






"""simple docstring"""


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from ..image_utils import load_image


if is_torch_available():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowercase_				     =     logging.get_logger(__name__)


lowercase_				     =     Dict[str, Any]
lowercase_				     =     List[Prediction]


@add_end_docstrings(_lowerCAmelCase						)
class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     Dict,       *_lowerCamelCase :     Any,       **_lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     super().__init__(*_lowerCamelCase,       **_lowerCamelCase  )

     if self.framework == "tf":
       raise ValueError(f'The {self.__class__} is only available in PyTorch.'  )

     requires_backends(self,       '''vision'''  )
     self.check_model_type(
         dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       **_lowerCamelCase :     str  ):

     '''simple docstring'''
     __A									= {}
     if "threshold" in kwargs:
       __A									= kwargs['''threshold''']
     return {}, {}, postprocess_kwargs


   def __call__(	self :     List[str],       *_lowerCamelCase :     List[str],       **_lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     return super().__call__(*_lowerCamelCase,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     __A									= load_image(_lowerCamelCase  )
     __A									= torch.IntTensor([[image.height, image.width]]  )
     __A									= self.image_processor(images=[image],       return_tensors='''pt'''  )
     if self.tokenizer is not None:
       __A									= self.tokenizer(text=inputs['''words'''],       boxes=inputs['''boxes'''],       return_tensors='''pt'''  )
     __A									= target_size
     return inputs


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     str  ):

     '''simple docstring'''
     __A									= model_inputs.pop('''target_size'''  )
     __A									= self.model(**_lowerCamelCase  )
     __A									= outputs.__class__({'''target_size''': target_size, **outputs}  )
     if self.tokenizer is not None:
       __A									= model_inputs['''bbox''']
     return model_outputs


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     str,       _lowerCamelCase :     Optional[Any]=0.9  ):

     '''simple docstring'''
     __A									= model_outputs['''target_size''']
     if self.tokenizer is not None:
       # This is a LayoutLMForTokenClassification variant.
       # The OCR got the boxes and the model classified the words.
       __A			,    __A									= target_size[0].tolist()

       def unnormalize(_lowerCamelCase :     Tuple  ):
         return self._get_bounding_box(
             torch.Tensor(
                 [
                     (width * bbox[0] / 10_00),
                     (height * bbox[1] / 10_00),
                     (width * bbox[2] / 10_00),
                     (height * bbox[3] / 10_00),
                 ]  )  )

       __A			,    __A									= model_outputs['''logits'''].squeeze(0  ).softmax(dim=-1  ).max(dim=-1  )
       __A									= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
       __A									= [unnormalize(_lowerCamelCase  ) for bbox in model_outputs['''bbox'''].squeeze(0  )]
       __A									= ['''score''', '''label''', '''box''']
       __A									= [dict(zip(_lowerCamelCase,       _lowerCamelCase  )  ) for vals in zip(scores.tolist(),       _lowerCamelCase,       _lowerCamelCase  ) if vals[0] > threshold]
     else:
       # This is a regular ForObjectDetectionModel
       __A									= self.image_processor.post_process_object_detection(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase  )
       __A									= raw_annotations[0]
       __A									= raw_annotation['''scores''']
       __A									= raw_annotation['''labels''']
       __A									= raw_annotation['''boxes''']

       __A									= scores.tolist()
       __A									= [self.model.config.idalabel[label.item()] for label in labels]
       __A									= [self._get_bounding_box(_lowerCamelCase  ) for box in boxes]

       # {"scores": [...], ...} --> [{"score":x, ...}, ...]
       __A									= ['''score''', '''label''', '''box''']
       __A									= [
           dict(zip(_lowerCamelCase,       _lowerCamelCase  )  )
           for vals in zip(raw_annotation['''scores'''],       raw_annotation['''labels'''],       raw_annotation['''boxes''']  )
       ]

     return annotation






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     "torch.Tensor"  ):

     '''simple docstring'''
     if self.framework != "pt":
       raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''  )
     __A			,    __A			,    __A			,    __A									= box.int().tolist()
     __A									= {
         '''xmin''': xmin,
         '''ymin''': ymin,
         '''xmax''': xmax,
         '''ymax''': ymax,
     }
     return bbox












"""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

lowercase_				     =     imread(R'digital_image_processing/image_data/lena_small.jpg')
lowercase_				     =     cvtColor(img, COLOR_BGR2GRAY)



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= cn.convert_to_negative(__UpperCamelCase )
  # assert negative_img array for at least one True
  assert negative_img.any()



def 					lowerCAmelCase				(    ):




  """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(__UpperCamelCase		,       1_1_0 ) ).startswith(
        '''<PIL.Image.Image image mode=RGB size=100x100 at''' )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= canny.gen_gaussian_kernel(9		,       sigma=1.4 )
  # Assert ambiguous array
  assert resp.all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= imread('''digital_image_processing/image_data/lena_small.jpg'''		,       0 )
  # assert ambiguous array for all == True
  assert canny_img.all()
  __A									= canny.canny(__UpperCamelCase )
  # assert canny array for at least one True
  assert canny_array.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert gg.gaussian_filter(__UpperCamelCase		,       5		,       sigma=0.9 ).all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
  __A									= conv.img_convolve(__UpperCamelCase		,       __UpperCamelCase ).astype(__UpperCamelCase )
  assert res.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert med.median_filter(__UpperCamelCase		,       3 ).any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A			,    __A									= sob.sobel_filter(__UpperCamelCase )
  assert grad.any() and theta.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= sp.make_sepia(__UpperCamelCase		,       2_0 )
  assert sepia.all()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ):




  """simple docstring"""







  __A									= bs.Burkes(imread(__UpperCamelCase		,       1 )		,       1_2_0 )
  burkes.process()
  assert burkes.output_img.any()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"		,       ):




  """simple docstring"""







  __A									= rs.NearestNeighbour(imread(__UpperCamelCase		,       1 )		,       4_0_0		,       2_0_0 )
  nn.process()
  assert nn.output.any()





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= '''digital_image_processing/image_data/lena.jpg'''

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

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

  __A									= lbp.get_neighbors_pixel(
      __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  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
  __A									= 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] ):
      __A									= lbp.local_binary_value(__UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert lbp_image.any()




