

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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
			UpperCAmelCase_       :				int		   =      None


UpperCAmelCase_       :				List[Any]		   =      logging.get_logger(__name__)

UpperCAmelCase_       :				Tuple		   =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_       :				Optional[Any]		   =      {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_       :				Any		   =      {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}






class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      str    				=							VOCAB_FILES_NAMES
	_lowercase		:      List[str]    				=							PRETRAINED_VOCAB_FILES_MAP
	_lowercase		:      Tuple    				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase		:      Union[str, Any]    				=							['''input_ids''', '''attention_mask''']
	_lowercase		:      int    				=							XLMRobertaTokenizer




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

		super().__init__(
		    __A				,			tokenizer_file=__A				,			bos_token=__A				,			eos_token=__A				,			sep_token=__A				,			cls_token=__A				,			unk_token=__A				,			pad_token=__A				,			mask_token=__A				,			**__A				,			)

		__A   : Optional[int]								= vocab_file
		__A   : Dict								= False if not self.vocab_file else True




	def 					lowerCAmelCase_					(    self	:    Optional[Any]				,			__A	:    List[int]				,			__A	:    Optional[List[int]] = None       ):

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




	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    List[int]				,			__A	:    Optional[List[int]] = None       ):
		__A   : Optional[int]								= [self.sep_token_id]
		__A   : List[Any]								= [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	:    List[Any]				,			__A	:    str				,			__A	:    Optional[str] = None       ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
			    """tokenizer."""       )

		if not os.path.isdir(__A       ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""       )
			return
		__A   : List[str]								= os.path.join(
		    __A				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

		if os.path.abspath(self.vocab_file       ) != os.path.abspath(__A       ):
			copyfile(self.vocab_file				,			__A       )

		return (out_vocab_file,)








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
			from PIL import Image






class     lowerCamelCase_			(  unittest.TestCase	,			_lowercase  ):




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : str								= load_tool("""image-captioning"""       )
		self.tool.setup()
		__A   : Dict								= load_tool("""image-captioning"""				,			remote=__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
		__A   : List[Any]								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : Optional[Any]								= self.tool(__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : Any								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : List[str]								= self.remote_tool(__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
		__A   : Optional[Any]								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : str								= self.tool(image=__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )




	def 					lowerCAmelCase_					(    self	:    str       ):
		__A   : Dict								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : List[Any]								= self.remote_tool(image=__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )





