
'''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 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     tempfile.mkdtemp()

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

											UpperCamelCase   			=     BlipaProcessor(A_						,				A_     )

											processor.save_pretrained(self.tmpdirname     )







				def 						UpperCAmelCase_    (       self						,				**A_     )->		Tuple:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				**A_     )->		str:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											shutil.rmtree(self.tmpdirname     )







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







											'''simple docstring'''






											UpperCamelCase   			=     [np.random.randint(255						,				size=(3, 30, 400)						,				dtype=np.uinta     )]

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

											return image_inputs







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






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

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

											UpperCamelCase   			=     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 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     self.get_image_processor()
											UpperCamelCase   			=     self.get_tokenizer()

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

											UpperCamelCase   			=     self.prepare_image_inputs()

											UpperCamelCase   			=     image_processor(A_						,				return_tensors='np'     )
											UpperCamelCase   			=     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 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     self.get_image_processor()
											UpperCamelCase   			=     self.get_tokenizer()

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

											UpperCamelCase   			=     'lower newer'

											UpperCamelCase   			=     processor(text=A_     )

											UpperCamelCase   			=     tokenizer(A_						,				return_token_type_ids=A_     )

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







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     self.get_image_processor()
											UpperCamelCase   			=     self.get_tokenizer()

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

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

											UpperCamelCase   			=     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 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.get_image_processor()
											UpperCamelCase   			=     self.get_tokenizer()

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

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

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

											self.assertListEqual(A_						,				A_     )







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     self.get_image_processor()
											UpperCamelCase   			=     self.get_tokenizer()

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

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

											UpperCamelCase   			=     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']     )






'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase







def 		A_(     A						:		str):
							if not sentence:
														return ""
							UpperCamelCase   			=     dict(zip(A		,     A))
							return lower_to_upper.get(sentence[0]		,     sentence[0]) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()




