



"""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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    Optional[Any]								=			tempfile.mkdtemp()

        lowerCAmelCase_       :    Union[str, Any]								=			BlipImageProcessor()
        lowerCAmelCase_       :    List[str]								=			GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'	)

        lowerCAmelCase_       :    Dict								=			BlipaProcessor(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

        processor.save_pretrained(self.tmpdirname	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      **SCREAMING_SNAKE_CASE_    :				List[Any]	):
        return AutoProcessor.from_pretrained(self.tmpdirname   ,      **SCREAMING_SNAKE_CASE_	).tokenizer



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      **SCREAMING_SNAKE_CASE_    :				Any	):
        return AutoProcessor.from_pretrained(self.tmpdirname   ,      **SCREAMING_SNAKE_CASE_	).image_processor



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        shutil.rmtree(self.tmpdirname	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    List[Any]								=			[np.random.randint(2_5_5   ,      size=(3, 3_0, 4_0_0)   ,      dtype=np.uinta	)]

        lowerCAmelCase_       :    str								=			[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_   ,      0   ,      -1	)	) for x in image_inputs]

        return image_inputs



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        lowerCAmelCase_       :    Tuple								=			BlipaProcessor(tokenizer=self.get_tokenizer()   ,      image_processor=self.get_image_processor()	)
        processor.save_pretrained(self.tmpdirname	)

        lowerCAmelCase_       :    int								=			self.get_tokenizer(bos_token='(BOS)'   ,      eos_token='(EOS)'	)
        lowerCAmelCase_       :    Optional[Any]								=			self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_   ,      padding_value=1.0	)

        lowerCAmelCase_       :    Dict								=			BlipaProcessor.from_pretrained(
            self.tmpdirname   ,      bos_token='(BOS)'   ,      eos_token='(EOS)'   ,      do_normalize=SCREAMING_SNAKE_CASE_   ,      padding_value=1.0	)

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

        self.assertEqual(processor.image_processor.to_json_string()   ,      image_processor_add_kwargs.to_json_string()	)
        self.assertIsInstance(processor.image_processor   ,      SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Any								=			self.get_image_processor()
        lowerCAmelCase_       :    List[Any]								=			self.get_tokenizer()

        lowerCAmelCase_       :    Optional[Any]								=			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,      image_processor=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			self.prepare_image_inputs()

        lowerCAmelCase_       :    int								=			image_processor(SCREAMING_SNAKE_CASE_   ,      return_tensors='np'	)
        lowerCAmelCase_       :    str								=			processor(images=SCREAMING_SNAKE_CASE_   ,      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 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    Any								=			self.get_image_processor()
        lowerCAmelCase_       :    Optional[int]								=			self.get_tokenizer()

        lowerCAmelCase_       :    Tuple								=			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,      image_processor=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Optional[Any]								=			'lower newer'

        lowerCAmelCase_       :    Any								=			processor(text=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Dict								=			tokenizer(SCREAMING_SNAKE_CASE_   ,      return_token_type_ids=SCREAMING_SNAKE_CASE_	)

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    Any								=			self.get_image_processor()
        lowerCAmelCase_       :    str								=			self.get_tokenizer()

        lowerCAmelCase_       :    Tuple								=			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,      image_processor=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    str								=			'lower newer'
        lowerCAmelCase_       :    int								=			self.prepare_image_inputs()

        lowerCAmelCase_       :    int								=			processor(text=SCREAMING_SNAKE_CASE_   ,      images=SCREAMING_SNAKE_CASE_	)

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

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Optional[int]								=			self.get_image_processor()
        lowerCAmelCase_       :    List[str]								=			self.get_tokenizer()

        lowerCAmelCase_       :    List[Any]								=			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,      image_processor=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Optional[int]								=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowerCAmelCase_       :    Tuple								=			processor.batch_decode(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[int]								=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	)

        self.assertListEqual(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Tuple								=			self.get_image_processor()
        lowerCAmelCase_       :    Tuple								=			self.get_tokenizer()

        lowerCAmelCase_       :    Tuple								=			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,      image_processor=SCREAMING_SNAKE_CASE_	)

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

        lowerCAmelCase_       :    Union[str, Any]								=			processor(text=SCREAMING_SNAKE_CASE_   ,      images=SCREAMING_SNAKE_CASE_	)

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










"""simple docstring"""







from __future__ import annotations

def     UpperCamelCase_   (   lowerCAmelCase__				:    int						)						->       list[int]:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			2
  lowerCAmelCase_       :    List[Any]								=			[]
  while i * i <= n:
    if n % i:
      i += 1
    else:
      n //= i
      factors.append(lowerCAmelCase__						)
  if n > 1:
    factors.append(lowerCAmelCase__						)
  return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()





