
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 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												lowercase							=						tempfile.mkdtemp()

												lowercase							=						BlipImageProcessor()
												lowercase							=						BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel''')

												lowercase							=						BlipProcessor(A__   ,A__)

												processor.save_pretrained(self.tmpdirname)




							def  A__      (				self   ,**A__):
												return AutoProcessor.from_pretrained(self.tmpdirname   ,**A__).tokenizer




							def  A__      (				self   ,**A__):
												return AutoProcessor.from_pretrained(self.tmpdirname   ,**A__).image_processor




							def  A__      (				self):
												shutil.rmtree(self.tmpdirname)




							def  A__      (				self):
												lowercase							=						[np.random.randint(2_5_5   ,size=(3, 3_0, 4_0_0)   ,dtype=np.uinta)]

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

												return image_inputs




							def  A__      (				self):
												lowercase							=						BlipProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor())
												processor.save_pretrained(self.tmpdirname)

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

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

												lowercase							=						BlipProcessor(tokenizer=A__   ,image_processor=A__)

												lowercase							=						self.prepare_image_inputs()

												lowercase							=						image_processor(A__   ,return_tensors='''np''')
												lowercase							=						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  A__      (				self):
												lowercase							=						self.get_image_processor()
												lowercase							=						self.get_tokenizer()

												lowercase							=						BlipProcessor(tokenizer=A__   ,image_processor=A__)

												lowercase							=						'''lower newer'''

												lowercase							=						processor(text=A__)

												lowercase							=						tokenizer(A__   ,return_token_type_ids=A__)

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




							def  A__      (				self):
												lowercase							=						self.get_image_processor()
												lowercase							=						self.get_tokenizer()

												lowercase							=						BlipProcessor(tokenizer=A__   ,image_processor=A__)

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

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

												lowercase							=						BlipProcessor(tokenizer=A__   ,image_processor=A__)

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

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

												self.assertListEqual(A__   ,A__)





							def  A__      (				self):
												lowercase							=						self.get_image_processor()
												lowercase							=						self.get_tokenizer()

												lowercase							=						BlipProcessor(tokenizer=A__   ,image_processor=A__)

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

												lowercase							=						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        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




