






def       _a   (							lowerCamelCase__				)  ->      Dict:
 if len(lowerCamelCase__				) > 1:
  lowerCamelCase_							:    Any							=     len(lowerCamelCase__				) // 2  # Finds the middle of the array
  lowerCamelCase_							:    Optional[Any]							=     arr[
      :middle_length
  ]  # Creates an array of the elements in the first half.
  lowerCamelCase_							:    int							=     arr[
      middle_length:
  ]  # Creates an array of the elements in the second half.
  lowerCamelCase_							:    Tuple							=     len(lowerCamelCase__				)
  lowerCamelCase_							:    Any							=     len(lowerCamelCase__				)
  merge(lowerCamelCase__				)  # Starts sorting the left.
  merge(lowerCamelCase__				)  # Starts sorting the right
  lowerCamelCase_							:    Optional[Any]							=     0  # Left Counter
  lowerCamelCase_							:    str							=     0  # Right Counter
  lowerCamelCase_							:    Union[str, Any]							=     0  # Position Counter
  while (
      left_index < left_size and right_index < right_size
  ):  # Runs until the lowers size of the left and right are sorted.
   if left_array[left_index] < right_array[right_index]:
    lowerCamelCase_							:    List[str]							=     left_array[left_index]
    left_index += 1
   else:
    lowerCamelCase_							:    Tuple							=     right_array[right_index]
    right_index += 1
   index += 1
  while (
      left_index < left_size
  ):  # Adds the left over elements in the left half of the array
   lowerCamelCase_							:    Tuple							=     left_array[left_index]
   left_index += 1
   index += 1
  while (
      right_index < right_size
  ):  # Adds the left over elements in the right half of the array
   lowerCamelCase_							:    List[str]							=     right_array[right_index]
   right_index += 1
   index += 1
 return arr


if __name__ == "__main__":
       import doctest

       doctest.testmod()







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   ):





            """simple docstring"""

            _UpperCamelCase														=    tempfile.mkdtemp()

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

            _UpperCamelCase														=    GitProcessor(A,							A   )

            processor.save_pretrained(self.tmpdirname   )

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,							**A   ).tokenizer

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,							**A   ).image_processor

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    [np.random.randint(255,							size=(3, 30, 400),							dtype=np.uinta   )]

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

            return image_inputs

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    GitProcessor(tokenizer=self.get_tokenizer(),							image_processor=self.get_image_processor()   )
            processor.save_pretrained(self.tmpdirname   )

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

            _UpperCamelCase														=    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   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    self.prepare_image_inputs()

            _UpperCamelCase														=    image_processor(A,							return_tensors='''np'''   )
            _UpperCamelCase														=    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   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

            _UpperCamelCase														=    '''lower newer'''

            _UpperCamelCase														=    processor(text=A   )

            _UpperCamelCase														=    tokenizer(A,							return_token_type_ids=A   )

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

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

            _UpperCamelCase														=    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   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

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

            _UpperCamelCase														=    processor.batch_decode(A   )
            _UpperCamelCase														=    tokenizer.batch_decode(A   )

            self.assertListEqual(A,							A   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_image_processor()
            _UpperCamelCase														=    self.get_tokenizer()

            _UpperCamelCase														=    GitProcessor(tokenizer=A,							image_processor=A   )

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

            _UpperCamelCase														=    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''']   )






