



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


A__								=						logging.get_logger(__name__)

A__								=						'''▁'''

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

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

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

A__								=						{
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Any:


				"""simple docstring"""

				snake_case__	:      Any     		=	collections.OrderedDict()
				with open(__lowerCAmelCase  ,  '''r'''  ,  encoding='''utf-8'''      ) as reader:
								snake_case__	:      Optional[Any]     		=	reader.readlines()
				for index, token in enumerate(__lowerCAmelCase      ):
								snake_case__	:      Union[str, Any]     		=	token.rstrip('''\n'''      )
								snake_case__	:      Optional[Any]     		=	index
				return vocab





class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    Tuple											=					VOCAB_FILES_NAMES
		__lowerCAmelCase	:    Dict											=					PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase	:    Dict											=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase	:    Any											=					["""input_ids""", """attention_mask"""]



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

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

						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

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

						for i in range(1_0  ):
										snake_case__	:      Union[str, Any]     		=	F"""[unused{i}]"""
										snake_case__	:      int     		=	5 + i

						# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
						snake_case__	:      List[str]     		=	1_2
						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(__lowercase  )



		def __getstate__(     self     :int  ):
						snake_case__	:      Union[str, Any]     		=	self.__dict__.copy()
						snake_case__	:      Optional[int]     		=	None
						return state



		def __setstate__(     self     :Any   ,__lowercase     :str  ):
						snake_case__	:      Optional[int]     		=	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'''  ):
										snake_case__	:      Any     		=	{}

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



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

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

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



		def 	__lowerCamelCase			(     self     :str   ,__lowercase     :List[int]   ,__lowercase     :Optional[List[int]] = None  ):
						snake_case__	:      Union[str, 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 	__lowerCamelCase			(     self     :Optional[int]  ):
						return len(self.sp_model  ) + self.fairseq_offset



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      str     		=	{self.convert_ids_to_tokens(__lowercase  ): i for i in range(self.vocab_size  )}
						vocab.update(self.added_tokens_encoder  )
						return vocab



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



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :str  ):
						if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
						snake_case__	:      List[str]     		=	self.sp_model.PieceToId(__lowercase  )

						# 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     :int   ,__lowercase     :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 	__lowerCamelCase			(     self     :Any   ,__lowercase     :Optional[Any]  ):
						snake_case__	:      Tuple     		=	''''''.join(__lowercase  ).replace(__lowercase   ,''' '''  ).strip()
						return out_string



		def 	__lowerCamelCase			(     self     :Tuple   ,__lowercase     :str   ,__lowercase     :Optional[str] = None  ):
						if not os.path.isdir(__lowercase  ):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
										return
						snake_case__	:      Dict     		=	os.path.join(
						    __lowercase   ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

						if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowercase  ) and os.path.isfile(self.vocab_file  ):
										copyfile(self.vocab_file   ,__lowercase  )
						elif not os.path.isfile(self.vocab_file  ):
										with open(__lowercase   ,'''wb'''  ) as fi:
														snake_case__	:      Optional[int]     		=	self.sp_model.serialized_model_proto()
														fi.write(__lowercase  )

						return (out_vocab_file,)






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

						if token_ids_a is None:
										return token_ids_a + [self.sep_token_id]
						snake_case__	:      List[str]     		=	[self.sep_token_id]
						return token_ids_a + sep + token_ids_a + sep






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__								=						abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				from transformers.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(__lowerCAmelCase      )





def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				from transformers.testing_utils import pytest_terminal_summary_main

				snake_case__	:      Dict     		=	terminalreporter.config.getoption('''--make-reports'''      )
				if make_reports:
								pytest_terminal_summary_main(__lowerCAmelCase  ,  id=__lowerCAmelCase      )

