





'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     str			=  ['''image_processor''', '''tokenizer''']
      _lowercase:     Any			=  '''Pix2StructImageProcessor'''
      _lowercase:     Union[str, Any]			=  ('''T5Tokenizer''', '''T5TokenizerFast''')




      def __init__(     self     :					List[Any]     ,     __snake_case     :					str     ,     __snake_case     :					Optional[int]    )							-> List[str]:
            _lowerCAmelCase					=				False
            super().__init__(__snake_case     ,     __snake_case    )




      def __call__(     self     :					List[str]     ,     __snake_case     :					str=None     ,     __snake_case     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,     __snake_case     :					bool = True     ,     __snake_case     :					Union[bool, str, PaddingStrategy] = False     ,     __snake_case     :					Union[bool, str, TruncationStrategy] = None     ,     __snake_case     :					Optional[int] = None     ,     __snake_case     :					Optional[int] = 20_48     ,     __snake_case     :					int = 0     ,     __snake_case     :					Optional[int] = None     ,     __snake_case     :					Optional[bool] = None     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = True     ,     __snake_case     :					Optional[Union[str, TensorType]] = None     ,     **__snake_case     :					Dict     ,     )							-> BatchEncoding:
            if images is None and text is None:
                  raise ValueError("""You have to specify either images or text."""    )

            # Get only text
            if images is None and not self.image_processor.is_vqa:
                  _lowerCAmelCase					=				self.tokenizer
                  _lowerCAmelCase					=				self.tokenizer(
                      text=__snake_case     ,     add_special_tokens=__snake_case     ,     padding=__snake_case     ,     truncation=__snake_case     ,     max_length=__snake_case     ,     stride=__snake_case     ,     pad_to_multiple_of=__snake_case     ,     return_attention_mask=__snake_case     ,     return_overflowing_tokens=__snake_case     ,     return_special_tokens_mask=__snake_case     ,     return_offsets_mapping=__snake_case     ,     return_token_type_ids=__snake_case     ,     return_length=__snake_case     ,     verbose=__snake_case     ,     return_tensors=__snake_case     ,     **__snake_case     ,     )
                  return text_encoding

            if not self.image_processor.is_vqa:
                  # add pixel_values
                  _lowerCAmelCase					=				self.image_processor(
                      __snake_case     ,     return_tensors=__snake_case     ,     max_patches=__snake_case     ,     **__snake_case    )
            else:
                  # add pixel_values and bbox
                  _lowerCAmelCase					=				self.image_processor(
                      __snake_case     ,     return_tensors=__snake_case     ,     max_patches=__snake_case     ,     header_text=__snake_case     ,     **__snake_case    )

            if text is not None and not self.image_processor.is_vqa:
                  _lowerCAmelCase					=				self.tokenizer(
                      text=__snake_case     ,     add_special_tokens=__snake_case     ,     padding=__snake_case     ,     truncation=__snake_case     ,     max_length=__snake_case     ,     stride=__snake_case     ,     pad_to_multiple_of=__snake_case     ,     return_attention_mask=__snake_case     ,     return_overflowing_tokens=__snake_case     ,     return_special_tokens_mask=__snake_case     ,     return_offsets_mapping=__snake_case     ,     return_token_type_ids=__snake_case     ,     return_length=__snake_case     ,     verbose=__snake_case     ,     return_tensors=__snake_case     ,     **__snake_case     ,     )

                  if "attention_mask" in text_encoding:
                        _lowerCAmelCase					=				text_encoding.pop("""attention_mask"""    )
                  if "input_ids" in text_encoding:
                        _lowerCAmelCase					=				text_encoding.pop("""input_ids"""    )
            else:
                  _lowerCAmelCase					=				None

            if text_encoding is not None:
                  encoding_image_processor.update(__snake_case    )

            return encoding_image_processor




      def 							lowercase__       (     self     :					Optional[Any]     ,     *__snake_case     :					Dict     ,     **__snake_case     :					str    )							-> Tuple:
            return self.tokenizer.batch_decode(*__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     *__snake_case     :					Optional[Any]     ,     **__snake_case     :					str    )							-> Tuple:
            return self.tokenizer.decode(*__snake_case     ,     **__snake_case    )




      @property
      def 							lowercase__       (     self     :					Optional[int]    )							-> Optional[Any]:
            _lowerCAmelCase					=				self.tokenizer.model_input_names
            _lowerCAmelCase					=				self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )













'''simple docstring'''






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__							:  List[str]    =imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A__							:  Union[str, Any]    =cvtColor(img, COLOR_BGR2GRAY)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				cn.convert_to_negative(lowerCAmelCase    )
      # assert negative_img array for at least one True
      assert negative_img.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""    ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowerCAmelCase				,      1_10    )    ).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""    )
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				canny.gen_gaussian_kernel(9				,      sigma=1.4    )
      # Assert ambiguous array
      assert resp.all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				imread("""digital_image_processing/image_data/lena_small.jpg"""				,      0    )
      # assert ambiguous array for all == True
      assert canny_img.all()
      _lowerCAmelCase					=				canny.canny(lowerCAmelCase    )
      # assert canny array for at least one True
      assert canny_array.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert gg.gaussian_filter(lowerCAmelCase				,      5				,      sigma=0.9    ).all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]    )
      _lowerCAmelCase					=				conv.img_convolve(lowerCAmelCase				,      lowerCAmelCase    ).astype(lowerCAmelCase    )
      assert res.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert med.median_filter(lowerCAmelCase				,      3    ).any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				sob.sobel_filter(lowerCAmelCase    )
      assert grad.any() and theta.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				sp.make_sepia(lowerCAmelCase				,      20    )
      assert sepia.all()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"    ):





      """simple docstring"""


      _lowerCAmelCase					=				bs.Burkes(imread(lowerCAmelCase				,      1    )				,      1_20    )
      burkes.process()
      assert burkes.output_img.any()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"				,      ):





      """simple docstring"""


      _lowerCAmelCase					=				rs.NearestNeighbour(imread(lowerCAmelCase				,      1    )				,      4_00				,      2_00    )
      nn.process()
      assert nn.output.any()





def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      _lowerCAmelCase					=				imread(lowerCAmelCase				,      0    )

      # Test for get_neighbors_pixel function() return not None
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				image[x_coordinate][y_coordinate]

      _lowerCAmelCase					=				lbp.get_neighbors_pixel(
          lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      _lowerCAmelCase					=				np.zeros((image.shape[0], image.shape[1])    )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0				,      image.shape[0]    ):
            for j in range(0				,      image.shape[1]    ):
                  _lowerCAmelCase					=				lbp.local_binary_value(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert lbp_image.any()






