



'''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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 	__magic_name__						(    unittest.TestCase   ):







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      str        =							tempfile.mkdtemp()

									_a     :      Optional[Any]        =							BlipImageProcessor()
									_a     :      Union[str, Any]        =							BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'		)

									_a     :      Any        =							BlipProcessor(__lowerCAmelCase	,__lowerCAmelCase		)

									processor.save_pretrained(self.tmpdirname		)







							def 							__lowercase			(							self					:       Optional[Any]	,**_UpperCAmelCase					:       Dict		):
									return AutoProcessor.from_pretrained(self.tmpdirname	,**__lowerCAmelCase		).tokenizer







							def 							__lowercase			(							self					:       List[str]	,**_UpperCAmelCase					:       Optional[int]		):
									return AutoProcessor.from_pretrained(self.tmpdirname	,**__lowerCAmelCase		).image_processor







							def 							__lowercase			(							self					:       Union[str, Any]		):
									shutil.rmtree(self.tmpdirname		)







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      List[str]        =							[np.random.randint(255	,size=(3, 30, 400)	,dtype=np.uinta		)]

									_a     :      Optional[Any]        =							[Image.fromarray(np.moveaxis(__lowerCAmelCase	,0	,-1		)		) for x in image_inputs]

									return image_inputs







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      List[str]        =							BlipProcessor(tokenizer=self.get_tokenizer()	,image_processor=self.get_image_processor()		)
									processor.save_pretrained(self.tmpdirname		)

									_a     :      str        =							self.get_tokenizer(bos_token='(BOS)'	,eos_token='(EOS)'		)
									_a     :      Tuple        =							self.get_image_processor(do_normalize=__lowerCAmelCase	,padding_value=1.0		)

									_a     :      Dict        =							BlipProcessor.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 							__lowercase			(							self					:       int		):
									_a     :      str        =							self.get_image_processor()
									_a     :      Dict        =							self.get_tokenizer()

									_a     :      Any        =							BlipProcessor(tokenizer=__lowerCAmelCase	,image_processor=__lowerCAmelCase		)

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

									_a     :      Union[str, Any]        =							image_processor(__lowerCAmelCase	,return_tensors='np'		)
									_a     :      Union[str, Any]        =							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 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Tuple        =							self.get_image_processor()
									_a     :      List[str]        =							self.get_tokenizer()

									_a     :      Tuple        =							BlipProcessor(tokenizer=__lowerCAmelCase	,image_processor=__lowerCAmelCase		)

									_a     :      List[str]        =							'lower newer'

									_a     :      Optional[int]        =							processor(text=__lowerCAmelCase		)

									_a     :      List[Any]        =							tokenizer(__lowerCAmelCase	,return_token_type_ids=__lowerCAmelCase		)

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







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Optional[Any]        =							self.get_image_processor()
									_a     :      List[str]        =							self.get_tokenizer()

									_a     :      Optional[int]        =							BlipProcessor(tokenizer=__lowerCAmelCase	,image_processor=__lowerCAmelCase		)

									_a     :      Tuple        =							'lower newer'
									_a     :      Union[str, Any]        =							self.prepare_image_inputs()

									_a     :      Optional[int]        =							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 							__lowercase			(							self					:       Any		):
									_a     :      List[str]        =							self.get_image_processor()
									_a     :      str        =							self.get_tokenizer()

									_a     :      Any        =							BlipProcessor(tokenizer=__lowerCAmelCase	,image_processor=__lowerCAmelCase		)

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

									_a     :      str        =							processor.batch_decode(__lowerCAmelCase		)
									_a     :      List[Any]        =							tokenizer.batch_decode(__lowerCAmelCase		)

									self.assertListEqual(__lowerCAmelCase	,__lowerCAmelCase		)







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      List[Any]        =							self.get_image_processor()
									_a     :      Optional[int]        =							self.get_tokenizer()

									_a     :      Any        =							BlipProcessor(tokenizer=__lowerCAmelCase	,image_processor=__lowerCAmelCase		)

									_a     :      List[Any]        =							'lower newer'
									_a     :      List[Any]        =							self.prepare_image_inputs()

									_a     :      Union[str, Any]        =							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"""






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	['torch', 'torchsde']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """torchsde"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)



