


"""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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast






@require_vision
class  __A							(			unittest.TestCase     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=tempfile.mkdtemp()

     a											=CLIPImageProcessor()
     a											=BertTokenizer.from_pretrained(
         '''hf-internal-testing/tiny-random-BertModel'''					,       model_input_names=['''input_ids''', '''attention_mask''']		)

     a											=GitProcessor(lowercase_					,       lowercase_		)

     processor.save_pretrained(self.tmpdirname		)


 def  SCREAMING_SNAKE_CASE				(						self					,       **__A		)   ->	Tuple:
     return AutoProcessor.from_pretrained(self.tmpdirname					,       **lowercase_		).tokenizer


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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     shutil.rmtree(self.tmpdirname		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     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  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=GitProcessor(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											=GitProcessor.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  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

     a											=GitProcessor(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  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

     a											=GitProcessor(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  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

     a											=GitProcessor(tokenizer=lowercase_					,       image_processor=lowercase_		)

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

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

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

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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

     a											=GitProcessor(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  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

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


def      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					Optional[int]				):





  '''simple docstring'''







  sequence.append(_A				)

  a__    =0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(_A				):
    return None

  return index


if __name__ == "__main__":
       lowerCamelCase 	=   input('''Enter numbers separated by comma:\n''').strip()
       lowerCamelCase 	=   [int(item) for item in user_input.split(''',''')]

       lowerCamelCase 	=   input('''Enter a single number to be found in the list:\n''')
       lowerCamelCase 	=   int(target_input)
       lowerCamelCase 	=   sentinel_linear_search(sequence, target)
       if result is not None:
              print(f"""{target} found at positions: {result}""")
       else:
              print('''Not found''')
