


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     ):

       def     UpperCAmelCase( self   :  str     ):
            a_     :  str				      =     tempfile.mkdtemp()

            a_     :  str				      =     CLIPImageProcessor()
            a_     :  str				      =     BertTokenizer.from_pretrained(
                """hf-internal-testing/tiny-random-BertModel"""   ,				model_input_names=["""input_ids""", """attention_mask"""]     )

            a_     :  Optional[int]				      =     GitProcessor(A_   ,				A_     )

            processor.save_pretrained(self.tmpdirname     )

       def     UpperCAmelCase( self   :  Tuple   ,				**lowerCamelCase_   :  Dict     ):
            return AutoProcessor.from_pretrained(self.tmpdirname   ,				**A_     ).tokenizer

       def     UpperCAmelCase( self   :  str   ,				**lowerCamelCase_   :  Union[str, Any]     ):
            return AutoProcessor.from_pretrained(self.tmpdirname   ,				**A_     ).image_processor

       def     UpperCAmelCase( self   :  List[Any]     ):
            shutil.rmtree(self.tmpdirname     )

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  str				      =     [np.random.randint(2_5_5   ,				size=(3, 3_0, 4_0_0)   ,				dtype=np.uinta     )]

            a_     :  Tuple				      =     [Image.fromarray(np.moveaxis(A_   ,				0   ,				-1     )     ) for x in image_inputs]

            return image_inputs

       def     UpperCAmelCase( self   :  str     ):
            a_     :  Tuple				      =     GitProcessor(tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()     )
            processor.save_pretrained(self.tmpdirname     )

            a_     :  int				      =     self.get_tokenizer(bos_token="""(BOS)"""   ,				eos_token="""(EOS)"""     )
            a_     :  int				      =     self.get_image_processor(do_normalize=A_   ,				padding_value=1.0     )

            a_     :  int				      =     GitProcessor.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     UpperCAmelCase( self   :  Dict     ):
            a_     :  str				      =     self.get_image_processor()
            a_     :  Dict				      =     self.get_tokenizer()

            a_     :  Optional[int]				      =     GitProcessor(tokenizer=A_   ,				image_processor=A_     )

            a_     :  List[str]				      =     self.prepare_image_inputs()

            a_     :  Optional[Any]				      =     image_processor(A_   ,				return_tensors="""np"""     )
            a_     :  Optional[int]				      =     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     UpperCAmelCase( self   :  str     ):
            a_     :  int				      =     self.get_image_processor()
            a_     :  Optional[Any]				      =     self.get_tokenizer()

            a_     :  Optional[Any]				      =     GitProcessor(tokenizer=A_   ,				image_processor=A_     )

            a_     :  Dict				      =     """lower newer"""

            a_     :  Dict				      =     processor(text=A_     )

            a_     :  Optional[Any]				      =     tokenizer(A_   ,				return_token_type_ids=A_     )

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

       def     UpperCAmelCase( self   :  List[str]     ):
            a_     :  Optional[int]				      =     self.get_image_processor()
            a_     :  Any				      =     self.get_tokenizer()

            a_     :  Any				      =     GitProcessor(tokenizer=A_   ,				image_processor=A_     )

            a_     :  List[Any]				      =     """lower newer"""
            a_     :  int				      =     self.prepare_image_inputs()

            a_     :  int				      =     processor(text=A_   ,				images=A_     )

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

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

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            a_     :  int				      =     self.get_image_processor()
            a_     :  Optional[Any]				      =     self.get_tokenizer()

            a_     :  List[str]				      =     GitProcessor(tokenizer=A_   ,				image_processor=A_     )

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

            a_     :  Optional[Any]				      =     processor.batch_decode(A_     )
            a_     :  int				      =     tokenizer.batch_decode(A_     )

            self.assertListEqual(A_   ,				A_     )

       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Any				      =     self.get_image_processor()
            a_     :  List[str]				      =     self.get_tokenizer()

            a_     :  List[Any]				      =     GitProcessor(tokenizer=A_   ,				image_processor=A_     )

            a_     :  Dict				      =     """lower newer"""
            a_     :  Optional[int]				      =     self.prepare_image_inputs()

            a_     :  str				      =     processor(text=A_   ,				images=A_     )

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





from __future__ import annotations


def  A			(   lowercase       ,			lowercase					)  ->    int:
		'''simple docstring'''

		UpperCamelCase      ,    UpperCamelCase            =	0, len(lowercase					) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
					print(double_linear_search(list(range(100)), 40))
