


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 DetaImageProcessor





class 						__SCREAMING_SNAKE_CASE					(      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     ,							)       ->    Union[str, Any]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          _a						=		size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
          _a						=		parent
          _a						=		batch_size
          _a						=		num_channels
          _a						=		min_resolution
          _a						=		max_resolution
          _a						=		do_resize
          _a						=		size
          _a						=		do_normalize
          _a						=		image_mean
          _a						=		image_std
          _a						=		do_rescale
          _a						=		rescale_factor
          _a						=		do_pad






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

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

          return expected_height, expected_width







@require_torch
@require_vision
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				DetaImageProcessor if is_vision_available() else None






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






     @property
     def  a_					(			self		)       ->    Union[str, Any]:
          return self.image_processor_tester.prepare_image_processor_dict()






     def  a_					(			self		)       ->    Dict:
          _a						=		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     ,							"do_rescale"		)		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"do_pad"		)		)
          self.assertTrue(hasattr(__UpperCamelCase     ,							"size"		)		)






     def  a_					(			self		)       ->    Tuple:
          _a						=		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     ,							__UpperCamelCase		)






     def  a_					(			self		)       ->    int:
          pass






     def  a_					(			self		)       ->    Any:
          # Initialize image_processing
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          _a						=		prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,							Image.Image		)

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

          _a			,			_a						=		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
          _a			,			_a						=		self.image_processor_tester.get_expected_values(__UpperCamelCase     ,							batched=__UpperCamelCase		)

          _a						=		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  a_					(			self		)       ->    Optional[Any]:
          # Initialize image_processing
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          _a						=		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
          _a						=		image_processing(image_inputs[0]     ,							return_tensors="pt"		).pixel_values

          _a			,			_a						=		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
          _a						=		image_processing(__UpperCamelCase     ,							return_tensors="pt"		).pixel_values

          _a			,			_a						=		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  a_					(			self		)       ->    Any:
          # Initialize image_processing
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          _a						=		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
          _a						=		image_processing(image_inputs[0]     ,							return_tensors="pt"		).pixel_values

          _a			,			_a						=		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
          _a						=		image_processing(__UpperCamelCase     ,							return_tensors="pt"		).pixel_values

          _a			,			_a						=		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,
              )     ,							)






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

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

          # encode them
          _a						=		DetaImageProcessor()
          _a						=		image_processing(images=__UpperCamelCase     ,							annotations=__UpperCamelCase     ,							return_tensors="pt"		)

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

          _a						=		torch.tensor([0.27_96, 0.31_38, 0.34_81]		)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,							__UpperCamelCase     ,							atol=1e-4		)		)

          # verify area
          _a						=		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
          _a						=		torch.Size([6, 4]		)
          self.assertEqual(encoding["labels"][0]["boxes"].shape     ,							__UpperCamelCase		)
          _a						=		torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,							__UpperCamelCase     ,							atol=1e-3		)		)
          # verify image_id
          _a						=		torch.tensor([39_769]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,							__UpperCamelCase		)		)
          # verify is_crowd
          _a						=		torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,							__UpperCamelCase		)		)
          # verify class_labels
          _a						=		torch.tensor([75, 75, 63, 65, 17, 17]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,							__UpperCamelCase		)		)
          # verify orig_size
          _a						=		torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,							__UpperCamelCase		)		)
          # verify size
          _a						=		torch.tensor([800, 1_066]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,							__UpperCamelCase		)		)






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

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

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

          # encode them
          _a						=		DetaImageProcessor(format="coco_panoptic"		)
          _a						=		image_processing(images=__UpperCamelCase     ,							annotations=__UpperCamelCase     ,							masks_path=__UpperCamelCase     ,							return_tensors="pt"		)

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

          _a						=		torch.tensor([0.27_96, 0.31_38, 0.34_81]		)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,							__UpperCamelCase     ,							atol=1e-4		)		)

          # verify area
          _a						=		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
          _a						=		torch.Size([6, 4]		)
          self.assertEqual(encoding["labels"][0]["boxes"].shape     ,							__UpperCamelCase		)
          _a						=		torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,							__UpperCamelCase     ,							atol=1e-3		)		)
          # verify image_id
          _a						=		torch.tensor([39_769]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,							__UpperCamelCase		)		)
          # verify is_crowd
          _a						=		torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,							__UpperCamelCase		)		)
          # verify class_labels
          _a						=		torch.tensor([17, 17, 63, 75, 75, 93]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,							__UpperCamelCase		)		)
          # verify masks
          _a						=		822_873
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,							__UpperCamelCase		)
          # verify orig_size
          _a						=		torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,							__UpperCamelCase		)		)
          # verify size
          _a						=		torch.tensor([800, 1_066]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,							__UpperCamelCase		)		)





'''simple docstring'''

from __future__ import annotations

import requests






def __UpperCamelCase   (			__lowerCamelCase		: str				)      ->     dict:







     '''simple docstring'''






     _a						=		F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
     return requests.get(__lowerCamelCase				).json()






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     list[dict]:







     '''simple docstring'''






     _a						=		"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
     _a						=		requests.get(__lowerCamelCase				).json()[:max_stories]
     return [get_hackernews_story(__lowerCamelCase				) for story_id in story_ids]






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     str:







     '''simple docstring'''






     _a						=		hackernews_top_stories(__lowerCamelCase				)
     return "\n".join("* [{title}]({url})".format(**__lowerCamelCase				) for story in stories				)


if __name__ == "__main__":
   print(hackernews_top_stories_as_markdown())

