Optional[int]			):
					__A             =			{}
					with io.open(A							,"r"							,encoding="utf-8"			) as f:
									for index, line in enumerate(A			):
													__A             =			line.rstrip("\n"			)
													__A             =			int(A			)

					return token_to_idx







	def     UpperCamelCase_  (	self :			List[Any]							,A :			str							,A :			Optional[str] = None			):
					__A             =			0
					if os.path.isdir(A			):
									__A             =			os.path.join(
									    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
					else:
									__A             =			(filename_prefix + "-" if filename_prefix else "") + save_directory
					with open(A							,"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!"			)
																	__A             =			token_index
													writer.write(token + "\n"			)
													index += 1

					__A             =			os.path.join(A							,"sentencepiece.bpe.model"			)
					with open(A							,"wb"			) as fi:
									__A             =			self.sp_model.serialized_model_proto()
									fi.write(A			)

					return (vocab_file,)


from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE							:Dict										=    300  # TEMPERATURE (unit = K)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       )		->     float:







				"""simple docstring"""



				if donor_conc <= 0:
								raise ValueError("Donor concentration should be positive"						)
				elif acceptor_conc <= 0:
								raise ValueError("Acceptor concentration should be positive"						)
				elif intrinsic_conc <= 0:
								raise ValueError("Intrinsic concentration should be positive"						)
				elif donor_conc <= intrinsic_conc:
								raise ValueError(
								    "Donor concentration should be greater than intrinsic concentration"						)
				elif acceptor_conc <= intrinsic_conc:
								raise ValueError(
								    "Acceptor concentration should be greater than intrinsic concentration"						)
				else:
								return (
								    Boltzmann
								    * T
								    * log((donor_conc * acceptor_conc) / intrinsic_conc**2						)
								    / physical_constants["electron volt"][0]
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
