


import argparse
from collections import defaultdict






def   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						Dict    ,     __a				:						Union[str, Any]    ,     __a				:						Optional[int]    ,     __a				:						Optional[int]     ):
							a__      =f"""{file}_{class_name}_{test_name}"""
							done_test[_id] += 1

							with open(__a    ,     'r'     ) as f:
														a__      =f.readlines()

							a__      =f"""class {class_name}("""
							a__      =f"""{4 * ' '}def {test_name}("""
							a__      =f"""{8 * ' '}{correct_line.split()[0]}"""
							a__      =f"""{16 * ' '}{correct_line.split()[0]}"""
							a__      =False
							a__      =False
							a__      =False
							a__      =False
							a__      =0
							a__      =0

							a__      =[]
							for line in lines:
														if line.startswith(__a     ):
																					a__      =True
														elif in_class and line.startswith(__a     ):
																					a__      =True
														elif in_class and in_func and (line.startswith(__a     ) or line.startswith(__a     )):
																					a__      =len(line.split(correct_line.split()[0]     )[0]     )
																					count += 1

																					if count == done_test[_id]:
																												a__      =True

														if in_class and in_func and in_line:
																					if ")" not in line:
																												continue
																					else:
																												a__      =True

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(f"""{spaces * ' '}{correct_line}"""     )
																					a__      =a__      =a__      =a__      =False
														else:
																					new_lines.append(__a     )

							with open(__a    ,     'w'     ) as f:
														for line in new_lines:
																					f.write(__a     )






def   _lowercase(					__a				:						int    ,     __a				:						Union[str, Any]=None     ):
							if fail is not None:
														with open(__a    ,     'r'     ) as f:
																					a__      ={l.strip() for l in f.readlines()}
							else:
														a__      =None

							with open(__a    ,     'r'     ) as f:
														a__      =f.readlines()

							a__      =defaultdict(__a     )
							for line in correct_lines:
														a__    ,     a__    ,     a__    ,     a__      =line.split(';'     )
														if test_failures is None or "::".join([file, class_name, test_name]     ) in test_failures:
																					overwrite_file(__a    ,     __a    ,     __a    ,     __a    ,     __a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Tuple   				=       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	_lowerCAmelCase:						int   				=       parser.parse_args()

	main(args.correct_filename, args.fail_filename)





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 						__UpperCamelCase    (				self)  -> int:
											a__      =tempfile.mkdtemp()

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

											a__      =BlipProcessor(lowercase_       ,  lowercase_)

											processor.save_pretrained(self.tmpdirname)





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> str:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).tokenizer





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> List[str]:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).image_processor





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											shutil.rmtree(self.tmpdirname)





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =[np.random.randint(255       ,  size=(3, 30, 400)       ,  dtype=np.uinta)]

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

											return image_inputs





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =BlipProcessor(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=lowercase_       ,  padding_value=1.0)

											a__      =BlipProcessor.from_pretrained(
											    self.tmpdirname       ,  bos_token='(BOS)'       ,  eos_token='(EOS)'       ,  do_normalize=lowercase_       ,  padding_value=1.0)

											self.assertEqual(processor.tokenizer.get_vocab()       ,  tokenizer_add_kwargs.get_vocab())
											self.assertIsInstance(processor.tokenizer       ,  lowercase_)

											self.assertEqual(processor.image_processor.to_json_string()       ,  image_processor_add_kwargs.to_json_string())
											self.assertIsInstance(processor.image_processor       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      =self.prepare_image_inputs()

											a__      =image_processor(lowercase_       ,  return_tensors='np')
											a__      =processor(images=lowercase_       ,  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]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'

											a__      =processor(text=lowercase_)

											a__      =tokenizer(lowercase_       ,  return_token_type_ids=lowercase_)

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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

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

											a__      =processor(text=lowercase_       ,  images=lowercase_)

											self.assertListEqual(list(inputs.keys())       ,  ['pixel_values', 'input_ids', 'attention_mask'])

											# test if it raises when no input is passed
											with pytest.raises(lowercase_):
																		processor()





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

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

											a__      =processor.batch_decode(lowercase_)
											a__      =tokenizer.batch_decode(lowercase_)

											self.assertListEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

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

											a__      =processor(text=lowercase_       ,  images=lowercase_)

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys())       ,  ['pixel_values', 'input_ids', 'attention_mask'])

