import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							Tuple=7  ):
					SCREAMING_SNAKE_CASE    			=       None
					if token is not None:
										SCREAMING_SNAKE_CASE    			=       {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

					# The id of a workflow (not of a workflow run)
					SCREAMING_SNAKE_CASE    			=       "636036"

					SCREAMING_SNAKE_CASE    			=       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}"

					SCREAMING_SNAKE_CASE    			=       requests.get(UpperCAmelCase__      , headers=UpperCAmelCase__  ).json()

					return result["workflow_runs"]





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[int]  ):
					SCREAMING_SNAKE_CASE    			=       get_daily_ci_runs(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       None
					for workflow_run in workflow_runs:
										if workflow_run["status"] == "completed":
															SCREAMING_SNAKE_CASE    			=       workflow_run["id"]
															break

					return workflow_run_id





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Any  ):
					SCREAMING_SNAKE_CASE    			=       get_last_daily_ci_runs(UpperCAmelCase__  )
					if workflow_run_id is not None:
										SCREAMING_SNAKE_CASE    			=       get_artifacts_links(worflow_run_id=UpperCAmelCase__      , token=UpperCAmelCase__  )
										for artifact_name in artifact_names:
															if artifact_name in artifacts_links:
																				SCREAMING_SNAKE_CASE    			=       artifacts_links[artifact_name]
																				download_artifact(
																				    artifact_name=UpperCAmelCase__      , artifact_url=UpperCAmelCase__      , output_dir=UpperCAmelCase__      , token=UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Dict  ):
					get_last_daily_ci_artifacts(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       {}
					for artifact_name in artifact_names:
										SCREAMING_SNAKE_CASE    			=       os.path.join(UpperCAmelCase__      , F"{artifact_name}.zip"  )
										if os.path.isfile(UpperCAmelCase__  ):
															SCREAMING_SNAKE_CASE    			=       {}
															with zipfile.ZipFile(UpperCAmelCase__  ) as z:
																				for filename in z.namelist():
																									if not os.path.isdir(UpperCAmelCase__  ):
																														# read the file
																														with z.open(UpperCAmelCase__  ) as f:
																																			SCREAMING_SNAKE_CASE    			=       f.read().decode("UTF-8"  )

					return results



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 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Dict=7	,							_UpperCamelCase   :					Union[str, Any]=3	,							_UpperCamelCase   :					Optional[int]=30	,							_UpperCamelCase   :					List[Any]=400	,							_UpperCamelCase   :					Dict=True	,							_UpperCamelCase   :					Union[str, Any]=None	,							_UpperCamelCase   :					Any=True	,							_UpperCamelCase   :					List[Any]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=True	,							_UpperCamelCase   :					List[Any]=1 / 255	,							_UpperCamelCase   :					Optional[Any]=True	,							)					->				List[str]:



												'''simple docstring'''

												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 	__snake_case(						self   :					List[str]      )					->				Union[str, 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 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					List[Any]=False      )					->				List[Any]:



												'''simple docstring'''

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

												return expected_height, expected_width



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Optional[int]								=							DetaImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[str]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DetaImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					int      )					->				Tuple:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												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	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					str      )					->				List[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

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

												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(_UpperCamelCase	,							batched=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					List[str]      )					->				Optional[int]:



												'''simple docstring'''

												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=_UpperCamelCase	,							numpify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							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(_UpperCamelCase      )

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

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					str      )					->				Dict:



												'''simple docstring'''

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

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

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Dict      )					->				str:



												'''simple docstring'''

												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    			=       DetaImageProcessor()
												SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							return_tensors="pt"      )

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

												SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-4      )      )

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





							@slow
							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												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    			=       DetaImageProcessor(format="coco_panoptic"      )
												SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							masks_path=_UpperCamelCase	,							return_tensors="pt"      )

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

												SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-4      )      )

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




