




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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						tempfile.mkdtemp()

             __lowerCamelCase   =						BlipImageProcessor()
             __lowerCamelCase   =						GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""   )

             __lowerCamelCase   =						BlipaProcessor(UpperCamelCase_    ,       UpperCamelCase_   )

             processor.save_pretrained(self.tmpdirname   )
      def 			lowerCAmelCase__						(				self: Dict    ,       **UpperCamelCase_: Tuple   ):
             return AutoProcessor.from_pretrained(self.tmpdirname    ,       **UpperCamelCase_   ).tokenizer
      def 			lowerCAmelCase__						(				self: List[Any]    ,       **UpperCamelCase_: Optional[int]   ):
             return AutoProcessor.from_pretrained(self.tmpdirname    ,       **UpperCamelCase_   ).image_processor
      def 			lowerCAmelCase__						(				self: Any   ):
             shutil.rmtree(self.tmpdirname   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						[np.random.randint(2_55    ,       size=(3, 30, 4_00)    ,       dtype=np.uinta   )]

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

             return image_inputs
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						BlipaProcessor(tokenizer=self.get_tokenizer()    ,       image_processor=self.get_image_processor()   )
             processor.save_pretrained(self.tmpdirname   )

             __lowerCamelCase   =						self.get_tokenizer(bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""   )
             __lowerCamelCase   =						self.get_image_processor(do_normalize=UpperCamelCase_    ,       padding_value=1.0   )

             __lowerCamelCase   =						BlipaProcessor.from_pretrained(
                 self.tmpdirname    ,       bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    ,       do_normalize=UpperCamelCase_    ,       padding_value=1.0   )

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

             self.assertEqual(processor.image_processor.to_json_string()    ,       image_processor_add_kwargs.to_json_string()   )
             self.assertIsInstance(processor.image_processor    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						self.prepare_image_inputs()

             __lowerCamelCase   =						image_processor(UpperCamelCase_    ,       return_tensors="""np"""   )
             __lowerCamelCase   =						processor(images=UpperCamelCase_    ,       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 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						"""lower newer"""

             __lowerCamelCase   =						processor(text=UpperCamelCase_   )

             __lowerCamelCase   =						tokenizer(UpperCamelCase_    ,       return_token_type_ids=UpperCamelCase_   )

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]    ,       encoded_processor[key]   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						"""lower newer"""
             __lowerCamelCase   =						self.prepare_image_inputs()

             __lowerCamelCase   =						processor(text=UpperCamelCase_    ,       images=UpperCamelCase_   )

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

             # test if it raises when no input is passed
             with pytest.raises(UpperCamelCase_   ):
                    processor()
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

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

             __lowerCamelCase   =						processor.batch_decode(UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.batch_decode(UpperCamelCase_   )

             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						"""lower newer"""
             __lowerCamelCase   =						self.prepare_image_inputs()

             __lowerCamelCase   =						processor(text=UpperCamelCase_    ,       images=UpperCamelCase_   )

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




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
          raise ValueError('String lengths must match!'			)

     lowercase__    :   Union[str, Any]      =       0

     for chara, chara in zip(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

