



'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  UpperCamelCase__    :     Union[str, Any]			  =   False

if is_vision_available():
  from PIL import Image

  from transformers import (
      AutoProcessor,
      PixaStructImageProcessor,
      PixaStructProcessor,
      PreTrainedTokenizerFast,
      TaTokenizer,
  )


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,							reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''',							)
@require_vision
@require_torch
class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Tuple						=					tempfile.mkdtemp()

    A_  :		Optional[int]						=					PixaStructImageProcessor()
    A_  :		List[str]						=					TaTokenizer.from_pretrained("""t5-small"""  )

    A_  :		Tuple						=					PixaStructProcessor(_lowerCamelCase					,    _lowerCamelCase  )

    processor.save_pretrained(self.tmpdirname  )







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Union[str, Any]:
    return AutoProcessor.from_pretrained(self.tmpdirname					,    **_lowerCamelCase  ).tokenizer







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    List[str]:
    return AutoProcessor.from_pretrained(self.tmpdirname					,    **_lowerCamelCase  ).image_processor







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    shutil.rmtree(self.tmpdirname  )







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_  :		Optional[Any]						=					[np.random.randint(255					,    size=(3, 30, 400)					,    dtype=np.uinta  )]

    A_  :		Optional[Any]						=					[Image.fromarray(np.moveaxis(_lowerCamelCase					,    0					,    -1  )  ) for x in image_inputs]

    return image_inputs







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_  :		Optional[int]						=					PixaStructProcessor(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_  :		Union[str, Any]						=					PixaStructProcessor.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       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		Dict						=					self.get_image_processor()
    A_  :		Dict						=					self.get_tokenizer()

    A_  :		Any						=					PixaStructProcessor(tokenizer=_lowerCamelCase					,    image_processor=_lowerCamelCase  )

    A_  :		Dict						=					self.prepare_image_inputs()

    A_  :		str						=					image_processor(_lowerCamelCase					,    return_tensors="""np"""  )
    A_  :		Dict						=					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       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		List[str]						=					self.get_image_processor()
    A_  :		str						=					self.get_tokenizer()

    A_  :		Tuple						=					PixaStructProcessor(tokenizer=_lowerCamelCase					,    image_processor=_lowerCamelCase  )

    A_  :		Dict						=					"""lower newer"""

    A_  :		Optional[int]						=					processor(text=_lowerCamelCase  )

    A_  :		Optional[int]						=					tokenizer(_lowerCamelCase					,    return_token_type_ids=_lowerCamelCase					,    add_special_tokens=_lowerCamelCase  )

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







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_  :		Any						=					self.get_image_processor()
    A_  :		int						=					self.get_tokenizer()

    A_  :		int						=					PixaStructProcessor(tokenizer=_lowerCamelCase					,    image_processor=_lowerCamelCase  )

    A_  :		Tuple						=					"""lower newer"""
    A_  :		Tuple						=					self.prepare_image_inputs()

    A_  :		str						=					processor(text=_lowerCamelCase					,    images=_lowerCamelCase  )

    self.assertListEqual(
        list(inputs.keys()  )					,    ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]  )

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







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					self.get_image_processor()
    A_  :		str						=					self.get_tokenizer()

    A_  :		Optional[int]						=					PixaStructProcessor(tokenizer=_lowerCamelCase					,    image_processor=_lowerCamelCase  )

    A_  :		Any						=					"""lower newer"""
    A_  :		List[str]						=					self.prepare_image_inputs()

    A_  :		int						=					processor(text=_lowerCamelCase					,    images=_lowerCamelCase  )

    A_  :		Dict						=					[512, 1024, 2048, 4096]
    A_  :		List[str]						=					[770, 770, 770, 770]
    # with text
    for i, max_patch in enumerate(_lowerCamelCase  ):
       A_  :		int						=					processor(text=_lowerCamelCase					,    images=_lowerCamelCase					,    max_patches=_lowerCamelCase  )
       self.assertEqual(inputs["""flattened_patches"""][0].shape[0]					,    _lowerCamelCase  )
       self.assertEqual(inputs["""flattened_patches"""][0].shape[1]					,    expected_hidden_size[i]  )

    # without text input
    for i, max_patch in enumerate(_lowerCamelCase  ):
       A_  :		Dict						=					processor(images=_lowerCamelCase					,    max_patches=_lowerCamelCase  )
       self.assertEqual(inputs["""flattened_patches"""][0].shape[0]					,    _lowerCamelCase  )
       self.assertEqual(inputs["""flattened_patches"""][0].shape[1]					,    expected_hidden_size[i]  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		List[str]						=					self.get_image_processor()
    A_  :		Dict						=					self.get_tokenizer()

    A_  :		Optional[int]						=					PixaStructProcessor(tokenizer=_lowerCamelCase					,    image_processor=_lowerCamelCase  )

    A_  :		List[str]						=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    A_  :		Union[str, Any]						=					processor.batch_decode(_lowerCamelCase  )
    A_  :		Any						=					tokenizer.batch_decode(_lowerCamelCase  )

    self.assertListEqual(_lowerCamelCase					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Dict						=					self.get_image_processor()
    A_  :		Any						=					self.get_tokenizer()

    A_  :		List[str]						=					PixaStructProcessor(tokenizer=_lowerCamelCase					,    image_processor=_lowerCamelCase  )

    A_  :		Union[str, Any]						=					"""lower newer"""
    A_  :		Dict						=					self.prepare_image_inputs()

    A_  :		List[str]						=					processor(text=_lowerCamelCase					,    images=_lowerCamelCase  )

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

    A_  :		str						=					processor(text=_lowerCamelCase  )

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





'''simple docstring'''





def     UpperCAmelCase     (    a_ = 9_9_8_0_0_1		)   ->       int:



   """simple docstring"""






   for number in range(n - 1      ,		9_9_9_9      ,		-1		):
      A_  :		Optional[Any]						=					str(a_		)

      # checks whether 'str_number' is a palindrome.
      if str_number == str_number[::-1]:
         A_  :		int						=					9_9_9

         # if 'number' is a product of two 3-digit numbers
         # then number is the answer otherwise fetch next number.
         while divisor != 9_9:
            if (number % divisor == 0) and (len(str(number // divisor		)		) == 3.0):
               return number
            divisor -= 1
   raise ValueError("""That number is larger than our acceptable range."""		)


if __name__ == "__main__":
  print(f'{solution() = }')
