



"""simple docstring"""







import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:

			class       _a  :



						@staticmethod
						def 				__lowercase					(			*_lowercase							:				Optional[int]   ,	**_lowercase							:				Any		)							->					Tuple:
												pass

@is_pipeline_test
@require_vision
@require_torch
class       _a  ( unittest.TestCase):
			__magic_name__     					=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				Dict   ,	_lowercase							:				Tuple   ,	_lowercase							:				int		)							->					List[str]:
									snake_case   :			Optional[Any]						    = pipeline(
									    "zero-shot-object-detection"   ,	model="hf-internal-testing/tiny-random-owlvit-object-detection"		)

									snake_case   :			str						    = [
									    {
									        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
									        "candidate_labels": ["cat", "remote", "couch"],
									    }
									]
									return object_detector, examples



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Optional[int]		)							->					Any:
									snake_case   :			Optional[Any]						    = object_detector(examples[0]   ,	threshold=0.0		)

									snake_case   :			List[str]						    = len(_lowercase		)
									self.assertGreater(_lowercase   ,	0		)
									self.assertEqual(
									    _lowercase   ,	[
									        {
									            "score": ANY(_lowercase		),
									            "label": ANY(_lowercase		),
									            "box": {"xmin": ANY(_lowercase		), "ymin": ANY(_lowercase		), "xmax": ANY(_lowercase		), "ymax": ANY(_lowercase		)},
									        }
									        for i in range(_lowercase		)
									    ]   ,	)



			@require_tf
			@unittest.skip("Zero Shot Object Detection not implemented in TF"		)
			def 				__lowercase					(			self							:				List[str]		)							->					Dict:
									pass



			@require_torch
			def 				__lowercase					(			self							:				Optional[int]		)							->					List[Any]:
									snake_case   :			List[Any]						    = pipeline(
									    "zero-shot-object-detection"   ,	model="hf-internal-testing/tiny-random-owlvit-object-detection"		)

									snake_case   :			List[str]						    = object_detector(
									    "./tests/fixtures/tests_samples/COCO/000000039769.png"   ,	candidate_labels=["cat", "remote", "couch"]   ,	threshold=0.64   ,	)

									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=4		)   ,	[
									        {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
									        {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
									        {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
									        {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
									        {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
									        {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
									        {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
									        {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
									        {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
									    ]   ,	)

									snake_case   :			Optional[Any]						    = object_detector(
									    [
									        {
									            "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
									            "candidate_labels": ["cat", "remote", "couch"],
									        }
									    ]   ,	threshold=0.64   ,	)

									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=4		)   ,	[
									        [
									            {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
									            {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
									            {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
									            {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
									            {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
									            {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
									            {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
									            {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
									            {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
									        ]
									    ]   ,	)



			@require_torch
			@slow
			def 				__lowercase					(			self							:				List[str]		)							->					Optional[Any]:
									snake_case   :			Optional[int]						    = pipeline("zero-shot-object-detection"		)

									snake_case   :			Union[str, Any]						    = object_detector(
									    "http://images.cocodataset.org/val2017/000000039769.jpg"   ,	candidate_labels=["cat", "remote", "couch"]   ,	)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=4		)   ,	[
									        {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
									        {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
									        {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
									        {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
									        {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
									    ]   ,	)

									snake_case   :			Optional[Any]						    = object_detector(
									    [
									        {
									            "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
									            "candidate_labels": ["cat", "remote", "couch"],
									        },
									        {
									            "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
									            "candidate_labels": ["cat", "remote", "couch"],
									        },
									    ]   ,	)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=4		)   ,	[
									        [
									            {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
									            {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
									            {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
									            {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
									            {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
									        ],
									        [
									            {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
									            {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
									            {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
									            {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
									            {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
									        ],
									    ]   ,	)



			@require_tf
			@unittest.skip("Zero Shot Object Detection not implemented in TF"		)
			def 				__lowercase					(			self							:				int		)							->					List[str]:
									pass



			@require_torch
			@slow
			def 				__lowercase					(			self							:				List[Any]		)							->					Union[str, Any]:
									snake_case   :			Tuple						    = 0.2
									snake_case   :			Any						    = pipeline("zero-shot-object-detection"		)

									snake_case   :			Optional[Any]						    = object_detector(
									    "http://images.cocodataset.org/val2017/000000039769.jpg"   ,	candidate_labels=["cat", "remote", "couch"]   ,	threshold=_lowercase   ,	)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=4		)   ,	[
									        {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
									        {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
									        {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
									    ]   ,	)



			@require_torch
			@slow
			def 				__lowercase					(			self							:				Dict		)							->					Dict:
									snake_case   :			Any						    = 2
									snake_case   :			Optional[Any]						    = pipeline("zero-shot-object-detection"		)

									snake_case   :			Any						    = object_detector(
									    "http://images.cocodataset.org/val2017/000000039769.jpg"   ,	candidate_labels=["cat", "remote", "couch"]   ,	top_k=_lowercase   ,	)
									self.assertEqual(
									    nested_simplify(_lowercase   ,	decimals=4		)   ,	[
									        {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
									        {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
									    ]   ,	)








"""simple docstring"""







import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


A		=						logging.get_logger(__name__)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = set()
						snake_case   :			Optional[int]						    = []

						def parse_line(lowerCamelCase_:							Union[str, Any] ):
												for line in fp:
																		if isinstance(lowerCamelCase_     ,		lowerCamelCase_ ):
																								snake_case   :			Optional[Any]						    = line.decode("UTF-8" )
																		if "warnings summary (final)" in line:
																								continue
																		# This means we are outside the body of a warning
																		elif not line.startswith(" " ):
																								# process a single warning and move it to `selected_warnings`.
																								if len(lowerCamelCase_ ) > 0:
																														snake_case   :			str						    = "\n".join(lowerCamelCase_ )
																														# Only keep the warnings specified in `targets`
																														if any(f''': {x}: ''' in warning for x in targets ):
																																				selected_warnings.add(lowerCamelCase_ )
																														buffer.clear()
																								continue
																		else:
																								snake_case   :			Dict						    = line.strip()
																								buffer.append(lowerCamelCase_ )

						if from_gh:
												for filename in os.listdir(lowerCamelCase_ ):
																		snake_case   :			Tuple						    = os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )
																		if not os.path.isdir(lowerCamelCase_ ):
																								# read the file
																								if filename != "warnings.txt":
																														continue
																								with open(lowerCamelCase_ ) as fp:
																														parse_line(lowerCamelCase_ )
						else:
												try:
																		with zipfile.ZipFile(lowerCamelCase_ ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(lowerCamelCase_ ):
																																				# read the file
																																				if filename != "warnings.txt":
																																										continue
																																				with z.open(lowerCamelCase_ ) as fp:
																																										parse_line(lowerCamelCase_ )
												except Exception:
																		logger.warning(
																		    f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.''' )

						return selected_warnings

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Dict						    = set()

						snake_case   :			str						    = [os.path.join(lowerCamelCase_     ,		lowerCamelCase_ ) for p in os.listdir(lowerCamelCase_ ) if (p.endswith(".zip" ) or from_gh)]
						for p in paths:
												selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase_     ,		lowerCamelCase_ ) )

						return selected_warnings


if __name__ == "__main__":

			def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
									"""simple docstring"""






									return values.split("," )

			A		=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
			parser.add_argument(
			    '--output_dir',
			    type=str,
			    required=True,
			    help='Where to store the downloaded artifacts and other result files.',
			)
			parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
			# optional parameters
			parser.add_argument(
			    '--targets',
			    default='DeprecationWarning,UserWarning,FutureWarning',
			    type=list_str,
			    help='Comma-separated list of target warning(s) which we want to extract.',
			)
			parser.add_argument(
			    '--from_gh',
			    action='store_true',
			    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
			)

			A		=						parser.parse_args()

			A		=						args.from_gh
			if from_gh:
						# The artifacts have to be downloaded using `actions/download-artifact@v3`
						pass
			else:
						os.makedirs(args.output_dir, exist_ok=True)

						# get download links
						A		=						get_artifacts_links(args.workflow_run_id, token=args.token)
						with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
									json.dump(artifacts, fp, ensure_ascii=False, indent=4)

						# download artifacts
						for idx, (name, url) in enumerate(artifacts.items()):
									print(name)
									print(url)
									print('=' * 8_0)
									download_artifact(name, url, args.output_dir, args.token)
									# Be gentle to GitHub
									time.sleep(1)

    # extract warnings from artifacts
			A		=						extract_warnings(args.output_dir, args.targets)
			A		=						sorted(selected_warnings)
			with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
						json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)



