




'''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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							tempfile.mkdtemp()

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

														_a      	=							BlipaProcessor(lowerCAmelCase_				,     lowerCAmelCase_   )

														processor.save_pretrained(self.tmpdirname   )







							def   __lowerCAmelCase     (    self			:    int				,     **lowerCAmelCase_			:    Any   )     ->				Any:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Tuple				,     **lowerCAmelCase_			:    Any   )     ->				Any:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[str]:


														"""simple docstring"""


														shutil.rmtree(self.tmpdirname   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				int:


														"""simple docstring"""


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

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

														return image_inputs







							def   __lowerCAmelCase     (    self			:    str   )     ->				Tuple:


														"""simple docstring"""


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

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

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

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

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







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							self.get_image_processor()
														_a      	=							self.get_tokenizer()

														_a      	=							BlipaProcessor(tokenizer=lowerCAmelCase_				,     image_processor=lowerCAmelCase_   )

														_a      	=							self.prepare_image_inputs()

														_a      	=							image_processor(lowerCAmelCase_				,     return_tensors='''np'''   )
														_a      	=							processor(images=lowerCAmelCase_				,     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   __lowerCAmelCase     (    self			:    Any   )     ->				str:


														"""simple docstring"""


														_a      	=							self.get_image_processor()
														_a      	=							self.get_tokenizer()

														_a      	=							BlipaProcessor(tokenizer=lowerCAmelCase_				,     image_processor=lowerCAmelCase_   )

														_a      	=							'''lower newer'''

														_a      	=							processor(text=lowerCAmelCase_   )

														_a      	=							tokenizer(lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_   )

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







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.get_image_processor()
														_a      	=							self.get_tokenizer()

														_a      	=							BlipaProcessor(tokenizer=lowerCAmelCase_				,     image_processor=lowerCAmelCase_   )

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

														_a      	=							processor(text=lowerCAmelCase_				,     images=lowerCAmelCase_   )

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

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







							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.get_image_processor()
														_a      	=							self.get_tokenizer()

														_a      	=							BlipaProcessor(tokenizer=lowerCAmelCase_				,     image_processor=lowerCAmelCase_   )

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

														_a      	=							processor.batch_decode(lowerCAmelCase_   )
														_a      	=							tokenizer.batch_decode(lowerCAmelCase_   )

														self.assertListEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							self.get_image_processor()
														_a      	=							self.get_tokenizer()

														_a      	=							BlipaProcessor(tokenizer=lowerCAmelCase_				,     image_processor=lowerCAmelCase_   )

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

														_a      	=							processor(text=lowerCAmelCase_				,     images=lowerCAmelCase_   )

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








"""simple docstring"""
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 return " ".join(input_str.split()[::-1]			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





