
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__				:      List[Any]       )  ->    Dict:
							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					:     Any   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Union[str, Any]="[SEP]"   ,					__UpperCamelCase					:     Any="[SEP]"   ,					__UpperCamelCase					:     int="[SEP]"   ,					__UpperCamelCase					:     Optional[int]="[UNK]"   ,					__UpperCamelCase					:     Tuple="[PAD]"   ,					__UpperCamelCase					:     int="[CLS]"   ,					__UpperCamelCase					:     Any="[MASK]"   ,					__UpperCamelCase					:     Optional[Dict[str, Any]] = None   ,					**__UpperCamelCase					:     Dict   ,					):
												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					:     List[str]   ):
												lowerCamelCase_							  =					self.__dict__.copy()
												lowerCamelCase_							  =					None
												return state

					def __setstate__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ):
												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					:     Dict   ,					__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					:     Dict   ,					__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					:     Dict   ):
												return len(self.sp_model   ) + self.fairseq_offset

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

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Optional[int]   ):
												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					:     Dict   ,					__UpperCamelCase					:     Optional[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					:     int   ,					__UpperCamelCase					:     Tuple   ):
												lowerCamelCase_							  =					"""""".join(__UpperCamelCase   ).replace(__UpperCamelCase   ,					""" """   ).strip()
												return out_string

					def    lowercase__      (	self					:     List[str]   ,					__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					:     List[str]   ,					__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







from ..utils import DummyObject, requires_backends





class       __A(				metaclass=UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''torch''', '''scipy''']

					def __init__(	self					:     Any   ,					*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Any   ):
												requires_backends(self   ,					["""torch""", """scipy"""]   )

					@classmethod
					def    lowercase__      (	cls					:     Dict   ,					*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
												requires_backends(cls   ,					["""torch""", """scipy"""]   )



					@classmethod
					def    lowercase__      (	cls					:     str   ,					*__UpperCamelCase					:     Optional[int]   ,					**__UpperCamelCase					:     Optional[int]   ):
												requires_backends(cls   ,					["""torch""", """scipy"""]   )





