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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase   =	logging.get_logger(__name__)

lowercase   =	'''▁'''

lowercase   =	{'''vocab_file''': '''prophetnet.tokenizer'''}

lowercase   =	{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

lowercase   =	{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

lowercase   =	{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_1_2,
}



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Dict							)	->     Any:
							lowerCamelCase_					=       collections.OrderedDict()
							with open(UpperCAmelCase__      ,					"""r"""      ,					encoding="""utf-8"""							) as reader:
														lowerCamelCase_					=       reader.readlines()
							for index, token in enumerate(UpperCAmelCase__							):
														lowerCamelCase_					=       token.rstrip("""\n"""							)
														lowerCamelCase_					=       index
							return vocab





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE           =     PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE           =     ['''input_ids''', '''attention_mask''']

					def __init__(						self       :  int ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  str="[SEP]" ,       __UpperCamelCase       :  List[str]="[SEP]" ,       __UpperCamelCase       :  Union[str, Any]="[SEP]" ,       __UpperCamelCase       :  List[Any]="[UNK]" ,       __UpperCamelCase       :  List[Any]="[PAD]" ,       __UpperCamelCase       :  Any="[CLS]" ,       __UpperCamelCase       :  Optional[int]="[MASK]" ,       __UpperCamelCase       :  Optional[Dict[str, Any]] = None ,       **__UpperCamelCase       :  Union[str, Any] ,       ):
												lowerCamelCase_					=       {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=__UpperCamelCase ,       eos_token=__UpperCamelCase ,       sep_token=__UpperCamelCase ,       unk_token=__UpperCamelCase ,       pad_token=__UpperCamelCase ,       cls_token=__UpperCamelCase ,       mask_token=__UpperCamelCase ,       sp_model_kwargs=self.sp_model_kwargs ,       **__UpperCamelCase ,       )

												try:
																			import sentencepiece as spm
												except ImportError:
																			logger.warning(
																			    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
																			    """ pip install sentencepiece""" )
																			raise

												lowerCamelCase_					=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
												self.sp_model.Load(str(__UpperCamelCase ) )
												lowerCamelCase_					=       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'

												# put special tokens and [unused] tokens into the vocab
												lowerCamelCase_					=       {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

												for i in range(1_0 ):
																			lowerCamelCase_					=       F'''[unused{i}]'''
																			lowerCamelCase_					=       5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												lowerCamelCase_					=       1_2
												lowerCamelCase_					=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																			self.unique_no_split_tokens.append(__UpperCamelCase )

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

					def __setstate__(						self       :  Dict ,       __UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       d
												try:
																			import sentencepiece as spm
												except ImportError:
																			logger.warning(
																			    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
																			    """ pip install sentencepiece""" )
																			raise

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

												lowerCamelCase_					=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
												self.sp_model.Load(self.vocab_file )

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ,       __UpperCamelCase       :  bool = 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 )

												if token_ids_a is None:
																			return ([0] * len(__UpperCamelCase )) + [1]
												return ([0] * len(__UpperCamelCase )) + [1] + ([0] * len(__UpperCamelCase )) + [1]

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       [self.sep_token_id]

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

					@property
					def  lowercase__   (						self       :  Any ):
												return len(self.sp_model ) + self.fairseq_offset

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       {self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )}
												vocab.update(self.added_tokens_encoder )
												return vocab

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  str ):
												return self.sp_model.encode(__UpperCamelCase ,       out_type=__UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  Any ):
												if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
												lowerCamelCase_					=       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  lowercase__   (						self       :  str ,       __UpperCamelCase       :  List[Any] ):
												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  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  Dict ):
												lowerCamelCase_					=       """""".join(__UpperCamelCase ).replace(__UpperCamelCase ,       """ """ ).strip()
												return out_string

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] = None ):
												if not os.path.isdir(__UpperCamelCase ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																			return
												lowerCamelCase_					=       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:
																										lowerCamelCase_					=       self.sp_model.serialized_model_proto()
																										fi.write(__UpperCamelCase )

												return (out_vocab_file,)



					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):

												if token_ids_a is None:
																			return token_ids_a + [self.sep_token_id]
												lowerCamelCase_					=       [self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep







lowercase   =	8.314_4598



def   __lowerCAmelCase    (				UpperCAmelCase__					:   float      ,					UpperCAmelCase__					:   float							)	->     float:
							if temperature < 0:
														raise Exception("""Temperature cannot be less than 0 K"""							)
							if molar_mass <= 0:
														raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""							)
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		lowercase   =	3_0_0
		lowercase   =	2_8
		lowercase   =	rms_speed_of_molecule(temperature, molar_mass)
		print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")





