



import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import YolosImageProcessor



class 			__lowerCAmelCase  (						unittest.TestCase ):




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase=7						,					__UpperCAmelCase=3						,					__UpperCAmelCase=30						,					__UpperCAmelCase=400						,					__UpperCAmelCase=True						,					__UpperCAmelCase=None						,					__UpperCAmelCase=True						,					__UpperCAmelCase=[0.5, 0.5, 0.5]						,					__UpperCAmelCase=[0.5, 0.5, 0.5]						,					__UpperCAmelCase=True						,					__UpperCAmelCase=1 / 255						,					__UpperCAmelCase=True						,					):



    '''simple docstring'''
    # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
    __lowerCamelCase    				=     size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
    __lowerCamelCase    				=     parent
    __lowerCamelCase    				=     batch_size
    __lowerCamelCase    				=     num_channels
    __lowerCamelCase    				=     min_resolution
    __lowerCamelCase    				=     max_resolution
    __lowerCamelCase    				=     do_resize
    __lowerCamelCase    				=     size
    __lowerCamelCase    				=     do_normalize
    __lowerCamelCase    				=     image_mean
    __lowerCamelCase    				=     image_std
    __lowerCamelCase    				=     do_rescale
    __lowerCamelCase    				=     rescale_factor
    __lowerCamelCase    				=     do_pad




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_rescale": self.do_rescale,
        "rescale_factor": self.rescale_factor,
        "do_pad": self.do_pad,
    }




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=False					):



    '''simple docstring'''
    if not batched:
       __lowerCamelCase    				=     image_inputs[0]
       if isinstance(__UpperCAmelCase						,					Image.Image					):
          __lowerCamelCase				,__lowerCamelCase    				=     image.size
       else:
          __lowerCamelCase				,__lowerCamelCase    				=     image.shape[1], image.shape[2]
       if w < h:
          __lowerCamelCase    				=     int(self.size['''shortest_edge'''] * h / w					)
          __lowerCamelCase    				=     self.size['''shortest_edge''']
       elif w > h:
          __lowerCamelCase    				=     self.size['''shortest_edge''']
          __lowerCamelCase    				=     int(self.size['''shortest_edge'''] * w / h					)
       else:
          __lowerCamelCase    				=     self.size['''shortest_edge''']
          __lowerCamelCase    				=     self.size['''shortest_edge''']

    else:
       __lowerCamelCase    				=     []
       for image in image_inputs:
          __lowerCamelCase				,__lowerCamelCase    				=     self.get_expected_values([image]					)
          expected_values.append((expected_height, expected_width)					)
       __lowerCamelCase    				=     max(__UpperCAmelCase						,					key=lambda __UpperCAmelCase					: item[0]					)[0]
       __lowerCamelCase    				=     max(__UpperCAmelCase						,					key=lambda __UpperCAmelCase					: item[1]					)[1]

    return expected_height, expected_width



@require_torch
@require_vision
class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	YolosImageProcessor if is_vision_available() else None




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     YolosImageProcessingTester(self					)




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return self.image_processor_tester.prepare_image_processor_dict()




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(__UpperCAmelCase						,					'''image_mean'''					)					)
    self.assertTrue(hasattr(__UpperCAmelCase						,					'''image_std'''					)					)
    self.assertTrue(hasattr(__UpperCAmelCase						,					'''do_normalize'''					)					)
    self.assertTrue(hasattr(__UpperCAmelCase						,					'''do_resize'''					)					)
    self.assertTrue(hasattr(__UpperCAmelCase						,					'''size'''					)					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size						,					{'''shortest_edge''': 18, '''longest_edge''': 1333}					)
    self.assertEqual(image_processor.do_pad						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     self.image_processing_class.from_dict(
        self.image_processor_dict						,					size=42						,					max_size=84						,					pad_and_return_pixel_mask=__UpperCAmelCase					)
    self.assertEqual(image_processor.size						,					{'''shortest_edge''': 42, '''longest_edge''': 84}					)
    self.assertEqual(image_processor.do_pad						,					__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    pass




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Initialize image_processing
    __lowerCamelCase    				=     self.image_processing_class(**self.image_processor_dict					)
    # create random PIL images
    __lowerCamelCase    				=     prepare_image_inputs(self.image_processor_tester						,					equal_resolution=__UpperCAmelCase					)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase						,					Image.Image					)

    # Test not batched input
    __lowerCamelCase    				=     image_processing(image_inputs[0]						,					return_tensors='''pt'''					).pixel_values

    __lowerCamelCase				,__lowerCamelCase    				=     self.image_processor_tester.get_expected_values(__UpperCAmelCase					)

    self.assertEqual(
        encoded_images.shape						,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,					)

    # Test batched
    __lowerCamelCase				,__lowerCamelCase    				=     self.image_processor_tester.get_expected_values(__UpperCAmelCase						,					batched=__UpperCAmelCase					)

    __lowerCamelCase    				=     image_processing(__UpperCAmelCase						,					return_tensors='''pt'''					).pixel_values
    self.assertEqual(
        encoded_images.shape						,					(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )						,					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Initialize image_processing
    __lowerCamelCase    				=     self.image_processing_class(**self.image_processor_dict					)
    # create random numpy tensors
    __lowerCamelCase    				=     prepare_image_inputs(self.image_processor_tester						,					equal_resolution=__UpperCAmelCase						,					numpify=__UpperCAmelCase					)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase						,					np.ndarray					)

    # Test not batched input
    __lowerCamelCase    				=     image_processing(image_inputs[0]						,					return_tensors='''pt'''					).pixel_values

    __lowerCamelCase				,__lowerCamelCase    				=     self.image_processor_tester.get_expected_values(__UpperCAmelCase					)

    self.assertEqual(
        encoded_images.shape						,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,					)

    # Test batched
    __lowerCamelCase    				=     image_processing(__UpperCAmelCase						,					return_tensors='''pt'''					).pixel_values

    __lowerCamelCase				,__lowerCamelCase    				=     self.image_processor_tester.get_expected_values(__UpperCAmelCase						,					batched=__UpperCAmelCase					)

    self.assertEqual(
        encoded_images.shape						,					(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )						,					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Initialize image_processing
    __lowerCamelCase    				=     self.image_processing_class(**self.image_processor_dict					)
    # create random PyTorch tensors
    __lowerCamelCase    				=     prepare_image_inputs(self.image_processor_tester						,					equal_resolution=__UpperCAmelCase						,					torchify=__UpperCAmelCase					)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase						,					torch.Tensor					)

    # Test not batched input
    __lowerCamelCase    				=     image_processing(image_inputs[0]						,					return_tensors='''pt'''					).pixel_values

    __lowerCamelCase				,__lowerCamelCase    				=     self.image_processor_tester.get_expected_values(__UpperCAmelCase					)

    self.assertEqual(
        encoded_images.shape						,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,					)

    # Test batched
    __lowerCamelCase    				=     image_processing(__UpperCAmelCase						,					return_tensors='''pt'''					).pixel_values

    __lowerCamelCase				,__lowerCamelCase    				=     self.image_processor_tester.get_expected_values(__UpperCAmelCase						,					batched=__UpperCAmelCase					)

    self.assertEqual(
        encoded_images.shape						,					(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )						,					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Initialize image_processings
    __lowerCamelCase    				=     self.image_processing_class(**self.image_processor_dict					)
    __lowerCamelCase    				=     self.image_processing_class(do_resize=__UpperCAmelCase						,					do_normalize=__UpperCAmelCase						,					do_rescale=__UpperCAmelCase					)
    # create random PyTorch tensors
    __lowerCamelCase    				=     prepare_image_inputs(self.image_processor_tester						,					equal_resolution=__UpperCAmelCase						,					torchify=__UpperCAmelCase					)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase						,					torch.Tensor					)

    # Test whether the method "pad" and calling the image processor return the same tensors
    __lowerCamelCase    				=     image_processing_a.pad(__UpperCAmelCase						,					return_tensors='''pt'''					)
    __lowerCamelCase    				=     image_processing_a(__UpperCAmelCase						,					return_tensors='''pt'''					)

    self.assertTrue(
        torch.allclose(encoded_images_with_method['''pixel_values''']						,					encoded_images['''pixel_values''']						,					atol=1E-4					)					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # prepare image and target
    __lowerCamelCase    				=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
    with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''						,					'''r'''					) as f:
       __lowerCamelCase    				=     json.loads(f.read()					)

    __lowerCamelCase    				=     {'''image_id''': 39769, '''annotations''': target}

    # encode them
    __lowerCamelCase    				=     YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''					)
    __lowerCamelCase    				=     image_processing(images=__UpperCAmelCase						,					annotations=__UpperCAmelCase						,					return_tensors='''pt'''					)

    # verify pixel values
    __lowerCamelCase    				=     torch.Size([1, 3, 800, 1066]					)
    self.assertEqual(encoding['''pixel_values'''].shape						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     torch.tensor([0.2_796, 0.3_138, 0.3_481]					)
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,					__UpperCAmelCase						,					atol=1E-4					)					)

    # verify area
    __lowerCamelCase    				=     torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,					__UpperCAmelCase					)					)
    # verify boxes
    __lowerCamelCase    				=     torch.Size([6, 4]					)
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,					__UpperCAmelCase					)
    __lowerCamelCase    				=     torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,					__UpperCAmelCase						,					atol=1E-3					)					)
    # verify image_id
    __lowerCamelCase    				=     torch.tensor([39769]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,					__UpperCAmelCase					)					)
    # verify is_crowd
    __lowerCamelCase    				=     torch.tensor([0, 0, 0, 0, 0, 0]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,					__UpperCAmelCase					)					)
    # verify class_labels
    __lowerCamelCase    				=     torch.tensor([75, 75, 63, 65, 17, 17]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,					__UpperCAmelCase					)					)
    # verify orig_size
    __lowerCamelCase    				=     torch.tensor([480, 640]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,					__UpperCAmelCase					)					)
    # verify size
    __lowerCamelCase    				=     torch.tensor([800, 1066]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,					__UpperCAmelCase					)					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # prepare image, target and masks_path
    __lowerCamelCase    				=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
    with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''						,					'''r'''					) as f:
       __lowerCamelCase    				=     json.loads(f.read()					)

    __lowerCamelCase    				=     {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

    __lowerCamelCase    				=     pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''					)

    # encode them
    __lowerCamelCase    				=     YolosImageProcessor(format='''coco_panoptic'''					)
    __lowerCamelCase    				=     image_processing(images=__UpperCAmelCase						,					annotations=__UpperCAmelCase						,					masks_path=__UpperCAmelCase						,					return_tensors='''pt'''					)

    # verify pixel values
    __lowerCamelCase    				=     torch.Size([1, 3, 800, 1066]					)
    self.assertEqual(encoding['''pixel_values'''].shape						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     torch.tensor([0.2_796, 0.3_138, 0.3_481]					)
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,					__UpperCAmelCase						,					atol=1E-4					)					)

    # verify area
    __lowerCamelCase    				=     torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,					__UpperCAmelCase					)					)
    # verify boxes
    __lowerCamelCase    				=     torch.Size([6, 4]					)
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,					__UpperCAmelCase					)
    __lowerCamelCase    				=     torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,					__UpperCAmelCase						,					atol=1E-3					)					)
    # verify image_id
    __lowerCamelCase    				=     torch.tensor([39769]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,					__UpperCAmelCase					)					)
    # verify is_crowd
    __lowerCamelCase    				=     torch.tensor([0, 0, 0, 0, 0, 0]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,					__UpperCAmelCase					)					)
    # verify class_labels
    __lowerCamelCase    				=     torch.tensor([17, 17, 63, 75, 75, 93]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,					__UpperCAmelCase					)					)
    # verify masks
    __lowerCamelCase    				=     822873
    self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,					__UpperCAmelCase					)
    # verify orig_size
    __lowerCamelCase    				=     torch.tensor([480, 640]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,					__UpperCAmelCase					)					)
    # verify size
    __lowerCamelCase    				=     torch.tensor([800, 1066]					)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,					__UpperCAmelCase					)					)











import numpy

# List of input, output pairs
a_								=       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
a_								=       (((515, 22, 13), 555), ((61, 35, 49), 150))
a_								=       [2, 4, 1, 5]
a_								=       len(train_data)
a_								=       0.0_09





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			List[Any]="train"				):
   return calculate_hypothesis_value(_UpperCamelCase			,_UpperCamelCase				) - output(
       _UpperCamelCase			,_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			Optional[Any]				):
   __lowerCamelCase    				=     0
   for i in range(len(_UpperCamelCase				) - 1				):
      hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
   hyp_val += parameter_vector[0]
   return hyp_val





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			List[Any]				):
   if data_set == "train":
      return train_data[example_no][1]
   elif data_set == "test":
      return test_data[example_no][1]
   return None





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Union[str, Any]				):
   if data_set == "train":
      return _hypothesis_value(train_data[example_no][0]				)
   elif data_set == "test":
      return _hypothesis_value(test_data[example_no][0]				)
   return None





def 		a__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Dict=m				):
   __lowerCamelCase    				=     0
   for i in range(_UpperCamelCase				):
      if index == -1:
         summation_value += _error(_UpperCamelCase				)
      else:
         summation_value += _error(_UpperCamelCase				) * train_data[i][0][index]
   return summation_value





def 		a__      (		_UpperCamelCase      :			Optional[int]				):
   __lowerCamelCase    				=     summation_of_cost_derivative(_UpperCamelCase			,_UpperCamelCase				) / m
   return cost_derivative_value





def 		a__      (		):
   global parameter_vector
   # Tune these values to set a tolerance value for predicted output
   __lowerCamelCase    				=     0.000_002
   __lowerCamelCase    				=     0
   __lowerCamelCase    				=     0
   while True:
      j += 1
      __lowerCamelCase    				=     [0, 0, 0, 0]
      for i in range(0			,len(_UpperCamelCase				)				):
         __lowerCamelCase    				=     get_cost_derivative(i - 1				)
         __lowerCamelCase    				=     (
             parameter_vector[i] - LEARNING_RATE * cost_derivative
         )
      if numpy.allclose(
          _UpperCamelCase			,_UpperCamelCase			,atol=_UpperCamelCase			,rtol=_UpperCamelCase			,):
         break
      __lowerCamelCase    				=     temp_parameter_vector
   print(('''Number of iterations:''', j)				)





def 		a__      (		):
   for i in range(len(_UpperCamelCase				)				):
      print(('''Actual output value:''', output(_UpperCamelCase			,'''test'''				))				)
      print(('''Hypothesis output:''', calculate_hypothesis_value(_UpperCamelCase			,'''test'''				))				)


if __name__ == "__main__":
  run_gradient_descent()
  print("""\nTesting gradient descent for a linear hypothesis function.\n""")
  test_gradient_descent()






