


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     lowercase__  (					unittest.TestCase		):






  def __init__(					self      :					Union[str, Any]		,						_lowercase      :					Dict		,						_lowercase      :					Optional[Any]=7		,						_lowercase      :					List[Any]=3		,						_lowercase      :					List[str]=30		,						_lowercase      :					Optional[Any]=4_00		,						_lowercase      :					Dict=True		,						_lowercase      :					int=None		,						_lowercase      :					Union[str, Any]=True		,						_lowercase      :					List[str]=[0.5, 0.5, 0.5]		,						_lowercase      :					Optional[Any]=[0.5, 0.5, 0.5]		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					str=1 / 2_55		,						_lowercase      :					Dict=True		,						):




         """simple docstring"""







         UpperCAmelCase__										=     size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
         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 			_UpperCAmelCase  (					self      :					List[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 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					List[Any]		,						_lowercase      :					Dict=False ):




         """simple docstring"""







         if not batched:
                UpperCAmelCase__										=     image_inputs[0]
                if isinstance(_lowercase		,						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(_lowercase		,						key=lambda _lowercase : item[0] )[0]
                UpperCAmelCase__										=     max(_lowercase		,						key=lambda _lowercase : item[1] )[1]

         return expected_height, expected_width




@require_torch
@require_vision
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  YolosImageProcessor if is_vision_available() else None






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     YolosImageProcessingTester(self )






  @property
  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(_lowercase		,						"image_mean" ) )
         self.assertTrue(hasattr(_lowercase		,						"image_std" ) )
         self.assertTrue(hasattr(_lowercase		,						"do_normalize" ) )
         self.assertTrue(hasattr(_lowercase		,						"do_resize" ) )
         self.assertTrue(hasattr(_lowercase		,						"size" ) )






  def 			_UpperCAmelCase  (					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": 13_33} )
         self.assertEqual(image_processor.do_pad		,						_lowercase )

         UpperCAmelCase__										=     self.image_processing_class.from_dict(
             self.image_processor_dict		,						size=42		,						max_size=84		,						pad_and_return_pixel_mask=_lowercase )
         self.assertEqual(image_processor.size		,						{"shortest_edge": 42, "longest_edge": 84} )
         self.assertEqual(image_processor.do_pad		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         pass






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """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=_lowercase )
         for image in image_inputs:
                self.assertIsInstance(_lowercase		,						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(_lowercase )

         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(_lowercase		,						batched=_lowercase )

         UpperCAmelCase__										=     image_processing(_lowercase		,						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      :					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=_lowercase		,						numpify=_lowercase )
         for image in image_inputs:
                self.assertIsInstance(_lowercase		,						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(_lowercase )

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

         # Test batched
         UpperCAmelCase__										=     image_processing(_lowercase		,						return_tensors="pt" ).pixel_values

         UpperCAmelCase__						,			UpperCAmelCase__										=     self.image_processor_tester.get_expected_values(_lowercase		,						batched=_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """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=_lowercase		,						torchify=_lowercase )
         for image in image_inputs:
                self.assertIsInstance(_lowercase		,						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(_lowercase )

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

         # Test batched
         UpperCAmelCase__										=     image_processing(_lowercase		,						return_tensors="pt" ).pixel_values

         UpperCAmelCase__						,			UpperCAmelCase__										=     self.image_processor_tester.get_expected_values(_lowercase		,						batched=_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







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

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

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






  @slow
  def 			_UpperCAmelCase  (					self      :					List[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_97_69, "annotations": target}

         # encode them
         UpperCAmelCase__										=     YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
         UpperCAmelCase__										=     image_processing(images=_lowercase		,						annotations=_lowercase		,						return_tensors="pt" )

         # verify pixel values
         UpperCAmelCase__										=     torch.Size([1, 3, 8_00, 10_66] )
         self.assertEqual(encoding["pixel_values"].shape		,						_lowercase )

         UpperCAmelCase__										=     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,						_lowercase		,						atol=1E-4 ) )

         # verify area
         UpperCAmelCase__										=     torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,						_lowercase ) )
         # verify boxes
         UpperCAmelCase__										=     torch.Size([6, 4] )
         self.assertEqual(encoding["labels"][0]["boxes"].shape		,						_lowercase )
         UpperCAmelCase__										=     torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,						_lowercase		,						atol=1E-3 ) )
         # verify image_id
         UpperCAmelCase__										=     torch.tensor([3_97_69] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,						_lowercase ) )
         # verify is_crowd
         UpperCAmelCase__										=     torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,						_lowercase ) )
         # verify class_labels
         UpperCAmelCase__										=     torch.tensor([75, 75, 63, 65, 17, 17] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,						_lowercase ) )
         # verify orig_size
         UpperCAmelCase__										=     torch.tensor([4_80, 6_40] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,						_lowercase ) )
         # verify size
         UpperCAmelCase__										=     torch.tensor([8_00, 10_66] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,						_lowercase ) )






  @slow
  def 			_UpperCAmelCase  (					self      :					Optional[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_97_69, "segments_info": target}

         UpperCAmelCase__										=     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

         # encode them
         UpperCAmelCase__										=     YolosImageProcessor(format="coco_panoptic" )
         UpperCAmelCase__										=     image_processing(images=_lowercase		,						annotations=_lowercase		,						masks_path=_lowercase		,						return_tensors="pt" )

         # verify pixel values
         UpperCAmelCase__										=     torch.Size([1, 3, 8_00, 10_66] )
         self.assertEqual(encoding["pixel_values"].shape		,						_lowercase )

         UpperCAmelCase__										=     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,						_lowercase		,						atol=1E-4 ) )

         # verify area
         UpperCAmelCase__										=     torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,						_lowercase ) )
         # verify boxes
         UpperCAmelCase__										=     torch.Size([6, 4] )
         self.assertEqual(encoding["labels"][0]["boxes"].shape		,						_lowercase )
         UpperCAmelCase__										=     torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,						_lowercase		,						atol=1E-3 ) )
         # verify image_id
         UpperCAmelCase__										=     torch.tensor([3_97_69] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,						_lowercase ) )
         # verify is_crowd
         UpperCAmelCase__										=     torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,						_lowercase ) )
         # verify class_labels
         UpperCAmelCase__										=     torch.tensor([17, 17, 63, 75, 75, 93] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,						_lowercase ) )
         # verify masks
         UpperCAmelCase__										=     82_28_73
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,						_lowercase )
         # verify orig_size
         UpperCAmelCase__										=     torch.tensor([4_80, 6_40] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,						_lowercase ) )
         # verify size
         UpperCAmelCase__										=     torch.tensor([8_00, 10_66] )
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,						_lowercase ) )









from __future__ import annotations

import math





def __UpperCAmelCase       (		__A ,						__A			)						->					float:



       '''simple docstring'''





       UpperCAmelCase__										=     u
       for i in range(1 ,						__A			):
              UpperCAmelCase__										=     temp * (u - i)
       return temp





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     int(input("enter the numbers of values: "			)			)
       UpperCAmelCase__										=     []
       for _ in range(__A			):
              y.append([]			)
       for i in range(__A			):
              for j in range(__A			):
                     y[i].append(__A			)
                     UpperCAmelCase__										=     0

       print("enter the values of parameters in a list: "			)
       UpperCAmelCase__										=     list(map(__A ,						input().split()			)			)

       print("enter the values of corresponding parameters: "			)
       for i in range(__A			):
              UpperCAmelCase__										=     float(input()			)

       UpperCAmelCase__										=     int(input("enter the value to interpolate: "			)			)
       UpperCAmelCase__										=     (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1 ,						__A			):
              for j in range(n - i			):
                     UpperCAmelCase__										=     y[j + 1][i - 1] - y[j][i - 1]

       UpperCAmelCase__										=     y[0][0]
       for i in range(1 ,						__A			):
              summ += (ucal(__A ,						__A			) * y[0][i]) / math.factorial(__A			)

       print(F"""the value at {value} is {summ}"""			)


if __name__ == "__main__":
    main()





