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     _A   (    unittest.TestCase							):






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





       '''simple docstring'''





       UpperCamelCase__										=      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      min_resolution
       UpperCamelCase__										=      max_resolution
       UpperCamelCase__										=      do_resize
       UpperCamelCase__										=      size
       UpperCamelCase__										=      do_normalize
       UpperCamelCase__										=      image_mean
       UpperCamelCase__										=      image_std
       UpperCamelCase__										=      do_rescale
       UpperCamelCase__										=      rescale_factor
       UpperCamelCase__										=      do_pad






  def 		_a					(self	)		->      Optional[Any]:





       '''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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      str:





       '''simple docstring'''





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

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

       return expected_height, expected_width



@require_torch
@require_vision
class     _A   (    __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Any						=YolosImageProcessor if is_vision_available() else None






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      YolosImageProcessingTester(self	)






  @property
  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''image_mean'''	)	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''image_std'''	)	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''do_normalize'''	)	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''do_resize'''	)	)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''size'''	)	)






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      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   ,				SCREAMING_SNAKE_CASE_	)

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






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       UpperCamelCase__										=      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=SCREAMING_SNAKE_CASE_	)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				Image.Image	)

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

       UpperCamelCase__						,       UpperCamelCase__										=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)

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

       # Test batched
       UpperCamelCase__						,       UpperCamelCase__										=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   ,				batched=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      image_processing(SCREAMING_SNAKE_CASE_   ,				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 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       UpperCamelCase__										=      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=SCREAMING_SNAKE_CASE_   ,				numpify=SCREAMING_SNAKE_CASE_	)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				np.ndarray	)

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

       UpperCamelCase__						,       UpperCamelCase__										=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)

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

       # Test batched
       UpperCamelCase__										=      image_processing(SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).pixel_values

       UpperCamelCase__						,       UpperCamelCase__										=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   ,				batched=SCREAMING_SNAKE_CASE_	)

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






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       UpperCamelCase__										=      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=SCREAMING_SNAKE_CASE_   ,				torchify=SCREAMING_SNAKE_CASE_	)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				torch.Tensor	)

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

       UpperCamelCase__						,       UpperCamelCase__										=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)

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

       # Test batched
       UpperCamelCase__										=      image_processing(SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).pixel_values

       UpperCamelCase__						,       UpperCamelCase__										=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   ,				batched=SCREAMING_SNAKE_CASE_	)

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






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.image_processing_class(**self.image_processor_dict	)
       UpperCamelCase__										=      self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE_   ,				do_normalize=SCREAMING_SNAKE_CASE_   ,				do_rescale=SCREAMING_SNAKE_CASE_	)
       # create random PyTorch tensors
       UpperCamelCase__										=      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=SCREAMING_SNAKE_CASE_   ,				torchify=SCREAMING_SNAKE_CASE_	)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				torch.Tensor	)

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

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






  @slow
  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,				'''r'''	) as f:
            UpperCamelCase__										=      json.loads(f.read()	)

       UpperCamelCase__										=      {'''image_id''': 3_9769, '''annotations''': target}

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

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

       UpperCamelCase__										=      torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-4	)	)

       # verify area
       UpperCamelCase__										=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify boxes
       UpperCamelCase__										=      torch.Size([6, 4]	)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-3	)	)
       # verify image_id
       UpperCamelCase__										=      torch.tensor([3_9769]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify is_crowd
       UpperCamelCase__										=      torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify class_labels
       UpperCamelCase__										=      torch.tensor([75, 75, 63, 65, 17, 17]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify orig_size
       UpperCamelCase__										=      torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify size
       UpperCamelCase__										=      torch.tensor([800, 1066]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				SCREAMING_SNAKE_CASE_	)	)






  @slow
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,				'''r'''	) as f:
            UpperCamelCase__										=      json.loads(f.read()	)

       UpperCamelCase__										=      {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

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

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

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

       UpperCamelCase__										=      torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-4	)	)

       # verify area
       UpperCamelCase__										=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify boxes
       UpperCamelCase__										=      torch.Size([6, 4]	)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-3	)	)
       # verify image_id
       UpperCamelCase__										=      torch.tensor([3_9769]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify is_crowd
       UpperCamelCase__										=      torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify class_labels
       UpperCamelCase__										=      torch.tensor([17, 17, 63, 75, 75, 93]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify masks
       UpperCamelCase__										=      82_2873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,				SCREAMING_SNAKE_CASE_	)
       # verify orig_size
       UpperCamelCase__										=      torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				SCREAMING_SNAKE_CASE_	)	)
       # verify size
       UpperCamelCase__										=      torch.tensor([800, 1066]	)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				SCREAMING_SNAKE_CASE_	)	)




import math

import tensorflow as tf
from packaging import version







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							) ,							x.dtype							)							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(math.pi ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(A ,							3							))							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)

     return x * tf.tanh(tf.math.softplus(A							)							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.79_78_84_56_08 ,							x.dtype							)

     return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)							))







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(1.7_02 ,							x.dtype							)
     return x * tf.math.sigmoid(coeff * x							)







def  __UpperCamelCase    (						A							):
     return tf.clip_by_value(_gelu(A							) ,							-10 ,							10							)







def  __UpperCamelCase    (						A ,							A=-1							):
     UpperCamelCase__						,       UpperCamelCase__										=      tf.split(A ,							2 ,							axis=A							)
     return a * tf.math.sigmoid(A							)


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):







     def  __UpperCamelCase    (						A							):
          return tf.keras.activations.gelu(A ,							approximate=A							)

     __magic_name__								=tf.keras.activations.gelu



     __magic_name__								=approximate_gelu_wrap
else:
     __magic_name__								=_gelu
     __magic_name__								=_gelu_new


__magic_name__								={
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}







def  __UpperCamelCase    (						A							):
     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}"							)



