





"""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,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )






@require_vision
class 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					tempfile.mkdtemp()

     __A   :						Any									=					BlipImageProcessor()
     __A   :						Optional[int]									=					GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''  )
     __A   :						Dict									=					BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''  )

     __A   :						str									=					InstructBlipProcessor(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  )

     processor.save_pretrained(self.tmpdirname  )

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return AutoProcessor.from_pretrained(self.tmpdirname ,						**__lowerCamelCase  ).qformer_tokenizer

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     shutil.rmtree(self.tmpdirname  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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

     __A   :						Dict									=					[Image.fromarray(np.moveaxis(__lowerCamelCase ,						0 ,						-1  )  ) for x in image_inputs]

     return image_inputs

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					InstructBlipProcessor(
         tokenizer=self.get_tokenizer() ,						image_processor=self.get_image_processor() ,						qformer_tokenizer=self.get_qformer_tokenizer() ,						)
     processor.save_pretrained(self.tmpdirname  )

     __A   :						Dict									=					self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)'''  )
     __A   :						Optional[int]									=					self.get_image_processor(do_normalize=__lowerCamelCase ,						padding_value=1.0  )

     __A   :						str									=					InstructBlipProcessor.from_pretrained(
         self.tmpdirname ,						bos_token='''(BOS)''' ,						eos_token='''(EOS)''' ,						do_normalize=__lowerCamelCase ,						padding_value=1.0  )

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

     self.assertEqual(processor.image_processor.to_json_string() ,						image_processor_add_kwargs.to_json_string()  )
     self.assertIsInstance(processor.image_processor ,						__lowerCamelCase  )
     self.assertIsInstance(processor.qformer_tokenizer ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.get_image_processor()
     __A   :						Any									=					self.get_tokenizer()
     __A   :						Any									=					self.get_qformer_tokenizer()

     __A   :						Optional[int]									=					InstructBlipProcessor(
         tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase ,						qformer_tokenizer=__lowerCamelCase  )

     __A   :						str									=					self.prepare_image_inputs()

     __A   :						Any									=					image_processor(__lowerCamelCase ,						return_tensors='''np'''  )
     __A   :						Dict									=					processor(images=__lowerCamelCase ,						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'''




     __A   :						Optional[Any]									=					self.get_image_processor()
     __A   :						List[Any]									=					self.get_tokenizer()
     __A   :						Union[str, Any]									=					self.get_qformer_tokenizer()

     __A   :						Dict									=					InstructBlipProcessor(
         tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase ,						qformer_tokenizer=__lowerCamelCase  )

     __A   :						Dict									=					'''lower newer'''

     __A   :						Dict									=					processor(text=__lowerCamelCase  )

     __A   :						int									=					tokenizer(__lowerCamelCase ,						return_token_type_ids=__lowerCamelCase  )
     __A   :						Any									=					qformer_tokenizer(__lowerCamelCase ,						return_token_type_ids=__lowerCamelCase  )

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

     for key in encoded_tokens_qformer.keys():
         self.assertListEqual(encoded_tokens_qformer[key] ,						encoded_processor['''qformer_''' + key]  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					self.get_image_processor()
     __A   :						Optional[int]									=					self.get_tokenizer()
     __A   :						List[str]									=					self.get_qformer_tokenizer()

     __A   :						Union[str, Any]									=					InstructBlipProcessor(
         tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase ,						qformer_tokenizer=__lowerCamelCase  )

     __A   :						List[str]									=					'''lower newer'''
     __A   :						List[Any]									=					self.prepare_image_inputs()

     __A   :						Dict									=					processor(text=__lowerCamelCase ,						images=__lowerCamelCase  )

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

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					self.get_image_processor()
     __A   :						str									=					self.get_tokenizer()
     __A   :						str									=					self.get_qformer_tokenizer()

     __A   :						str									=					InstructBlipProcessor(
         tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase ,						qformer_tokenizer=__lowerCamelCase  )

     __A   :						Tuple									=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     __A   :						Optional[Any]									=					processor.batch_decode(__lowerCamelCase  )
     __A   :						int									=					tokenizer.batch_decode(__lowerCamelCase  )

     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					self.get_image_processor()
     __A   :						List[Any]									=					self.get_tokenizer()
     __A   :						List[Any]									=					self.get_qformer_tokenizer()

     __A   :						Any									=					InstructBlipProcessor(
         tokenizer=__lowerCamelCase ,						image_processor=__lowerCamelCase ,						qformer_tokenizer=__lowerCamelCase  )

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

     __A   :						Any									=					processor(text=__lowerCamelCase ,						images=__lowerCamelCase  )

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











"""simple docstring"""



def 		__lowercase					(       snake_case_  :  int						)       ->Optional[Any]:



    '''simple docstring'''







    stooge(snake_case_    ,0    ,len(snake_case_						) - 1						)
    return arr







def 		__lowercase					(       snake_case_  :  Optional[Any]    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  Any						)       ->Tuple:



    '''simple docstring'''







    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A			,	__A   :						Optional[int]									=					arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A   :						Any									=					(int)((h - i + 1) / 3						)

        # Recursively sort first 2/3 elements
        stooge(snake_case_    ,snake_case_    ,(h - t)						)

        # Recursively sort last 2/3 elements
        stooge(snake_case_    ,i + t    ,(snake_case_)						)

        # Recursively sort first 2/3 elements
        stooge(snake_case_    ,snake_case_    ,(h - t)						)


if __name__ == "__main__":
       a_			=      input("""Enter numbers separated by a comma:\n""").strip()
       a_			=      [int(item) for item in user_input.split(""",""")]
       print(stooge_sort(unsorted))




