





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    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      Tuple	)			->	str:
     lowerCamelCase__    :      int					 =		tempfile.mkdtemp()

     lowerCamelCase__    :      Tuple					 =		BlipImageProcessor()
     lowerCamelCase__    :      str					 =		GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'	)

     lowerCamelCase__    :      List[Any]					 =		BlipaProcessor(UpperCAmelCase			,     UpperCAmelCase	)

     processor.save_pretrained(self.tmpdirname	)
 def      A_	(      self  :      List[str]			,     **UpperCAmelCase  :      str	)			->	str:
     return AutoProcessor.from_pretrained(self.tmpdirname			,     **UpperCAmelCase	).tokenizer
 def      A_	(      self  :      Optional[int]			,     **UpperCAmelCase  :      List[str]	)			->	Tuple:
     return AutoProcessor.from_pretrained(self.tmpdirname			,     **UpperCAmelCase	).image_processor
 def      A_	(      self  :      Optional[Any]	)			->	Optional[Any]:
     shutil.rmtree(self.tmpdirname	)
 def      A_	(      self  :      Tuple	)			->	Any:
     lowerCamelCase__    :      Optional[Any]					 =		[np.random.randint(255			,     size=(3, 30, 400)			,     dtype=np.uinta	)]

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

     return image_inputs
 def      A_	(      self  :      Dict	)			->	List[str]:
     lowerCamelCase__    :      Optional[Any]					 =		BlipaProcessor(tokenizer=self.get_tokenizer()			,     image_processor=self.get_image_processor()	)
     processor.save_pretrained(self.tmpdirname	)

     lowerCamelCase__    :      Optional[Any]					 =		self.get_tokenizer(bos_token='(BOS)'			,     eos_token='(EOS)'	)
     lowerCamelCase__    :      Union[str, Any]					 =		self.get_image_processor(do_normalize=UpperCAmelCase			,     padding_value=1.0	)

     lowerCamelCase__    :      str					 =		BlipaProcessor.from_pretrained(
         self.tmpdirname			,     bos_token='(BOS)'			,     eos_token='(EOS)'			,     do_normalize=UpperCAmelCase			,     padding_value=1.0	)

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

     self.assertEqual(processor.image_processor.to_json_string()			,     image_processor_add_kwargs.to_json_string()	)
     self.assertIsInstance(processor.image_processor			,     UpperCAmelCase	)
 def      A_	(      self  :      Tuple	)			->	str:
     lowerCamelCase__    :      Dict					 =		self.get_image_processor()
     lowerCamelCase__    :      List[Any]					 =		self.get_tokenizer()

     lowerCamelCase__    :      Any					 =		BlipaProcessor(tokenizer=UpperCAmelCase			,     image_processor=UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		self.prepare_image_inputs()

     lowerCamelCase__    :      List[Any]					 =		image_processor(UpperCAmelCase			,     return_tensors='np'	)
     lowerCamelCase__    :      List[Any]					 =		processor(images=UpperCAmelCase			,     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  :      int	)			->	Any:
     lowerCamelCase__    :      Optional[Any]					 =		self.get_image_processor()
     lowerCamelCase__    :      List[str]					 =		self.get_tokenizer()

     lowerCamelCase__    :      int					 =		BlipaProcessor(tokenizer=UpperCAmelCase			,     image_processor=UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		'lower newer'

     lowerCamelCase__    :      Optional[int]					 =		processor(text=UpperCAmelCase	)

     lowerCamelCase__    :      Tuple					 =		tokenizer(UpperCAmelCase			,     return_token_type_ids=UpperCAmelCase	)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]			,     encoded_processor[key]	)
 def      A_	(      self  :      Tuple	)			->	List[Any]:
     lowerCamelCase__    :      Any					 =		self.get_image_processor()
     lowerCamelCase__    :      Dict					 =		self.get_tokenizer()

     lowerCamelCase__    :      Optional[Any]					 =		BlipaProcessor(tokenizer=UpperCAmelCase			,     image_processor=UpperCAmelCase	)

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

     lowerCamelCase__    :      Optional[Any]					 =		processor(text=UpperCAmelCase			,     images=UpperCAmelCase	)

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

     # test if it raises when no input is passed
     with pytest.raises(UpperCAmelCase	):
         processor()
 def      A_	(      self  :      Dict	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[int]					 =		self.get_image_processor()
     lowerCamelCase__    :      Optional[int]					 =		self.get_tokenizer()

     lowerCamelCase__    :      str					 =		BlipaProcessor(tokenizer=UpperCAmelCase			,     image_processor=UpperCAmelCase	)

     lowerCamelCase__    :      int					 =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__    :      str					 =		processor.batch_decode(UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		tokenizer.batch_decode(UpperCAmelCase	)

     self.assertListEqual(UpperCAmelCase			,     UpperCAmelCase	)







 def      A_	(      self  :      Tuple	)			->	str:
     lowerCamelCase__    :      Optional[int]					 =		self.get_image_processor()
     lowerCamelCase__    :      List[Any]					 =		self.get_tokenizer()

     lowerCamelCase__    :      List[str]					 =		BlipaProcessor(tokenizer=UpperCAmelCase			,     image_processor=UpperCAmelCase	)

     lowerCamelCase__    :      Any					 =		'lower newer'
     lowerCamelCase__    :      Optional[Any]					 =		self.prepare_image_inputs()

     lowerCamelCase__    :      Union[str, Any]					 =		processor(text=UpperCAmelCase			,     images=UpperCAmelCase	)

     # 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"""





def       lowerCamelCase__		(			__snake_case	)							->		bool:


					"""simple docstring"""





					return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''')	)



def       lowerCamelCase__		(			__snake_case	)							->		bool:


					"""simple docstring"""





					_UpperCamelCase							 =  credit_card_number
					_UpperCamelCase							 =  0
					_UpperCamelCase							 =  len(__snake_case	) - 2
					for i in range(__snake_case,     -1,     -2	):
										#  double the value of every second digit
										_UpperCamelCase							 =  int(cc_number[i]	)
										digit *= 2
										# If doubling of a number results in a two digit number
										# i.e greater than 9(e.g., 6 × 2 = 12),
										# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
										# to get a single digit number.
										if digit > 9:
															digit %= 10
															digit += 1
										_UpperCamelCase							 =  cc_number[:i] + str(__snake_case	) + cc_number[i + 1 :]
										total += digit

					# Sum up the remaining digits
					for i in range(len(__snake_case	) - 1,     -1,     -2	):
										total += int(cc_number[i]	)

					return total % 10 == 0



def       lowerCamelCase__		(			__snake_case	)							->		bool:


					"""simple docstring"""





					_UpperCamelCase							 =  F'''{credit_card_number} is an invalid credit card number because'''
					if not credit_card_number.isdigit():
										print(F'''{error_message} it has nonnumerical characters.'''	)
										return False

					if not 13 <= len(__snake_case	) <= 16:
										print(F'''{error_message} of its length.'''	)
										return False

					if not validate_initial_digits(__snake_case	):
										print(F'''{error_message} of its first two digits.'''	)
										return False

					if not luhn_validation(__snake_case	):
										print(F'''{error_message} it fails the Luhn check.'''	)
										return False

					print(F'''{credit_card_number} is a valid credit card number.'''	)
					return True


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				validate_credit_card_number("""4111111111111111""")
				validate_credit_card_number("""32323""")






