




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 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							Any:
           _A							:     str			=						tempfile.mkdtemp()

           _A							:     str			=						BlipImageProcessor()
           _A							:     List[Any]			=						GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""				)

           _A							:     Union[str, Any]			=						BlipaProcessor(_a		,     _a				)

           processor.save_pretrained(self.tmpdirname				)

     def 					a__	(  self		,     **_a				)						->							List[Any]:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).tokenizer

     def 					a__	(  self		,     **_a				)						->							Optional[int]:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **_a				).image_processor

     def 					a__	(  self				)						->							Any:
           shutil.rmtree(self.tmpdirname				)

     def 					a__	(  self				)						->							Any:
           _A							:     List[str]			=						[np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta				)]

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

           return image_inputs

     def 					a__	(  self				)						->							List[Any]:
           _A							:     List[Any]			=						BlipaProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

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

           _A							:     List[Any]			=						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				)						->							str:
           _A							:     Any			=						self.get_image_processor()
           _A							:     Optional[Any]			=						self.get_tokenizer()

           _A							:     List[Any]			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

           _A							:     List[str]			=						self.prepare_image_inputs()

           _A							:     Optional[Any]			=						image_processor(_a		,     return_tensors="""np"""				)
           _A							:     Optional[int]			=						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				)						->							List[Any]:
           _A							:     Tuple			=						self.get_image_processor()
           _A							:     List[str]			=						self.get_tokenizer()

           _A							:     str			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

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

           _A							:     Tuple			=						processor(text=_a				)

           _A							:     Optional[int]			=						tokenizer(_a		,     return_token_type_ids=_a				)

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

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     str			=						self.get_image_processor()
           _A							:     List[str]			=						self.get_tokenizer()

           _A							:     Dict			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

           _A							:     int			=						"""lower newer"""
           _A							:     List[str]			=						self.prepare_image_inputs()

           _A							:     Any			=						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				)						->							Optional[Any]:
           _A							:     List[Any]			=						self.get_image_processor()
           _A							:     Optional[int]			=						self.get_tokenizer()

           _A							:     str			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

           _A							:     Union[str, Any]			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _A							:     Tuple			=						processor.batch_decode(_a				)
           _A							:     Optional[Any]			=						tokenizer.batch_decode(_a				)

           self.assertListEqual(_a		,     _a				)



     def 					a__	(  self				)						->							Any:
           _A							:     List[str]			=						self.get_image_processor()
           _A							:     Any			=						self.get_tokenizer()

           _A							:     Optional[Any]			=						BlipaProcessor(tokenizer=_a		,     image_processor=_a				)

           _A							:     Tuple			=						"""lower newer"""
           _A							:     str			=						self.prepare_image_inputs()

           _A							:     Any			=						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"""]				)







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      if n == 0:
            return 0
      _A							:     Tuple			=						float("""-inf"""     )
      for i in range(1,n + 1     ):
            _A							:     str			=						max(
                snake_case_,prices[i - 1] + naive_cut_rod_recursive(n - i,snake_case_     )     )

      return max_revue







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      _A							:     Dict			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      return _top_down_cut_rod_recursive(snake_case_,snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            _A							:     List[str]			=						float("""-inf"""     )
            for i in range(1,n + 1     ):
                  _A							:     Optional[Any]			=						max(
                      snake_case_,prices[i - 1] + _top_down_cut_rod_recursive(n - i,snake_case_,snake_case_     ),)

            _A							:     Tuple			=						max_revenue

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      _A							:     List[Any]			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      _A							:     Any			=						0

      for i in range(1,n + 1     ):
            _A							:     Optional[Any]			=						max_rev[i]
            for j in range(1,i + 1     ):
                  _A							:     int			=						max(snake_case_,prices[j - 1] + max_rev[i - j]     )

            _A							:     int			=						max_revenue_i

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      if n < 0:
            _A							:     Optional[Any]			=						f'''n must be greater than or equal to 0. Got n = {n}'''
            raise ValueError(snake_case_     )

      if n > len(snake_case_     ):
            _A							:     Any			=						(
                """Each integral piece of rod must have a corresponding price. """
                f'''Got n = {n} but length of prices = {len(snake_case_     )}'''
            )
            raise ValueError(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						[6, 10, 12, 15, 20, 23]
      _A							:     List[Any]			=						len(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							:     List[Any]			=						top_down_cut_rod(snake_case_,snake_case_     )
      _A							:     List[Any]			=						bottom_up_cut_rod(snake_case_,snake_case_     )
      _A							:     Dict			=						naive_cut_rod_recursive(snake_case_,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()

