





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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       tempfile.mkdtemp()

         __snake_case								=       BlipImageProcessor()
         __snake_case								=       GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''				)

         __snake_case								=       BlipaProcessor(a__    ,      a__				)

         processor.save_pretrained(self.tmpdirname				)


      def        a			(self	:				List[Any]    ,      **a__	:				List[str]				):





         """simple docstring"""



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


      def        a			(self	:				Optional[int]    ,      **a__	:				List[Any]				):





         """simple docstring"""



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


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         shutil.rmtree(self.tmpdirname				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



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

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

         return image_inputs


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       BlipaProcessor(tokenizer=self.get_tokenizer()    ,      image_processor=self.get_image_processor()				)
         processor.save_pretrained(self.tmpdirname				)

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

         __snake_case								=       BlipaProcessor.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        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

         __snake_case								=       BlipaProcessor(tokenizer=a__    ,      image_processor=a__				)

         __snake_case								=       self.prepare_image_inputs()

         __snake_case								=       image_processor(a__    ,      return_tensors='''np'''				)
         __snake_case								=       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        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

         __snake_case								=       BlipaProcessor(tokenizer=a__    ,      image_processor=a__				)

         __snake_case								=       '''lower newer'''

         __snake_case								=       processor(text=a__				)

         __snake_case								=       tokenizer(a__    ,      return_token_type_ids=a__				)

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


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

         __snake_case								=       BlipaProcessor(tokenizer=a__    ,      image_processor=a__				)

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

         __snake_case								=       processor(text=a__    ,      images=a__				)

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

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


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

         __snake_case								=       BlipaProcessor(tokenizer=a__    ,      image_processor=a__				)

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

         __snake_case								=       processor.batch_decode(a__				)
         __snake_case								=       tokenizer.batch_decode(a__				)

         self.assertListEqual(a__    ,      a__				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

         __snake_case								=       BlipaProcessor(tokenizer=a__    ,      image_processor=a__				)

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

         __snake_case								=       processor(text=a__    ,      images=a__				)

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








from pathlib import Path

import fire






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  str	,				snake_case_     :  int	)				->    str:
   __snake_case								=       Path(snake_case_	)
   __snake_case								=       Path(snake_case_	)
   dest_dir.mkdir(exist_ok=snake_case_	)
   for path in src_dir.iterdir():
      __snake_case								=       [x.rstrip() for x in list(path.open().readlines()	)][:n]
      __snake_case								=       dest_dir.joinpath(path.name	)
      print(snake_case_	)
      dest_path.open('''w'''	).write('''\n'''.join(snake_case_	)	)


if __name__ == "__main__":
    fire.Fire(minify)

