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, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast






@require_vision
class 		a__						(					unittest.TestCase					):

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tempfile.mkdtemp()

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    BlipImageProcessor()
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    BlipProcessor(_A,_A    )

							processor.save_pretrained(self.tmpdirname    )

					def 				__UpperCamelCase   (    self      :       Dict,**_A      :       str    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Any,**_A      :       int    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							shutil.rmtree(self.tmpdirname    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [np.random.randint(255,size=(3, 30, 400),dtype=np.uinta    )]

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [Image.fromarray(np.moveaxis(_A,0,-1    )    ) for x in image_inputs]

							return image_inputs

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    BlipProcessor(tokenizer=self.get_tokenizer(),image_processor=self.get_image_processor()    )
							processor.save_pretrained(self.tmpdirname    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_tokenizer(bos_token="(BOS)",eos_token="(EOS)"    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.get_image_processor(do_normalize=_A,padding_value=1.0    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    BlipProcessor.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      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    image_processor(_A,return_tensors="np"    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		str				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    "lower newer"

							SCREAMING_SNAKE_CASE_  :		int				      =    processor(text=_A    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tokenizer(_A,return_token_type_ids=_A    )

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

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		str				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		str				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    "lower newer"
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_  :		Any				      =    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      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		str				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		str				      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    processor.batch_decode(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    tokenizer.batch_decode(_A    )

							self.assertListEqual(_A,_A    )






					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_image_processor()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_tokenizer()

							SCREAMING_SNAKE_CASE_  :		int				      =    BlipProcessor(tokenizer=_A,image_processor=_A    )

							SCREAMING_SNAKE_CASE_  :		str				      =    "lower newer"
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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"]    )

def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		while b:
				SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    b, a % b
		return a




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		return a if b == 0 else euclidean_gcd_recursive(lowerCAmelCase   , a % b	)




def       _snake_case	(   ):
		"""simple docstring"""







		print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3   , 5	)}'	)
		print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5   , 3	)}'	)
		print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1   , 3	)}'	)
		print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3   , 6	)}'	)
		print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6   , 3	)}'	)

		print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3   , 5	)}'	)
		print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5   , 3	)}'	)
		print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1   , 3	)}'	)
		print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3   , 6	)}'	)
		print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6   , 3	)}'	)


if __name__ == "__main__":
		main()
