




'''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:
  a		:  Tuple   			=			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     UpperCamelCase					(  unittest.TestCase					):



  def 						a_       ( self   ):







      '''simple docstring'''


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

      _a							:       List[Any]       					=			PixaStructImageProcessor()
      _a							:       Tuple       					=			TaTokenizer.from_pretrained("""t5-small"""   )

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

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

      return image_inputs



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			PixaStructProcessor(tokenizer=self.get_tokenizer()		,    image_processor=self.get_image_processor()   )
      processor.save_pretrained(self.tmpdirname   )

      _a							:       Union[str, Any]       					=			self.get_tokenizer(bos_token="""(BOS)"""		,    eos_token="""(EOS)"""   )
      _a							:       str       					=			self.get_image_processor(do_normalize=__lowerCAmelCase		,    padding_value=1.0   )

      _a							:       List[str]       					=			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 						a_       ( self   ):







      '''simple docstring'''


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

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

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

      _a							:       Optional[Any]       					=			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 						a_       ( self   ):







      '''simple docstring'''


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

      _a							:       Tuple       					=			PixaStructProcessor(tokenizer=__lowerCAmelCase		,    image_processor=__lowerCAmelCase   )

      _a							:       int       					=			"""lower newer"""

      _a							:       List[Any]       					=			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 						a_       ( self   ):







      '''simple docstring'''


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

      _a							:       Tuple       					=			PixaStructProcessor(tokenizer=__lowerCAmelCase		,    image_processor=__lowerCAmelCase   )

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

      _a							:       int       					=			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 						a_       ( self   ):







      '''simple docstring'''


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

      _a							:       Optional[int]       					=			PixaStructProcessor(tokenizer=__lowerCAmelCase		,    image_processor=__lowerCAmelCase   )

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

      _a							:       Union[str, Any]       					=			processor(text=__lowerCAmelCase		,    images=__lowerCAmelCase   )

      _a							:       Dict       					=			[512, 1024, 2048, 4096]
      _a							:       Optional[int]       					=			[770, 770, 770, 770]
      # with text
      for i, max_patch in enumerate(__lowerCAmelCase   ):
          _a							:       List[str]       					=			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							:       List[Any]       					=			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 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[str]       					=			self.get_image_processor()
      _a							:       Tuple       					=			self.get_tokenizer()

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

      _a							:       Optional[Any]       					=			[[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 						a_       ( self   ):







      '''simple docstring'''


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

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

      _a							:       Dict       					=			"""lower newer"""
      _a							:       Tuple       					=			self.prepare_image_inputs()

      _a							:       List[Any]       					=			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							:       Dict       					=			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'''





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=3	):
    _a							:       Optional[Any]       					=			int(mask / 2	)
    # copy image size
    _a							:       Optional[int]       					=			zeros_like(UpperCAmelCase_	)
    for i in range(UpperCAmelCase_      ,  gray_img.shape[0] - bd	):
        for j in range(UpperCAmelCase_      ,  gray_img.shape[1] - bd	):
            # get mask according with mask
            _a							:       List[str]       					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _a							:       Optional[Any]       					=			sort(UpperCAmelCase_	)[inta(divide((multiply(UpperCAmelCase_      ,  UpperCAmelCase_	))      ,  2	) + 1	)]
            _a							:       int       					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  a		:  List[Any]   			=			imread('''../image_data/lena.jpg''')
  # turn image in gray scale value
  a		:  Optional[Any]   			=			cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

  # show result images
  imshow('''median filter with 3x3 mask''', medianaxa)
  imshow('''median filter with 5x5 mask''', medianaxa)
  waitKey(0)


