





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  A__    (							unittest.TestCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	str =				tempfile.mkdtemp()

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

          a__						:	int =				BlipaProcessor(lowercase					,  lowercase)

          processor.save_pretrained(self.tmpdirname)

   def 							__lowercase		(					self					,  **lowercase)			->						int:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  **lowercase)			->						Union[str, Any]:







          '''simple docstring'''

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

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          shutil.rmtree(self.tmpdirname)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	List[str] =				[np.random.randint(255					,  size=(3, 30, 400)					,  dtype=np.uinta)]

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

          return image_inputs

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

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

          a__						:	int =				self.get_tokenizer(bos_token='(BOS)'					,  eos_token='(EOS)')
          a__						:	Tuple =				self.get_image_processor(do_normalize=lowercase					,  padding_value=1.0)

          a__						:	str =				BlipaProcessor.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)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

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

          a__						:	List[str] =				BlipaProcessor(tokenizer=lowercase					,  image_processor=lowercase)

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

          a__						:	Tuple =				image_processor(lowercase					,  return_tensors='np')
          a__						:	Optional[int] =				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 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

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

          a__						:	Tuple =				BlipaProcessor(tokenizer=lowercase					,  image_processor=lowercase)

          a__						:	Union[str, Any] =				'lower newer'

          a__						:	str =				processor(text=lowercase)

          a__						:	int =				tokenizer(lowercase					,  return_token_type_ids=lowercase)

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

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

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

          a__						:	Optional[int] =				BlipaProcessor(tokenizer=lowercase					,  image_processor=lowercase)

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

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

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

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

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

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

          a__						:	Tuple =				BlipaProcessor(tokenizer=lowercase					,  image_processor=lowercase)

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

          a__						:	Dict =				processor.batch_decode(lowercase)
          a__						:	Any =				tokenizer.batch_decode(lowercase)

          self.assertListEqual(lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

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

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

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

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

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










from __future__ import annotations

UpperCAmelCase__				   =		list[list[int]]

# assigning initial values to the grid
UpperCAmelCase__				   =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
UpperCAmelCase__				   =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def 						_a    (      a :Matrix	,							a :int	,							a :int	,							a :int )							->			bool:
 for i in range(9 ):
  if grid[row][i] == n or grid[i][column] == n:
   return False

 for i in range(3 ):
  for j in range(3 ):
   if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
    return False

 return True
def 						_a    (      a :Matrix )							->			tuple[int, int] | None:
 for i in range(9 ):
  for j in range(9 ):
   if grid[i][j] == 0:
    return i, j
 return None
def 						_a    (      a :Matrix )							->			Matrix | None:
 if location := find_empty_location(a ):
  a  ,     a		  =      location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

 for digit in range(1	,							10 ):
  if is_safe(a	,							a	,							a	,							a ):
   a		  =      digit

   if sudoku(a ) is not None:
    return grid

   a		  =      0

 return None
def 						_a    (      a :Matrix )							->			None:
 for row in grid:
  for cell in row:
   print(a	,							end=''' ''' )
  print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("\nExample grid:\n" + "=" * 20)
          print_solution(example_grid)
          print("\nExample grid solution:")
          UpperCAmelCase__				   =		sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("Cannot find a solution.")


