




'''simple docstring'''


import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
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 BeitImageProcessor

class       A   (		unittest.TestCase   ):







							def __init__(  self	:  List[Any]							,      lowerCAmelCase_	:  Optional[int]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Tuple=3							,      lowerCAmelCase_	:  Optional[int]=18							,      lowerCAmelCase_	:  Any=30							,      lowerCAmelCase_	:  Tuple=4_00							,      lowerCAmelCase_	:  Tuple=True							,      lowerCAmelCase_	:  List[str]=None							,      lowerCAmelCase_	:  Optional[Any]=True							,      lowerCAmelCase_	:  Tuple=None							,      lowerCAmelCase_	:  Tuple=True							,      lowerCAmelCase_	:  List[Any]=[0.5, 0.5, 0.5]							,      lowerCAmelCase_	:  List[Any]=[0.5, 0.5, 0.5]							,      lowerCAmelCase_	:  List[str]=False							,      )      ->							Tuple:


														"""simple docstring"""


														_a     					=    size if size is not None else {'''height''': 20, '''width''': 20}
														_a     					=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
														_a     					=    parent
														_a     					=    batch_size
														_a     					=    num_channels
														_a     					=    image_size
														_a     					=    min_resolution
														_a     					=    max_resolution
														_a     					=    do_resize
														_a     					=    size
														_a     					=    do_center_crop
														_a     					=    crop_size
														_a     					=    do_normalize
														_a     					=    image_mean
														_a     					=    image_std
														_a     					=    do_reduce_labels







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Tuple:


														"""simple docstring"""


														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_center_crop": self.do_center_crop,
														    "crop_size": self.crop_size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_reduce_labels": self.do_reduce_labels,
														}



def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    load_dataset('''hf-internal-testing/fixtures_ade20k'''			,						split='''test'''				)

							_a     					=    Image.open(dataset[0]['''file''']				)
							_a     					=    Image.open(dataset[1]['''file''']				)

							return image, map
def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    load_dataset('''hf-internal-testing/fixtures_ade20k'''			,						split='''test'''				)

							_a     					=    Image.open(ds[0]['''file''']				)
							_a     					=    Image.open(ds[1]['''file''']				)
							_a     					=    Image.open(ds[2]['''file''']				)
							_a     					=    Image.open(ds[3]['''file''']				)

							return [imagea, imagea], [mapa, mapa]

@require_torch
@require_vision
class       A   (		_a					,unittest.TestCase   ):
							lowercase_          =    BeitImageProcessor if is_vision_available() else None







							def        __lowerCAmelCase      (  self	:  int )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    BeitImageProcessingTester(self )







							@property
							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Optional[Any]:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							int:


														"""simple docstring"""


														_a     					=    self.image_processing_class(**self.image_processor_dict )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''do_resize''' ) )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''size''' ) )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''do_center_crop''' ) )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''center_crop''' ) )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''do_normalize''' ) )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''image_mean''' ) )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''image_std''' ) )







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


														"""simple docstring"""


														_a     					=    self.image_processing_class.from_dict(self.image_processor_dict )
														self.assertEqual(image_processor.size							,      {'''height''': 20, '''width''': 20} )
														self.assertEqual(image_processor.crop_size							,      {'''height''': 18, '''width''': 18} )
														self.assertEqual(image_processor.do_reduce_labels							,      lowerCAmelCase_ )

														_a     					=    self.image_processing_class.from_dict(
														    self.image_processor_dict							,      size=42							,      crop_size=84							,      reduce_labels=lowerCAmelCase_ )
														self.assertEqual(image_processor.size							,      {'''height''': 42, '''width''': 42} )
														self.assertEqual(image_processor.crop_size							,      {'''height''': 84, '''width''': 84} )
														self.assertEqual(image_processor.do_reduce_labels							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Optional[int]:


														"""simple docstring"""


														pass







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Dict:


														"""simple docstring"""


														_a     					=    self.image_processing_class(**self.image_processor_dict )
														# create random PIL images
														_a     					=    prepare_image_inputs(self.image_processor_tester							,      equal_resolution=lowerCAmelCase_ )
														for image in image_inputs:
																					self.assertIsInstance(lowerCAmelCase_							,      Image.Image )

														# Test not batched input
														_a     					=    image_processing(image_inputs[0]							,      return_tensors='''pt''' ).pixel_values
														self.assertEqual(
														    encoded_images.shape							,      (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )

														# Test batched
														_a     					=    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,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    self.image_processing_class(**self.image_processor_dict )
														# create random numpy tensors
														_a     					=    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
														_a     					=    image_processing(image_inputs[0]							,      return_tensors='''pt''' ).pixel_values
														self.assertEqual(
														    encoded_images.shape							,      (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )

														# Test batched
														_a     					=    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,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    self.image_processing_class(**self.image_processor_dict )
														# create random PyTorch tensors
														_a     					=    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
														_a     					=    image_processing(image_inputs[0]							,      return_tensors='''pt''' ).pixel_values
														self.assertEqual(
														    encoded_images.shape							,      (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )

														# Test batched
														_a     					=    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,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )







							def        __lowerCAmelCase      (  self	:  int )      ->							List[str]:


														"""simple docstring"""


														_a     					=    self.image_processing_class(**self.image_processor_dict )
														# create random PyTorch tensors
														_a     					=    prepare_image_inputs(self.image_processor_tester							,      equal_resolution=lowerCAmelCase_							,      torchify=lowerCAmelCase_ )
														_a     					=    []
														for image in image_inputs:
																					self.assertIsInstance(lowerCAmelCase_							,      torch.Tensor )
																					maps.append(torch.zeros(image.shape[-2:] ).long() )

														# Test not batched input
														_a     					=    image_processing(image_inputs[0]							,      maps[0]							,      return_tensors='''pt''' )
														self.assertEqual(
														    encoding['''pixel_values'''].shape							,      (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )
														self.assertEqual(
														    encoding['''labels'''].shape							,      (
														        1,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )
														self.assertEqual(encoding['''labels'''].dtype							,      torch.long )
														self.assertTrue(encoding['''labels'''].min().item() >= 0 )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )

														# Test batched
														_a     					=    image_processing(lowerCAmelCase_							,      lowerCAmelCase_							,      return_tensors='''pt''' )
														self.assertEqual(
														    encoding['''pixel_values'''].shape							,      (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )
														self.assertEqual(
														    encoding['''labels'''].shape							,      (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )
														self.assertEqual(encoding['''labels'''].dtype							,      torch.long )
														self.assertTrue(encoding['''labels'''].min().item() >= 0 )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )

														# Test not batched input (PIL images)
														_a					,							_a     					=    prepare_semantic_single_inputs()

														_a     					=    image_processing(lowerCAmelCase_							,      lowerCAmelCase_							,      return_tensors='''pt''' )
														self.assertEqual(
														    encoding['''pixel_values'''].shape							,      (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )
														self.assertEqual(
														    encoding['''labels'''].shape							,      (
														        1,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )
														self.assertEqual(encoding['''labels'''].dtype							,      torch.long )
														self.assertTrue(encoding['''labels'''].min().item() >= 0 )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )

														# Test batched input (PIL images)
														_a					,							_a     					=    prepare_semantic_batch_inputs()

														_a     					=    image_processing(lowerCAmelCase_							,      lowerCAmelCase_							,      return_tensors='''pt''' )
														self.assertEqual(
														    encoding['''pixel_values'''].shape							,      (
														        2,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )
														self.assertEqual(
														    encoding['''labels'''].shape							,      (
														        2,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )							,      )
														self.assertEqual(encoding['''labels'''].dtype							,      torch.long )
														self.assertTrue(encoding['''labels'''].min().item() >= 0 )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )







							def        __lowerCAmelCase      (  self	:  str )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.image_processing_class(**self.image_processor_dict )

														# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
														_a					,							_a     					=    prepare_semantic_single_inputs()
														_a     					=    image_processing(lowerCAmelCase_							,      lowerCAmelCase_							,      return_tensors='''pt''' )
														self.assertTrue(encoding['''labels'''].min().item() >= 0 )
														self.assertTrue(encoding['''labels'''].max().item() <= 1_50 )

														_a     					=    True
														_a     					=    image_processing(lowerCAmelCase_							,      lowerCAmelCase_							,      return_tensors='''pt''' )
														self.assertTrue(encoding['''labels'''].min().item() >= 0 )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )







'''simple docstring'''


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


							_a     					=    {}

							_a     					=    job['''started_at''']
							_a     					=    job['''completed_at''']

							_a     					=    date_parser.parse(UpperCamelCase				)
							_a     					=    date_parser.parse(UpperCamelCase				)

							_a     					=    round((end_datetime - start_datetime).total_seconds() / 60.0				)

							_a     					=    start
							_a     					=    end
							_a     					=    duration_in_min

							return job_info
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  int=None				):





							'''simple docstring'''


							_a     					=    None
							if token is not None:
														_a     					=    {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

							_a     					=    f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
							_a     					=    requests.get(UpperCamelCase			,						headers=UpperCamelCase				).json()
							_a     					=    {}

							try:
														job_time.update({job['''name''']: extract_time_from_single_job(UpperCamelCase				) for job in result['''jobs''']}				)
														_a     					=    math.ceil((result['''total_count'''] - 100) / 100				)

														for i in range(UpperCamelCase				):
																					_a     					=    requests.get(url + f'&page={i + 2}'			,						headers=UpperCamelCase				).json()
																					job_time.update({job['''name''']: extract_time_from_single_job(UpperCamelCase				) for job in result['''jobs''']}				)

														return job_time
							except Exception:
														print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'				)

							return {}


if __name__ == "__main__":
				_snake_case				:				int       							=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
				_snake_case				:				Tuple       							=		parser.parse_args()

				_snake_case				:				int       							=		get_job_time(args.workflow_run_id)
				_snake_case				:				int       							=		dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

				for k, v in job_time.items():
								print(F'''{k}: {v['duration']}''')

