





"""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       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       tempfile.mkdtemp()

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

					A__							 =       BlipaProcessor(__a  ,					__a						)

					processor.save_pretrained(self.tmpdirname						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					shutil.rmtree(self.tmpdirname						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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

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

					return image_inputs


	def       _UpperCAmelCase						(      self						):


					"""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=__a  ,					padding_value=1.0						)

					A__							 =       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						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_tokenizer()

					A__							 =       BlipaProcessor(tokenizer=__a  ,					image_processor=__a						)

					A__							 =       self.prepare_image_inputs()

					A__							 =       image_processor(__a  ,					return_tensors='np'						)
					A__							 =       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						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_tokenizer()

					A__							 =       BlipaProcessor(tokenizer=__a  ,					image_processor=__a						)

					A__							 =       'lower newer'

					A__							 =       processor(text=__a						)

					A__							 =       tokenizer(__a  ,					return_token_type_ids=__a						)

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_tokenizer()

					A__							 =       BlipaProcessor(tokenizer=__a  ,					image_processor=__a						)

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

					A__							 =       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						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_tokenizer()

					A__							 =       BlipaProcessor(tokenizer=__a  ,					image_processor=__a						)

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

					A__							 =       processor.batch_decode(__a						)
					A__							 =       tokenizer.batch_decode(__a						)

					self.assertListEqual(__a  ,					__a						)






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_tokenizer()

					A__							 =       BlipaProcessor(tokenizer=__a  ,					image_processor=__a						)

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

					A__							 =       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"""






import numpy as np



def  __lowerCamelCase					(					lowerCAmelCase__				):
				return 1 / (1 + np.exp(-vector				))



def  __lowerCamelCase					(					lowerCAmelCase__				):
				return vector * sigmoid(lowerCAmelCase__				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


