





"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


A 			=  logging.get_logger(__name__)

A 			=  '''▁'''

A 			=  {'''vocab_file''': '''sentencepiece.bpe.model'''}

A 			=  {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

A 			=  {
    '''facebook/mbart-large-en-ro''': 1_024,
    '''facebook/mbart-large-cc25''': 1_024,
}

# fmt: off
A 			=  ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  VOCAB_FILES_NAMES
					__lowerCAmelCase  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__lowerCAmelCase  =  PRETRAINED_VOCAB_FILES_MAP
					__lowerCAmelCase  =  ['''input_ids''', '''attention_mask''']

					__lowerCAmelCase  =  []
					__lowerCAmelCase  =  []







					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase="<s>"	,			_UpperCAmelCase="</s>"	,			_UpperCAmelCase="</s>"	,			_UpperCAmelCase="<s>"	,			_UpperCAmelCase="<unk>"	,			_UpperCAmelCase="<pad>"	,			_UpperCAmelCase="<mask>"	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			_UpperCAmelCase = None	,			_UpperCAmelCase=None	,			**_UpperCAmelCase	,			):
									# Mask token behave like a normal word, i.e. include the space before it
									__a :						List[str]			       =  AddedToken(_UpperCAmelCase	,			lstrip=_UpperCAmelCase	,			rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ) else mask_token

									__a :						Tuple			       =  {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_UpperCAmelCase	,			eos_token=_UpperCAmelCase	,			unk_token=_UpperCAmelCase	,			sep_token=_UpperCAmelCase	,			cls_token=_UpperCAmelCase	,			pad_token=_UpperCAmelCase	,			mask_token=_UpperCAmelCase	,			tokenizer_file=_UpperCAmelCase	,			src_lang=_UpperCAmelCase	,			tgt_lang=_UpperCAmelCase	,			additional_special_tokens=_UpperCAmelCase	,			sp_model_kwargs=self.sp_model_kwargs	,			**_UpperCAmelCase	,			)

									__a :						List[str]			       =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
									self.sp_model.Load(str(_UpperCAmelCase     )     )
									__a :						Tuple			       =  vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# Mimic fairseq token-to-id alignment for the first 4 token
									__a :						Optional[Any]			       =  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									__a :						List[Any]			       =  1

									__a :						Dict			       =  len(self.sp_model     )
									__a :						Any			       =  {
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCAmelCase     )
									}
									__a :						Optional[int]			       =  {v: k for k, v in self.lang_code_to_id.items()}
									__a :						Optional[int]			       =  len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id     )
									__a :						List[Any]			       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
									__a :						List[Any]			       =  list(self.lang_code_to_id.keys()     )

									if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													self._additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in self._additional_special_tokens]     )

									__a :						Any			       =  src_lang if src_lang is not None else '''en_XX'''
									__a :						List[Any]			       =  self.lang_code_to_id[self._src_lang]
									__a :						str			       =  tgt_lang
									self.set_src_lang_special_tokens(self._src_lang     )







					def __getstate__(						self     ):
									__a :						Dict			       =  self.__dict__.copy()
									__a :						str			       =  None
									__a :						Tuple			       =  self.sp_model.serialized_model_proto()
									return state







					def __setstate__(						self	,			_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  d

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

									__a :						List[str]			       =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )







					@property
					def  _lowerCamelCase		(						self     ):
									return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token







					@property
					def  _lowerCamelCase		(						self     ):
									return self._src_lang







					@src_lang.setter
					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						str			       =  new_src_lang
									self.set_src_lang_special_tokens(self._src_lang     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = False     ):

									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_UpperCAmelCase	,			token_ids_a=_UpperCAmelCase	,			already_has_special_tokens=_UpperCAmelCase     )

									__a :						List[str]			       =  [1] * len(self.prefix_tokens     )
									__a :						Any			       =  [1] * len(self.suffix_tokens     )
									if token_ids_a is None:
													return prefix_ones + ([0] * len(_UpperCAmelCase     )) + suffix_ones
									return prefix_ones + ([0] * len(_UpperCAmelCase     )) + ([0] * len(_UpperCAmelCase     )) + suffix_ones







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
									if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
									__a :						Union[str, Any]			       =  [self.sep_token_id]
									__a :						List[str]			       =  [self.cls_token_id]

									if token_ids_a is None:
													return len(cls + token_ids_a + sep     ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									if src_lang is None or tgt_lang is None:
													raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''     )
									__a :						Optional[int]			       =  src_lang
									__a :						List[Any]			       =  self(_UpperCAmelCase	,			add_special_tokens=_UpperCAmelCase	,			return_tensors=_UpperCAmelCase	,			**_UpperCAmelCase     )
									__a :						Any			       =  self.convert_tokens_to_ids(_UpperCAmelCase     )
									__a :						Any			       =  tgt_lang_id
									return inputs







					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  {self.convert_ids_to_tokens(_UpperCAmelCase     ): i for i in range(self.vocab_size     )}
									vocab.update(self.added_tokens_encoder     )
									return vocab







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return self.sp_model.encode(_UpperCAmelCase	,			out_type=_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
									__a :						Any			       =  self.sp_model.PieceToId(_UpperCAmelCase     )

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Dict			       =  ''''''.join(_UpperCAmelCase     ).replace(_UpperCAmelCase	,			''' '''     ).strip()
									return out_string







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
									if not os.path.isdir(_UpperCAmelCase     ):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
													return
									__a :						List[Any]			       =  os.path.join(
									    _UpperCAmelCase	,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

									if os.path.abspath(self.vocab_file     ) != os.path.abspath(_UpperCAmelCase     ) and os.path.isfile(self.vocab_file     ):
													copyfile(self.vocab_file	,			_UpperCAmelCase     )
									elif not os.path.isfile(self.vocab_file     ):
													with open(_UpperCAmelCase	,			'''wb'''     ) as fi:
																	__a :						str			       =  self.sp_model.serialized_model_proto()
																	fi.write(_UpperCAmelCase     )

									return (out_vocab_file,)







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = "en_XX"	,			_UpperCAmelCase = None	,			_UpperCAmelCase = "ro_RO"	,			**_UpperCAmelCase	,			):
									__a :						List[Any]			       =  src_lang
									__a :						Union[str, Any]			       =  tgt_lang
									return super().prepare_seqaseq_batch(_UpperCAmelCase	,			_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									return self.set_src_lang_special_tokens(self.src_lang     )







					def  _lowerCamelCase		(						self     ):
									return self.set_tgt_lang_special_tokens(self.tgt_lang     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Union[str, Any]			       =  self.lang_code_to_id[src_lang]
									__a :						int			       =  []
									__a :						str			       =  [self.eos_token_id, self.cur_lang_code]







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Tuple			       =  self.lang_code_to_id[lang]
									__a :						Optional[Any]			       =  []
									__a :						Dict			       =  [self.eos_token_id, self.cur_lang_code]






"""simple docstring"""






from math import log

from scipy.constants import Boltzmann, physical_constants

A 			=  300  # TEMPERATURE (unit = K)

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

				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()