




'''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, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast




@require_vision
class      UpperCamelCase       (	unittest.TestCase       ):



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      tempfile.mkdtemp()

      _a				:							Any							       =      CLIPImageProcessor()
      _a				:							Any							       =      BertTokenizer.from_pretrained(
          """hf-internal-testing/tiny-random-BertModel"""		,     model_input_names=["""input_ids""", """attention_mask"""] )

      _a				:							Optional[int]							       =      GitProcessor(__lowerCAmelCase		,     __lowerCAmelCase )

      processor.save_pretrained(self.tmpdirname )



  def     a_							(			self		,     **__lowerCAmelCase ):







      '''simple docstring'''


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



  def     a_							(			self		,     **__lowerCAmelCase ):







      '''simple docstring'''


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



  def     a_							(			self ):







      '''simple docstring'''


      shutil.rmtree(self.tmpdirname )



  def     a_							(			self ):







      '''simple docstring'''


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

      _a				:							Union[str, Any]							       =      [Image.fromarray(np.moveaxis(__lowerCAmelCase		,     0		,     -1 ) ) for x in image_inputs]

      return image_inputs



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      GitProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor() )
      processor.save_pretrained(self.tmpdirname )

      _a				:							Optional[int]							       =      self.get_tokenizer(bos_token="""(BOS)"""		,     eos_token="""(EOS)""" )
      _a				:							Dict							       =      self.get_image_processor(do_normalize=__lowerCAmelCase		,     padding_value=1.0 )

      _a				:							Dict							       =      GitProcessor.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 )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      self.get_image_processor()
      _a				:							Tuple							       =      self.get_tokenizer()

      _a				:							int							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

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

      _a				:							List[str]							       =      image_processor(__lowerCAmelCase		,     return_tensors="""np""" )
      _a				:							Any							       =      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     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.get_image_processor()
      _a				:							str							       =      self.get_tokenizer()

      _a				:							List[Any]							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[Any]							       =      """lower newer"""

      _a				:							str							       =      processor(text=__lowerCAmelCase )

      _a				:							List[str]							       =      tokenizer(__lowerCAmelCase		,     return_token_type_ids=__lowerCAmelCase )

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



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_image_processor()
      _a				:							Union[str, Any]							       =      self.get_tokenizer()

      _a				:							List[Any]							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[int]							       =      """lower newer"""
      _a				:							List[Any]							       =      self.prepare_image_inputs()

      _a				:							Any							       =      processor(text=__lowerCAmelCase		,     images=__lowerCAmelCase )

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

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



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      self.get_image_processor()
      _a				:							Optional[Any]							       =      self.get_tokenizer()

      _a				:							Dict							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

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

      _a				:							Union[str, Any]							       =      processor.batch_decode(__lowerCAmelCase )
      _a				:							Optional[Any]							       =      tokenizer.batch_decode(__lowerCAmelCase )

      self.assertListEqual(__lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_image_processor()
      _a				:							Union[str, Any]							       =      self.get_tokenizer()

      _a				:							str							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Any							       =      """lower newer"""
      _a				:							int							       =      self.prepare_image_inputs()

      _a				:							int							       =      processor(text=__lowerCAmelCase		,     images=__lowerCAmelCase )

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






from __future__ import annotations







def 	A (		A__		,		A__		,		A__		,		A__			):

     # Base Case
     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(A__		,		A__		,		A__		,		counter - 1			)
     else:
          UpperCAmelCase    :					List[str]			=      capacity - weights[counter - 1]
          UpperCAmelCase    :					Tuple			=      values[counter - 1] + knapsack(
              A__		,		A__		,		A__		,		counter - 1			)
          UpperCAmelCase    :					Optional[int]			=      knapsack(A__		,		A__		,		A__		,		counter - 1			)
          return max(A__		,		A__			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



