





"""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"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_			=      {
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
a_			=      """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
a_			=      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""







def 		__lowercase					(       snake_case_  :  str						)       ->dict[str, int]:



    '''simple docstring'''







    __A   :						Any									=					{letter: 0 for letter in string.ascii_uppercase}
    for letter in message.upper():
        if letter in LETTERS:
            letter_count[letter] += 1

    return letter_count







def 		__lowercase					(       snake_case_  :  tuple						)       ->str:



    '''simple docstring'''







    return x[0]







def 		__lowercase					(       snake_case_  :  str						)       ->str:



    '''simple docstring'''







    __A   :						Union[str, Any]									=					get_letter_count(snake_case_						)
    __A   :						dict[int, list[str]]									=					{
        freq: [] for letter, freq in letter_to_freq.items()
    }
    for letter in LETTERS:
        freq_to_letter[letter_to_freq[letter]].append(snake_case_						)

    __A   :						dict[int, str]									=					{}

    for freq in freq_to_letter:
        freq_to_letter[freq].sort(key=ETAOIN.find    ,reverse=snake_case_						)
        __A   :						Optional[int]									=					''''''.join(freq_to_letter[freq]						)

    __A   :						str									=					list(freq_to_letter_str.items()						)
    freq_pairs.sort(key=snake_case_    ,reverse=snake_case_						)

    __A   :						list[str]									=					[freq_pair[1] for freq_pair in freq_pairs]

    return "".join(snake_case_						)







def 		__lowercase					(       snake_case_  :  str						)       ->int:



    '''simple docstring'''







    __A   :						Any									=					get_frequency_order(snake_case_						)
    __A   :						str									=					0
    for common_letter in ETAOIN[:6]:
        if common_letter in freq_order[:6]:
            match_score += 1

    for uncommon_letter in ETAOIN[-6:]:
        if uncommon_letter in freq_order[-6:]:
            match_score += 1

    return match_score


if __name__ == "__main__":
       import doctest

       doctest.testmod()




