


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      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   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   ,							):
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        lowercase_       :Optional[int]     =       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
        lowercase_       :Optional[Any]     =       parent
        lowercase_       :Union[str, Any]     =       batch_size
        lowercase_       :List[Any]     =       num_channels
        lowercase_       :Tuple     =       min_resolution
        lowercase_       :Union[str, Any]     =       max_resolution
        lowercase_       :Dict     =       do_resize
        lowercase_       :List[Any]     =       size
        lowercase_       :Any     =       do_normalize
        lowercase_       :List[str]     =       image_mean
        lowercase_       :Union[str, Any]     =       image_std
        lowercase_       :int     =       do_rescale
        lowercase_       :int     =       rescale_factor
        lowercase_       :Union[str, Any]     =       do_pad
   def 		UpperCamelCase  (     self		):
        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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=False		):
        if not batched:
             lowercase_       :Optional[int]     =       image_inputs[0]
             if isinstance(UpperCamelCase_   ,							Image.Image		):
                  lowercase_       :int     =       image.size
             else:
                  lowercase_       :int     =       image.shape[1], image.shape[2]
             if w < h:
                  lowercase_       :int     =       int(self.size['''shortest_edge'''] * h / w		)
                  lowercase_       :List[Any]     =       self.size['''shortest_edge''']
             elif w > h:
                  lowercase_       :Tuple     =       self.size['''shortest_edge''']
                  lowercase_       :Any     =       int(self.size['''shortest_edge'''] * w / h		)
             else:
                  lowercase_       :Optional[int]     =       self.size['''shortest_edge''']
                  lowercase_       :int     =       self.size['''shortest_edge''']

        else:
             lowercase_       :str     =       []
             for image in image_inputs:
                  lowercase_       :int     =       self.get_expected_values([image]		)
                  expected_values.append((expected_height, expected_width)		)
             lowercase_       :Optional[Any]     =       max(UpperCamelCase_   ,							key=lambda UpperCamelCase_		: item[0]		)[0]
             lowercase_       :List[str]     =       max(UpperCamelCase_   ,							key=lambda UpperCamelCase_		: item[1]		)[1]

        return expected_height, expected_width






@require_torch
@require_vision
class      UpperCamelCase					(		lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	Any									=YolosImageProcessor if is_vision_available() else None
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       YolosImageProcessingTester(self		)
   @property
   def 		UpperCamelCase  (     self		):
        return self.image_processor_tester.prepare_image_processor_dict()
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       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 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       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_		)

        lowercase_       :List[str]     =       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 		UpperCamelCase  (     self		):
        pass
   def 		UpperCamelCase  (     self		):
        # Initialize image_processing
        lowercase_       :List[str]     =       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        lowercase_       :Union[str, Any]     =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=UpperCamelCase_		)
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase_   ,							Image.Image		)

        # Test not batched input
        lowercase_       :Union[str, Any]     =       image_processing(image_inputs[0]   ,							return_tensors='''pt'''		).pixel_values

        lowercase_       :Optional[Any]     =       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
        lowercase_       :str     =       self.image_processor_tester.get_expected_values(UpperCamelCase_   ,							batched=UpperCamelCase_		)

        lowercase_       :Tuple     =       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 		UpperCamelCase  (     self		):
        # Initialize image_processing
        lowercase_       :List[Any]     =       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        lowercase_       :Dict     =       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
        lowercase_       :Tuple     =       image_processing(image_inputs[0]   ,							return_tensors='''pt'''		).pixel_values

        lowercase_       :str     =       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
        lowercase_       :Tuple     =       image_processing(UpperCamelCase_   ,							return_tensors='''pt'''		).pixel_values

        lowercase_       :Optional[int]     =       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 		UpperCamelCase  (     self		):
        # Initialize image_processing
        lowercase_       :int     =       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        lowercase_       :Optional[Any]     =       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
        lowercase_       :Any     =       image_processing(image_inputs[0]   ,							return_tensors='''pt'''		).pixel_values

        lowercase_       :Optional[Any]     =       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
        lowercase_       :Union[str, Any]     =       image_processing(UpperCamelCase_   ,							return_tensors='''pt'''		).pixel_values

        lowercase_       :List[str]     =       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 		UpperCamelCase  (     self		):
        # Initialize image_processings
        lowercase_       :str     =       self.image_processing_class(**self.image_processor_dict		)
        lowercase_       :List[Any]     =       self.image_processing_class(do_resize=UpperCamelCase_   ,							do_normalize=UpperCamelCase_   ,							do_rescale=UpperCamelCase_		)
        # create random PyTorch tensors
        lowercase_       :List[str]     =       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
        lowercase_       :Dict     =       image_processing_a.pad(UpperCamelCase_   ,							return_tensors='''pt'''		)
        lowercase_       :Any     =       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 		UpperCamelCase  (     self		):
        # prepare image and target
        lowercase_       :Any     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
        with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,							'''r'''		) as f:
             lowercase_       :Any     =       json.loads(f.read()		)

        lowercase_       :int     =       {'''image_id''': 3_9769, '''annotations''': target}

        # encode them
        lowercase_       :List[Any]     =       YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''		)
        lowercase_       :Any     =       image_processing(images=UpperCamelCase_   ,							annotations=UpperCamelCase_   ,							return_tensors='''pt'''		)

        # verify pixel values
        lowercase_       :Dict     =       torch.Size([1, 3, 800, 1066]		)
        self.assertEqual(encoding['''pixel_values'''].shape   ,							UpperCamelCase_		)

        lowercase_       :Union[str, Any]     =       torch.tensor([0.2796, 0.3138, 0.3481]		)
        self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,							UpperCamelCase_   ,							atol=1E-4		)		)

        # verify area
        lowercase_       :List[str]     =       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,							UpperCamelCase_		)		)
        # verify boxes
        lowercase_       :List[Any]     =       torch.Size([6, 4]		)
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,							UpperCamelCase_		)
        lowercase_       :str     =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,							UpperCamelCase_   ,							atol=1E-3		)		)
        # verify image_id
        lowercase_       :Optional[Any]     =       torch.tensor([3_9769]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,							UpperCamelCase_		)		)
        # verify is_crowd
        lowercase_       :Optional[int]     =       torch.tensor([0, 0, 0, 0, 0, 0]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,							UpperCamelCase_		)		)
        # verify class_labels
        lowercase_       :Union[str, Any]     =       torch.tensor([75, 75, 63, 65, 17, 17]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,							UpperCamelCase_		)		)
        # verify orig_size
        lowercase_       :Dict     =       torch.tensor([480, 640]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,							UpperCamelCase_		)		)
        # verify size
        lowercase_       :Tuple     =       torch.tensor([800, 1066]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,							UpperCamelCase_		)		)




   @slow
   def 		UpperCamelCase  (     self		):
        # prepare image, target and masks_path
        lowercase_       :int     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
        with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,							'''r'''		) as f:
             lowercase_       :List[Any]     =       json.loads(f.read()		)

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

        lowercase_       :Any     =       pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

        # encode them
        lowercase_       :Tuple     =       YolosImageProcessor(format='''coco_panoptic'''		)
        lowercase_       :Optional[Any]     =       image_processing(images=UpperCamelCase_   ,							annotations=UpperCamelCase_   ,							masks_path=UpperCamelCase_   ,							return_tensors='''pt'''		)

        # verify pixel values
        lowercase_       :Union[str, Any]     =       torch.Size([1, 3, 800, 1066]		)
        self.assertEqual(encoding['''pixel_values'''].shape   ,							UpperCamelCase_		)

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

        # verify area
        lowercase_       :Optional[Any]     =       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,							UpperCamelCase_		)		)
        # verify boxes
        lowercase_       :Tuple     =       torch.Size([6, 4]		)
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,							UpperCamelCase_		)
        lowercase_       :Dict     =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,							UpperCamelCase_   ,							atol=1E-3		)		)
        # verify image_id
        lowercase_       :Optional[Any]     =       torch.tensor([3_9769]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,							UpperCamelCase_		)		)
        # verify is_crowd
        lowercase_       :int     =       torch.tensor([0, 0, 0, 0, 0, 0]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,							UpperCamelCase_		)		)
        # verify class_labels
        lowercase_       :List[str]     =       torch.tensor([17, 17, 63, 75, 75, 93]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,							UpperCamelCase_		)		)
        # verify masks
        lowercase_       :Optional[int]     =       82_2873
        self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,							UpperCamelCase_		)
        # verify orig_size
        lowercase_       :Tuple     =       torch.tensor([480, 640]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,							UpperCamelCase_		)		)
        # verify size
        lowercase_       :Dict     =       torch.tensor([800, 1066]		)
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,							UpperCamelCase_		)		)





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE       :		int         =				299_792_458

# Symbols
SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		Optional[Any]         =				symbols("ct x y z")




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     if velocity > c:
          raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''		)
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError('''Speed must be greater than or equal to 1!'''		)

     return velocity / c




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return 1 / sqrt(1 - beta(_a		) ** 2		)




def     UpperCamelCase    (      _a		)					->							np.ndarray:
     '''simple docstring'''





     return np.array(
         [
             [gamma(_a		), -gamma(_a		) * beta(_a		), 0, 0],
             [-gamma(_a		) * beta(_a		), gamma(_a		), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]		)




def     UpperCamelCase    (      _a       ,		_a = None		)					->							np.ndarray:
     '''simple docstring'''





     if event is None:
          lowercase_       :Optional[int]     =       np.array([ct, x, y, z]		)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(_a		) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   SCREAMING_SNAKE_CASE       :		List[str]         =				transform(29_979_245)
   print("Example of four vector: ")
   print(f"ct' = {four_vector[0]}")
   print(f"x' = {four_vector[1]}")
   print(f"y' = {four_vector[2]}")
   print(f"z' = {four_vector[3]}")

   # Substitute symbols with numerical values
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				{ct: c, x: 1, y: 1, z: 1}
   SCREAMING_SNAKE_CASE       :		str         =				[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f"\n{numerical_vector}")

