"""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    ):



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     tempfile.mkdtemp()

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

        a__:					List[str]      							=     InstructBlipProcessor(lowercase   ,      lowercase   ,      lowercase)

        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      Dict:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      Dict:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     [np.random.randint(2_55   ,      size=(3, 30, 4_00)   ,      dtype=np.uinta)]

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

        return image_inputs



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					str      							=     InstructBlipProcessor(
            tokenizer=self.get_tokenizer()   ,      image_processor=self.get_image_processor()   ,      qformer_tokenizer=self.get_qformer_tokenizer()   ,      )
        processor.save_pretrained(self.tmpdirname)

        a__:					Optional[Any]      							=     self.get_tokenizer(bos_token='(BOS)'   ,      eos_token='(EOS)')
        a__:					List[Any]      							=     self.get_image_processor(do_normalize=lowercase   ,      padding_value=1.0)

        a__:					Dict      							=     InstructBlipProcessor.from_pretrained(
            self.tmpdirname   ,      bos_token='(BOS)'   ,      eos_token='(EOS)'   ,      do_normalize=lowercase   ,      padding_value=1.0)

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

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



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





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

        a__:					List[Any]      							=     InstructBlipProcessor(
            tokenizer=lowercase   ,      image_processor=lowercase   ,      qformer_tokenizer=lowercase)

        a__:					Optional[Any]      							=     self.prepare_image_inputs()

        a__:					Optional[int]      							=     image_processor(lowercase   ,      return_tensors='np')
        a__:					List[str]      							=     processor(images=lowercase   ,      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)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[int]      							=     self.get_image_processor()
        a__:					Dict      							=     self.get_tokenizer()
        a__:					Optional[int]      							=     self.get_qformer_tokenizer()

        a__:					str      							=     InstructBlipProcessor(
            tokenizer=lowercase   ,      image_processor=lowercase   ,      qformer_tokenizer=lowercase)

        a__:					Optional[int]      							=     'lower newer'

        a__:					int      							=     processor(text=lowercase)

        a__:					Union[str, Any]      							=     tokenizer(lowercase   ,      return_token_type_ids=lowercase)
        a__:					Any      							=     qformer_tokenizer(lowercase   ,      return_token_type_ids=lowercase)

        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   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					str      							=     self.get_image_processor()
        a__:					Tuple      							=     self.get_tokenizer()
        a__:					str      							=     self.get_qformer_tokenizer()

        a__:					Optional[Any]      							=     InstructBlipProcessor(
            tokenizer=lowercase   ,      image_processor=lowercase   ,      qformer_tokenizer=lowercase)

        a__:					str      							=     'lower newer'
        a__:					Optional[Any]      							=     self.prepare_image_inputs()

        a__:					Dict      							=     processor(text=lowercase   ,      images=lowercase)

        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(lowercase):
          processor()



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





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

        a__:					List[Any]      							=     InstructBlipProcessor(
            tokenizer=lowercase   ,      image_processor=lowercase   ,      qformer_tokenizer=lowercase)

        a__:					Optional[Any]      							=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__:					Dict      							=     processor.batch_decode(lowercase)
        a__:					List[str]      							=     tokenizer.batch_decode(lowercase)

        self.assertListEqual(lowercase   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





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

        a__:					str      							=     InstructBlipProcessor(
            tokenizer=lowercase   ,      image_processor=lowercase   ,      qformer_tokenizer=lowercase)

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

        a__:					Optional[Any]      							=     processor(text=lowercase   ,      images=lowercase)

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



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Tuple:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  if n == 0:
    return 0
  a__:					List[Any]      							=     float('-inf'       )
  for i in range(1		,					n + 1       ):
    a__:					Optional[Any]      							=     max(
        _SCREAMING_SNAKE_CASE		,					prices[i - 1] + naive_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE       )       )

  return max_revue







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->str:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  return _top_down_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  if max_rev[n] >= 0:
    return max_rev[n]
  elif n == 0:
    return 0
  else:
    a__:					Dict      							=     float('-inf'       )
    for i in range(1		,					n + 1       ):
      a__:					Optional[Any]      							=     max(
          _SCREAMING_SNAKE_CASE		,					prices[i - 1] + _top_down_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					)

    a__:					Optional[int]      							=     max_revenue

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
  # length 0.
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  a__:					Tuple      							=     0

  for i in range(1		,					n + 1       ):
    a__:					List[str]      							=     max_rev[i]
    for j in range(1		,					i + 1       ):
      a__:					Tuple      							=     max(_SCREAMING_SNAKE_CASE		,					prices[j - 1] + max_rev[i - j]       )

    a__:					Union[str, Any]      							=     max_revenue_i

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Any:
  if n < 0:
    a__:					Optional[int]      							=     F'n must be greater than or equal to 0. Got n = {n}'
    raise ValueError(_SCREAMING_SNAKE_CASE       )

  if n > len(_SCREAMING_SNAKE_CASE       ):
    a__:					List[str]      							=     (
        'Each integral piece of rod must have a corresponding price. '
        F'Got n = {n} but length of prices = {len(_SCREAMING_SNAKE_CASE       )}'
    )
    raise ValueError(_SCREAMING_SNAKE_CASE       )







def 						__a       (     )     ->str:
  a__:					int      							=     [6, 10, 12, 15, 20, 23]
  a__:					Union[str, Any]      							=     len(_SCREAMING_SNAKE_CASE       )

  # the best revenue comes from cutting the rod into 6 pieces, each
  # of length 1 resulting in a revenue of 6 * 6 = 36.
  a__:					Any      							=     36

  a__:					Optional[int]      							=     top_down_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					List[Any]      							=     bottom_up_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     naive_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  assert expected_max_revenue == max_rev_top_down
  assert max_rev_top_down == max_rev_bottom_up
  assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
 main()


