



'''simple docstring'''



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


lowerCAmelCase_     :       Dict								=						logging.get_logger(__name__)

lowerCAmelCase_     :       str								=						'▁'

lowerCAmelCase_     :       List[Any]								=						{'vocab_file': 'prophetnet.tokenizer'}

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

lowerCAmelCase_     :       List[str]								=						{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

lowerCAmelCase_     :       Union[str, Any]								=						{
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}
def 					UpperCAmelCase   (						A  :				List[Any]				):
							SCREAMING_SNAKE_CASE					:      Dict     					=			collections.OrderedDict()
							with open(A					,		'''r'''					,		encoding='''utf-8'''				) as reader:
														SCREAMING_SNAKE_CASE					:      str     					=			reader.readlines()
							for index, token in enumerate(A				):
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			token.rstrip('''\n'''				)
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			index
							return vocab






class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   Union[str, Any]	     =				VOCAB_FILES_NAMES
						_lowerCAmelCase     :   Optional[Any]	     =				PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase     :   Tuple	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase     :   Tuple	     =				['input_ids', 'attention_mask']







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{} if sp_model_kwargs is None else sp_model_kwargs

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

													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

													SCREAMING_SNAKE_CASE					:      List[str]     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(str(lowerCAmelCase__				)				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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
													SCREAMING_SNAKE_CASE					:      int     					=			{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

													for i in range(10				):
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			F"""[unused{i}]"""
																				SCREAMING_SNAKE_CASE					:      Any     					=			5 + i

													# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
													SCREAMING_SNAKE_CASE					:      Dict     					=			12
													SCREAMING_SNAKE_CASE					:      List[str]     					=			{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(lowerCAmelCase__				)







						def __getstate__(  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.__dict__.copy()
													SCREAMING_SNAKE_CASE					:      Dict     					=			None
													return state







						def __setstate__(  self   :      List[Any]				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			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'''				):
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			{}

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(self.vocab_file				)







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      List[int]				,    lowerCAmelCase__   :      Optional[List[int]] = None				,    lowerCAmelCase__   :      bool = False				):

													"""simple docstring"""

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

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







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      List[int]				,    lowerCAmelCase__   :      Optional[List[int]] = None				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[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				):

													"""simple docstring"""
													return len(self.sp_model				) + self.fairseq_offset







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			{self.convert_ids_to_tokens(lowerCAmelCase__				): i for i in range(self.vocab_size				)}
													vocab.update(self.added_tokens_encoder				)
													return vocab







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

													"""simple docstring"""
													return self.sp_model.encode(lowerCAmelCase__				,    out_type=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													if token in self.fairseq_tokens_to_ids:
																				return self.fairseq_tokens_to_ids[token]
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.sp_model.PieceToId(lowerCAmelCase__				)

													# 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   :      int				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													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   :      Dict				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			''''''.join(lowerCAmelCase__				).replace(lowerCAmelCase__				,    ''' '''				).strip()
													return out_string







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

													"""simple docstring"""
													if not os.path.isdir(lowerCAmelCase__				):
																				logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
																				return
													SCREAMING_SNAKE_CASE					:      int     					=			os.path.join(
													    lowerCAmelCase__				,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

													if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase__				) and os.path.isfile(self.vocab_file				):
																				copyfile(self.vocab_file				,    lowerCAmelCase__				)
													elif not os.path.isfile(self.vocab_file				):
																				with open(lowerCAmelCase__				,    '''wb'''				) as fi:
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.sp_model.serialized_model_proto()
																											fi.write(lowerCAmelCase__				)

													return (out_vocab_file,)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      List[int]				,    lowerCAmelCase__   :      Optional[List[int]] = None				):

													"""simple docstring"""

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









'''simple docstring'''



from bisect import bisect
from itertools import accumulate
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Optional[Any]					,		A  :				Dict					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			sorted(zip(A					,		A				)					,		key=lambda A				: x[0] / x[1]					,		reverse=A				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			[i[0] for i in r], [i[1] for i in r]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			list(accumulate(A				)				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			bisect(A					,		A				)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]				)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




