





"""simple docstring"""


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







@require_vision
class 	__a   (  unittest.TestCase		):



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					tempfile.mkdtemp()

            _lowerCamelCase    	=					CLIPImageProcessor()
            _lowerCamelCase    	=					BertTokenizer.from_pretrained(
                'hf-internal-testing/tiny-random-BertModel'					,     model_input_names=['input_ids', 'attention_mask']   )

            _lowerCamelCase    	=					GitProcessor(UpperCAmelCase_					,     UpperCAmelCase_   )

            processor.save_pretrained(self.tmpdirname   )



     def 						snake_case_			(       self					,     **a__   ):
            return AutoProcessor.from_pretrained(self.tmpdirname					,     **UpperCAmelCase_   ).tokenizer



     def 						snake_case_			(       self					,     **a__   ):
            return AutoProcessor.from_pretrained(self.tmpdirname					,     **UpperCAmelCase_   ).image_processor



     def 						snake_case_			(       self   ):
            shutil.rmtree(self.tmpdirname   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					[np.random.randint(2_55					,     size=(3, 30, 4_00)					,     dtype=np.uinta   )]

            _lowerCamelCase    	=					[Image.fromarray(np.moveaxis(UpperCAmelCase_					,     0					,     -1   )   ) for x in image_inputs]

            return image_inputs



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					GitProcessor(tokenizer=self.get_tokenizer()					,     image_processor=self.get_image_processor()   )
            processor.save_pretrained(self.tmpdirname   )

            _lowerCamelCase    	=					self.get_tokenizer(bos_token='(BOS)'					,     eos_token='(EOS)'   )
            _lowerCamelCase    	=					self.get_image_processor(do_normalize=UpperCAmelCase_					,     padding_value=1.0   )

            _lowerCamelCase    	=					GitProcessor.from_pretrained(
                self.tmpdirname					,     bos_token='(BOS)'					,     eos_token='(EOS)'					,     do_normalize=UpperCAmelCase_					,     padding_value=1.0   )

            self.assertEqual(processor.tokenizer.get_vocab()					,     tokenizer_add_kwargs.get_vocab()   )
            self.assertIsInstance(processor.tokenizer					,     UpperCAmelCase_   )

            self.assertEqual(processor.image_processor.to_json_string()					,     image_processor_add_kwargs.to_json_string()   )
            self.assertIsInstance(processor.image_processor					,     UpperCAmelCase_   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.get_image_processor()
            _lowerCamelCase    	=					self.get_tokenizer()

            _lowerCamelCase    	=					GitProcessor(tokenizer=UpperCAmelCase_					,     image_processor=UpperCAmelCase_   )

            _lowerCamelCase    	=					self.prepare_image_inputs()

            _lowerCamelCase    	=					image_processor(UpperCAmelCase_					,     return_tensors='np'   )
            _lowerCamelCase    	=					processor(images=UpperCAmelCase_					,     return_tensors='np'   )

            for key in input_feat_extract.keys():
                   self.assertAlmostEqual(input_feat_extract[key].sum()					,     input_processor[key].sum()					,     delta=1e-2   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.get_image_processor()
            _lowerCamelCase    	=					self.get_tokenizer()

            _lowerCamelCase    	=					GitProcessor(tokenizer=UpperCAmelCase_					,     image_processor=UpperCAmelCase_   )

            _lowerCamelCase    	=					'lower newer'

            _lowerCamelCase    	=					processor(text=UpperCAmelCase_   )

            _lowerCamelCase    	=					tokenizer(UpperCAmelCase_					,     return_token_type_ids=UpperCAmelCase_   )

            for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key]					,     encoded_processor[key]   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.get_image_processor()
            _lowerCamelCase    	=					self.get_tokenizer()

            _lowerCamelCase    	=					GitProcessor(tokenizer=UpperCAmelCase_					,     image_processor=UpperCAmelCase_   )

            _lowerCamelCase    	=					'lower newer'
            _lowerCamelCase    	=					self.prepare_image_inputs()

            _lowerCamelCase    	=					processor(text=UpperCAmelCase_					,     images=UpperCAmelCase_   )

            self.assertListEqual(list(inputs.keys()   )					,     ['input_ids', 'attention_mask', 'pixel_values']   )

            # test if it raises when no input is passed
            with pytest.raises(UpperCAmelCase_   ):
                   processor()



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.get_image_processor()
            _lowerCamelCase    	=					self.get_tokenizer()

            _lowerCamelCase    	=					GitProcessor(tokenizer=UpperCAmelCase_					,     image_processor=UpperCAmelCase_   )

            _lowerCamelCase    	=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _lowerCamelCase    	=					processor.batch_decode(UpperCAmelCase_   )
            _lowerCamelCase    	=					tokenizer.batch_decode(UpperCAmelCase_   )

            self.assertListEqual(UpperCAmelCase_					,     UpperCAmelCase_   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.get_image_processor()
            _lowerCamelCase    	=					self.get_tokenizer()

            _lowerCamelCase    	=					GitProcessor(tokenizer=UpperCAmelCase_					,     image_processor=UpperCAmelCase_   )

            _lowerCamelCase    	=					'lower newer'
            _lowerCamelCase    	=					self.prepare_image_inputs()

            _lowerCamelCase    	=					processor(text=UpperCAmelCase_					,     images=UpperCAmelCase_   )

            # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
            self.assertListEqual(list(inputs.keys()   )					,     ['input_ids', 'attention_mask', 'pixel_values']   )













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__  :	Union[str, Any]     =None


UpperCAmelCase__  :	str     =logging.get_logger(__name__)

UpperCAmelCase__  :	Optional[int]     ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__  :	str     ={
    '''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__  :	int     ={
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class 					__A   (  a			):
				__A            = VOCAB_FILES_NAMES
				__A            = PRETRAINED_VOCAB_FILES_MAP
				__A            = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A            = ["""input_ids""", """attention_mask"""]
				__A            = XLMRobertaTokenizer


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

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

								lowerCamelCase		       =vocab_file
								lowerCamelCase		       =False if not self.vocab_file else True


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):

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


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								lowerCamelCase		       =[self.sep_token_id]
								lowerCamelCase		       =[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 _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = 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(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_							):
												copyfile(self.vocab_file      ,       UpperCAmelCase_							)

								return (out_vocab_file,)





