





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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast



@require_vision
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			tempfile.mkdtemp()

        SCREAMING_SNAKE_CASE__											=			BlipImageProcessor()
        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''     )

        SCREAMING_SNAKE_CASE__											=			BlipaProcessor(A_   ,				A_     )

        processor.save_pretrained(self.tmpdirname     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        return AutoProcessor.from_pretrained(self.tmpdirname   ,				**A_     ).tokenizer



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        return AutoProcessor.from_pretrained(self.tmpdirname   ,				**A_     ).image_processor



    def  lowercase_					(       self     ):





        '''simple docstring'''

        shutil.rmtree(self.tmpdirname     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[np.random.randint(2_55   ,				size=(3, 30, 4_00)   ,				dtype=np.uinta     )]

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

        return image_inputs



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BlipaProcessor(tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

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

        SCREAMING_SNAKE_CASE__											=			BlipaProcessor.from_pretrained(
            self.tmpdirname   ,				bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''   ,				do_normalize=A_   ,				padding_value=1.0     )

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

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.get_image_processor()
        SCREAMING_SNAKE_CASE__											=			self.get_tokenizer()

        SCREAMING_SNAKE_CASE__											=			BlipaProcessor(tokenizer=A_   ,				image_processor=A_     )

        SCREAMING_SNAKE_CASE__											=			self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE__											=			image_processor(A_   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			processor(images=A_   ,				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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.get_image_processor()
        SCREAMING_SNAKE_CASE__											=			self.get_tokenizer()

        SCREAMING_SNAKE_CASE__											=			BlipaProcessor(tokenizer=A_   ,				image_processor=A_     )

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''

        SCREAMING_SNAKE_CASE__											=			processor(text=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				return_token_type_ids=A_     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.get_image_processor()
        SCREAMING_SNAKE_CASE__											=			self.get_tokenizer()

        SCREAMING_SNAKE_CASE__											=			BlipaProcessor(tokenizer=A_   ,				image_processor=A_     )

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

        SCREAMING_SNAKE_CASE__											=			processor(text=A_   ,				images=A_     )

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

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.get_image_processor()
        SCREAMING_SNAKE_CASE__											=			self.get_tokenizer()

        SCREAMING_SNAKE_CASE__											=			BlipaProcessor(tokenizer=A_   ,				image_processor=A_     )

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

        SCREAMING_SNAKE_CASE__											=			processor.batch_decode(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.batch_decode(A_     )

        self.assertListEqual(A_   ,				A_     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.get_image_processor()
        SCREAMING_SNAKE_CASE__											=			self.get_tokenizer()

        SCREAMING_SNAKE_CASE__											=			BlipaProcessor(tokenizer=A_   ,				image_processor=A_     )

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

        SCREAMING_SNAKE_CASE__											=			processor(text=A_   ,				images=A_     )

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







import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	BioGptTokenizer
    lowerCamelCase__    :		List[Any]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__											=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']     )
        with open(self.vocab_file   ,				'''w'''     ) as fp:
            fp.write(json.dumps(A_     )     )
        with open(self.merges_file   ,				'''w'''     ) as fp:
            fp.write('''\n'''.join(A_     )     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        return input_text, output_text



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer(self.vocab_file   ,				self.merges_file     )

        SCREAMING_SNAKE_CASE__											=			'''lower'''
        SCREAMING_SNAKE_CASE__											=			['''low''', '''er</w>''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + ['''<unk>''']
        SCREAMING_SNAKE_CASE__											=			[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''sequence builders'''   ,				add_special_tokens=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''multi-sequence build'''   ,				add_special_tokens=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_   ,				A_     )

        self.assertTrue(encoded_sentence == [2] + text     )
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a     )
