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    ):
  """simple docstring"""







  def __init__(							self       :				str     ,   __magic_name__       :				Any     ,   __magic_name__       :				Tuple=7     ,   __magic_name__       :				List[Any]=3     ,   __magic_name__       :				Union[str, Any]=30     ,   __magic_name__       :				str=400     ,   __magic_name__       :				Optional[Any]=True     ,   __magic_name__       :				Dict=None     ,   __magic_name__       :				int=True     ,   __magic_name__       :				List[str]=[0.5, 0.5, 0.5]     ,   __magic_name__       :				Optional[int]=[0.5, 0.5, 0.5]     ,   __magic_name__       :				str=True     ,   __magic_name__       :				Optional[int]=1 / 255     ,   __magic_name__       :				Union[str, Any]=True     ,   )     ->		Dict:
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        SCREAMING_SNAKE_CASE_      =     size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
        SCREAMING_SNAKE_CASE_      =     parent
        SCREAMING_SNAKE_CASE_      =     batch_size
        SCREAMING_SNAKE_CASE_      =     num_channels
        SCREAMING_SNAKE_CASE_      =     min_resolution
        SCREAMING_SNAKE_CASE_      =     max_resolution
        SCREAMING_SNAKE_CASE_      =     do_resize
        SCREAMING_SNAKE_CASE_      =     size
        SCREAMING_SNAKE_CASE_      =     do_normalize
        SCREAMING_SNAKE_CASE_      =     image_mean
        SCREAMING_SNAKE_CASE_      =     image_std
        SCREAMING_SNAKE_CASE_      =     do_rescale
        SCREAMING_SNAKE_CASE_      =     rescale_factor
        SCREAMING_SNAKE_CASE_      =     do_pad







  def       __A   (							self       :				str      )     ->		Optional[Any]:
        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       :				Optional[int]     ,   __magic_name__       :				str     ,   __magic_name__       :				Union[str, Any]=False      )     ->		List[str]:
        if not batched:
              SCREAMING_SNAKE_CASE_      =     image_inputs[0]
              if isinstance(__magic_name__     ,   Image.Image      ):
                    SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.size
              else:
                    SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[1], image.shape[2]
              if w < h:
                    SCREAMING_SNAKE_CASE_      =     int(self.size["shortest_edge"] * h / w      )
                    SCREAMING_SNAKE_CASE_      =     self.size["shortest_edge"]
              elif w > h:
                    SCREAMING_SNAKE_CASE_      =     self.size["shortest_edge"]
                    SCREAMING_SNAKE_CASE_      =     int(self.size["shortest_edge"] * w / h      )
              else:
                    SCREAMING_SNAKE_CASE_      =     self.size["shortest_edge"]
                    SCREAMING_SNAKE_CASE_      =     self.size["shortest_edge"]

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

        return expected_height, expected_width



@require_torch
@require_vision
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  YolosImageProcessor if is_vision_available() else None







  def       __A   (							self       :				List[str]      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     YolosImageProcessingTester(self      )







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		List[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()







  def       __A   (							self       :				Optional[Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(__magic_name__     ,   "image_mean"      )      )
        self.assertTrue(hasattr(__magic_name__     ,   "image_std"      )      )
        self.assertTrue(hasattr(__magic_name__     ,   "do_normalize"      )      )
        self.assertTrue(hasattr(__magic_name__     ,   "do_resize"      )      )
        self.assertTrue(hasattr(__magic_name__     ,   "size"      )      )







  def       __A   (							self       :				List[str]      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size     ,   {"shortest_edge": 18, "longest_edge": 1_333}      )
        self.assertEqual(image_processor.do_pad     ,   __magic_name__      )

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







  def       __A   (							self       :				List[str]      )     ->		Optional[int]:
        pass







  def       __A   (							self       :				Union[str, Any]      )     ->		Any:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(**self.image_processor_dict      )
        # create random PIL images
        SCREAMING_SNAKE_CASE_      =     prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__magic_name__      )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__     ,   Image.Image      )

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

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

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

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

        SCREAMING_SNAKE_CASE_      =     image_processing(__magic_name__     ,   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]      )     ->		Optional[int]:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(**self.image_processor_dict      )
        # create random numpy tensors
        SCREAMING_SNAKE_CASE_      =     prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__magic_name__     ,   numpify=__magic_name__      )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__     ,   np.ndarray      )

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

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

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

        # Test batched
        SCREAMING_SNAKE_CASE_      =     image_processing(__magic_name__     ,   return_tensors="pt"      ).pixel_values

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

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







  def       __A   (							self       :				Optional[Any]      )     ->		List[Any]:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_      =     prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__magic_name__     ,   torchify=__magic_name__      )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__     ,   torch.Tensor      )

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

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

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

        # Test batched
        SCREAMING_SNAKE_CASE_      =     image_processing(__magic_name__     ,   return_tensors="pt"      ).pixel_values

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

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







  def       __A   (							self       :				Tuple      )     ->		List[Any]:
        # Initialize image_processings
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(**self.image_processor_dict      )
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(do_resize=__magic_name__     ,   do_normalize=__magic_name__     ,   do_rescale=__magic_name__      )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_      =     prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__magic_name__     ,   torchify=__magic_name__      )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__     ,   torch.Tensor      )

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

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







  @slow
  def       __A   (							self       :				Any      )     ->		Any:
        # prepare image and target
        SCREAMING_SNAKE_CASE_      =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,   "r"      ) as f:
              SCREAMING_SNAKE_CASE_      =     json.loads(f.read()      )

        SCREAMING_SNAKE_CASE_      =     {"image_id": 39_769, "annotations": target}

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

        # verify pixel values
        SCREAMING_SNAKE_CASE_      =     torch.Size([1, 3, 800, 1_066]      )
        self.assertEqual(encoding["pixel_values"].shape     ,   __magic_name__      )

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

        # verify area
        SCREAMING_SNAKE_CASE_      =     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"]     ,   __magic_name__      )      )
        # verify boxes
        SCREAMING_SNAKE_CASE_      =     torch.Size([6, 4]      )
        self.assertEqual(encoding["labels"][0]["boxes"].shape     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,   __magic_name__     ,   atol=1e-3      )      )
        # verify image_id
        SCREAMING_SNAKE_CASE_      =     torch.tensor([39_769]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,   __magic_name__      )      )
        # verify is_crowd
        SCREAMING_SNAKE_CASE_      =     torch.tensor([0, 0, 0, 0, 0, 0]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,   __magic_name__      )      )
        # verify class_labels
        SCREAMING_SNAKE_CASE_      =     torch.tensor([75, 75, 63, 65, 17, 17]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,   __magic_name__      )      )
        # verify orig_size
        SCREAMING_SNAKE_CASE_      =     torch.tensor([480, 640]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,   __magic_name__      )      )
        # verify size
        SCREAMING_SNAKE_CASE_      =     torch.tensor([800, 1_066]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,   __magic_name__      )      )







  @slow
  def       __A   (							self       :				Any      )     ->		int:
        # prepare image, target and masks_path
        SCREAMING_SNAKE_CASE_      =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"     ,   "r"      ) as f:
              SCREAMING_SNAKE_CASE_      =     json.loads(f.read()      )

        SCREAMING_SNAKE_CASE_      =     {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

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

        # verify pixel values
        SCREAMING_SNAKE_CASE_      =     torch.Size([1, 3, 800, 1_066]      )
        self.assertEqual(encoding["pixel_values"].shape     ,   __magic_name__      )

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

        # verify area
        SCREAMING_SNAKE_CASE_      =     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"]     ,   __magic_name__      )      )
        # verify boxes
        SCREAMING_SNAKE_CASE_      =     torch.Size([6, 4]      )
        self.assertEqual(encoding["labels"][0]["boxes"].shape     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,   __magic_name__     ,   atol=1e-3      )      )
        # verify image_id
        SCREAMING_SNAKE_CASE_      =     torch.tensor([39_769]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,   __magic_name__      )      )
        # verify is_crowd
        SCREAMING_SNAKE_CASE_      =     torch.tensor([0, 0, 0, 0, 0, 0]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,   __magic_name__      )      )
        # verify class_labels
        SCREAMING_SNAKE_CASE_      =     torch.tensor([17, 17, 63, 75, 75, 93]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,   __magic_name__      )      )
        # verify masks
        SCREAMING_SNAKE_CASE_      =     822_873
        self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,   __magic_name__      )
        # verify orig_size
        SCREAMING_SNAKE_CASE_      =     torch.tensor([480, 640]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,   __magic_name__      )      )
        # verify size
        SCREAMING_SNAKE_CASE_      =     torch.tensor([800, 1_066]      )
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,   __magic_name__      )      )



from functools import lru_cache


@lru_cache
def    a__       (						__UpperCamelCase ):
      if num < 0:
            raise ValueError("Number should not be negative." )

      return 1 if num in (0, 1) else num * factorial(num - 1 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()


