





"""simple docstring"""


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 DetrImageProcessor



class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







		'''simple docstring'''


		def __init__(		self   :       Dict					,   __a   :       List[Any]					,   __a   :       List[str]=7					,   __a   :       int=3					,   __a   :       List[str]=30					,   __a   :       Optional[int]=400					,   __a   :       str=True					,   __a   :       Union[str, Any]=None					,   __a   :       int=True					,   __a   :       int=1 / 255					,   __a   :       Tuple=True					,   __a   :       Optional[int]=[0.5, 0.5, 0.5]					,   __a   :       Any=[0.5, 0.5, 0.5]					,   __a   :       int=True					,   )		->				List[str]:
								_UpperCamelCase      :	int						=       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
								_UpperCamelCase      :	Any						=       parent
								_UpperCamelCase      :	Dict						=       batch_size
								_UpperCamelCase      :	Optional[int]						=       num_channels
								_UpperCamelCase      :	List[str]						=       min_resolution
								_UpperCamelCase      :	List[str]						=       max_resolution
								_UpperCamelCase      :	Dict						=       do_resize
								_UpperCamelCase      :	Union[str, Any]						=       size
								_UpperCamelCase      :	Optional[Any]						=       do_rescale
								_UpperCamelCase      :	str						=       rescale_factor
								_UpperCamelCase      :	List[str]						=       do_normalize
								_UpperCamelCase      :	str						=       image_mean
								_UpperCamelCase      :	Optional[int]						=       image_std
								_UpperCamelCase      :	Tuple						=       do_pad

		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Optional[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_pad": self.do_pad,
								}


		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       List[Any]					,   __a   :       Any=False	)		->				Dict:






								if not batched:
														_UpperCamelCase      :	Tuple						=       image_inputs[0]
														if isinstance(lowerCamelCase__					,   Image.Image	):
																				_UpperCamelCase      :	List[str]						=       image.size
														else:
																				_UpperCamelCase      :	int						=       image.shape[1], image.shape[2]
														if w < h:
																				_UpperCamelCase      :	int						=       int(self.size["shortest_edge"] * h / w	)
																				_UpperCamelCase      :	Union[str, Any]						=       self.size['''shortest_edge''']
														elif w > h:
																				_UpperCamelCase      :	Optional[Any]						=       self.size['''shortest_edge''']
																				_UpperCamelCase      :	str						=       int(self.size["shortest_edge"] * w / h	)
														else:
																				_UpperCamelCase      :	List[str]						=       self.size['''shortest_edge''']
																				_UpperCamelCase      :	Optional[Any]						=       self.size['''shortest_edge''']

								else:
														_UpperCamelCase      :	Any						=       []
														for image in image_inputs:
																				_UpperCamelCase      :	Union[str, Any]						=       self.get_expected_values([image]	)
																				expected_values.append((expected_height, expected_width)	)
														_UpperCamelCase      :	int						=       max(lowerCamelCase__					,   key=lambda __a	: item[0]	)[0]
														_UpperCamelCase      :	Tuple						=       max(lowerCamelCase__					,   key=lambda __a	: item[1]	)[1]

								return expected_height, expected_width





@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE    (						__lowerCAmelCase		,  unittest.TestCase   ):







		'''simple docstring'''


		SCREAMING_SNAKE_CASE__							:Tuple	     =					DetrImageProcessor if is_vision_available() else None

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Optional[int]:
								_UpperCamelCase      :	Dict						=       DetrImageProcessingTester(self	)

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

		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[int]:
								_UpperCamelCase      :	Any						=       self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(lowerCamelCase__					,   "image_mean"	)	)
								self.assertTrue(hasattr(lowerCamelCase__					,   "image_std"	)	)
								self.assertTrue(hasattr(lowerCamelCase__					,   "do_normalize"	)	)
								self.assertTrue(hasattr(lowerCamelCase__					,   "do_rescale"	)	)
								self.assertTrue(hasattr(lowerCamelCase__					,   "rescale_factor"	)	)
								self.assertTrue(hasattr(lowerCamelCase__					,   "do_resize"	)	)
								self.assertTrue(hasattr(lowerCamelCase__					,   "size"	)	)
								self.assertTrue(hasattr(lowerCamelCase__					,   "do_pad"	)	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Any:
								_UpperCamelCase      :	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					,   lowerCamelCase__	)

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

		def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				str:
								pass

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Optional[Any]:
								_UpperCamelCase      :	int						=       self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								_UpperCamelCase      :	Union[str, Any]						=       prepare_image_inputs(self.image_processor_tester					,   equal_resolution=lowerCamelCase__	)
								for image in image_inputs:
														self.assertIsInstance(lowerCamelCase__					,   Image.Image	)

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

								_UpperCamelCase      :	int						=       self.image_processor_tester.get_expected_values(lowerCamelCase__	)

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

								# Test batched
								_UpperCamelCase      :	str						=       self.image_processor_tester.get_expected_values(lowerCamelCase__					,   batched=lowerCamelCase__	)

								_UpperCamelCase      :	Dict						=       image_processing(lowerCamelCase__					,   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    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Optional[Any]:
								_UpperCamelCase      :	Tuple						=       self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								_UpperCamelCase      :	Optional[Any]						=       prepare_image_inputs(self.image_processor_tester					,   equal_resolution=lowerCamelCase__					,   numpify=lowerCamelCase__	)
								for image in image_inputs:
														self.assertIsInstance(lowerCamelCase__					,   np.ndarray	)

								# Test not batched input
								_UpperCamelCase      :	Optional[Any]						=       image_processing(image_inputs[0]					,   return_tensors="pt"	).pixel_values

								_UpperCamelCase      :	List[Any]						=       self.image_processor_tester.get_expected_values(lowerCamelCase__	)

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

								# Test batched
								_UpperCamelCase      :	Optional[Any]						=       image_processing(lowerCamelCase__					,   return_tensors="pt"	).pixel_values

								_UpperCamelCase      :	Dict						=       self.image_processor_tester.get_expected_values(lowerCamelCase__					,   batched=lowerCamelCase__	)

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

		def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				int:
								_UpperCamelCase      :	str						=       self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								_UpperCamelCase      :	Any						=       prepare_image_inputs(self.image_processor_tester					,   equal_resolution=lowerCamelCase__					,   torchify=lowerCamelCase__	)
								for image in image_inputs:
														self.assertIsInstance(lowerCamelCase__					,   torch.Tensor	)

								# Test not batched input
								_UpperCamelCase      :	Optional[Any]						=       image_processing(image_inputs[0]					,   return_tensors="pt"	).pixel_values

								_UpperCamelCase      :	Optional[Any]						=       self.image_processor_tester.get_expected_values(lowerCamelCase__	)

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

								# Test batched
								_UpperCamelCase      :	str						=       image_processing(lowerCamelCase__					,   return_tensors="pt"	).pixel_values

								_UpperCamelCase      :	List[str]						=       self.image_processor_tester.get_expected_values(lowerCamelCase__					,   batched=lowerCamelCase__	)

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

		@slow
		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[Any]:
								_UpperCamelCase      :	Dict						=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
								with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"					,   "r"	) as f:
														_UpperCamelCase      :	Any						=       json.loads(f.read()	)

								_UpperCamelCase      :	Union[str, Any]						=       {'''image_id''': 3_9769, '''annotations''': target}

								# encode them
								_UpperCamelCase      :	Optional[Any]						=       DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"	)
								_UpperCamelCase      :	int						=       image_processing(images=lowerCamelCase__					,   annotations=lowerCamelCase__					,   return_tensors="pt"	)

								# verify pixel values
								_UpperCamelCase      :	List[str]						=       torch.Size([1, 3, 800, 1066]	)
								self.assertEqual(encoding["pixel_values"].shape					,   lowerCamelCase__	)

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

								# verify area
								_UpperCamelCase      :	Dict						=       torch.tensor([58_87.96_00, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,   lowerCamelCase__	)	)
								# verify boxes
								_UpperCamelCase      :	Optional[Any]						=       torch.Size([6, 4]	)
								self.assertEqual(encoding["labels"][0]["boxes"].shape					,   lowerCamelCase__	)
								_UpperCamelCase      :	List[Any]						=       torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,   lowerCamelCase__					,   atol=1e-3	)	)
								# verify image_id
								_UpperCamelCase      :	Union[str, Any]						=       torch.tensor([3_9769]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,   lowerCamelCase__	)	)
								# verify is_crowd
								_UpperCamelCase      :	str						=       torch.tensor([0, 0, 0, 0, 0, 0]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,   lowerCamelCase__	)	)
								# verify class_labels
								_UpperCamelCase      :	str						=       torch.tensor([75, 75, 63, 65, 17, 17]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,   lowerCamelCase__	)	)
								# verify orig_size
								_UpperCamelCase      :	List[str]						=       torch.tensor([480, 640]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,   lowerCamelCase__	)	)
								# verify size
								_UpperCamelCase      :	List[Any]						=       torch.tensor([800, 1066]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,   lowerCamelCase__	)	)


		@slow
		def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Tuple:
								_UpperCamelCase      :	Union[str, Any]						=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
								with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"					,   "r"	) as f:
														_UpperCamelCase      :	Tuple						=       json.loads(f.read()	)

								_UpperCamelCase      :	Optional[Any]						=       {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

								_UpperCamelCase      :	Tuple						=       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

								# encode them
								_UpperCamelCase      :	Optional[Any]						=       DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"	)
								_UpperCamelCase      :	Dict						=       image_processing(images=lowerCamelCase__					,   annotations=lowerCamelCase__					,   masks_path=lowerCamelCase__					,   return_tensors="pt"	)

								# verify pixel values
								_UpperCamelCase      :	Tuple						=       torch.Size([1, 3, 800, 1066]	)
								self.assertEqual(encoding["pixel_values"].shape					,   lowerCamelCase__	)

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

								# verify area
								_UpperCamelCase      :	Optional[Any]						=       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 58_79.65_62, 76_34.11_47]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,   lowerCamelCase__	)	)
								# verify boxes
								_UpperCamelCase      :	List[Any]						=       torch.Size([6, 4]	)
								self.assertEqual(encoding["labels"][0]["boxes"].shape					,   lowerCamelCase__	)
								_UpperCamelCase      :	Union[str, Any]						=       torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,   lowerCamelCase__					,   atol=1e-3	)	)
								# verify image_id
								_UpperCamelCase      :	Union[str, Any]						=       torch.tensor([3_9769]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,   lowerCamelCase__	)	)
								# verify is_crowd
								_UpperCamelCase      :	Optional[Any]						=       torch.tensor([0, 0, 0, 0, 0, 0]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,   lowerCamelCase__	)	)
								# verify class_labels
								_UpperCamelCase      :	List[str]						=       torch.tensor([17, 17, 63, 75, 75, 93]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,   lowerCamelCase__	)	)
								# verify masks
								_UpperCamelCase      :	Optional[int]						=       82_2873
								self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,   lowerCamelCase__	)
								# verify orig_size
								_UpperCamelCase      :	int						=       torch.tensor([480, 640]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,   lowerCamelCase__	)	)
								# verify size
								_UpperCamelCase      :	List[Any]						=       torch.tensor([800, 1066]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,   lowerCamelCase__	)	)







"""simple docstring"""


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links




def 					lowercase__    (					lowercase_		,lowercase_=7			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       None
      if token is not None:
            _UpperCamelCase      :	Optional[Any]						=       {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

      # The id of a workflow (not of a workflow run)
      _UpperCamelCase      :	Any						=       "636036"

      _UpperCamelCase      :	Tuple						=       F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
      # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
      url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''

      _UpperCamelCase      :	Dict						=       requests.get(lowercase_		,headers=lowercase_			).json()

      return result["workflow_runs"]




def 					lowercase__    (					lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       get_daily_ci_runs(lowercase_			)
      _UpperCamelCase      :	Tuple						=       None
      for workflow_run in workflow_runs:
            if workflow_run["status"] == "completed":
                  _UpperCamelCase      :	Union[str, Any]						=       workflow_run["id"]
                  break

      return workflow_run_id




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       get_last_daily_ci_runs(lowercase_			)
      if workflow_run_id is not None:
            _UpperCamelCase      :	int						=       get_artifacts_links(worflow_run_id=lowercase_		,token=lowercase_			)
            for artifact_name in artifact_names:
                  if artifact_name in artifacts_links:
                        _UpperCamelCase      :	Dict						=       artifacts_links[artifact_name]
                        download_artifact(
                            artifact_name=lowercase_		,artifact_url=lowercase_		,output_dir=lowercase_		,token=lowercase_			)




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		int:




      """simple docstring"""

      get_last_daily_ci_artifacts(lowercase_		,lowercase_		,lowercase_			)

      _UpperCamelCase      :	Dict						=       {}
      for artifact_name in artifact_names:
            _UpperCamelCase      :	Union[str, Any]						=       os.path.join(lowercase_		,F'''{artifact_name}.zip'''			)
            if os.path.isfile(lowercase_			):
                  _UpperCamelCase      :	int						=       {}
                  with zipfile.ZipFile(lowercase_			) as z:
                        for filename in z.namelist():
                              if not os.path.isdir(lowercase_			):
                                    # read the file
                                    with z.open(lowercase_			) as f:
                                          _UpperCamelCase      :	int						=       f.read().decode("UTF-8"			)

      return results
