




import unittest

import numpy as np
import requests

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

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				_snake_case				:				Any       							=		False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor

class       A   (		unittest.TestCase   ):







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


														"""simple docstring"""


														_a     					=    size if size is not None else {'''height''': 20, '''width''': 20}
														_a     					=    parent
														_a     					=    batch_size
														_a     					=    num_channels
														_a     					=    image_size
														_a     					=    min_resolution
														_a     					=    max_resolution
														_a     					=    size
														_a     					=    do_normalize
														_a     					=    do_convert_rgb
														_a     					=    [5_12, 10_24, 20_48, 40_96]
														_a     					=    patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}







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


														"""simple docstring"""


														return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Tuple:


														"""simple docstring"""


														_a     					=    '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
														_a     					=    Image.open(requests.get(lowerCAmelCase_							,      stream=lowerCAmelCase_ ).raw ).convert('''RGB''' )
														return raw_image



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					,reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'					,)
@require_torch
@require_vision
class       A   (		_a					,unittest.TestCase   ):
							lowercase_          =    PixaStructImageProcessor if is_vision_available() else None







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


														"""simple docstring"""


														_a     					=    PixaStructImageProcessingTester(self )







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


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







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


														"""simple docstring"""


														_a     					=    self.image_processing_class(**self.image_processor_dict )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''do_normalize''' ) )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''do_convert_rgb''' ) )







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


														"""simple docstring"""


														_a     					=    self.image_processor_tester.prepare_dummy_image()

														_a     					=    self.image_processing_class(**self.image_processor_dict )
														_a     					=    20_48

														_a     					=    image_processor(lowerCAmelCase_							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ )
														self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,      torch.tensor(0.0_6_0_6 )							,      atol=1e-3							,      rtol=1e-3 ) )







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


														"""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     					=    (
														    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
														    * self.image_processor_tester.num_channels
														) + 2

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					_a     					=    image_processor(
																					    image_inputs[0]							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (1, max_patch, expected_hidden_dim)							,      )

																					# Test batched
																					_a     					=    image_processor(
																					    lowerCAmelCase_							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,      )







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


														"""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     					=    (
														    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
														    * self.image_processor_tester.num_channels
														) + 2

														_a     					=    True

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					with self.assertRaises(lowerCAmelCase_ ):
																												_a     					=    image_processor(
																												    image_inputs[0]							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ ).flattened_patches

																					_a     					=    '''Hello'''

																					_a     					=    image_processor(
																					    image_inputs[0]							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_							,      header_text=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (1, max_patch, expected_hidden_dim)							,      )

																					# Test batched
																					_a     					=    image_processor(
																					    lowerCAmelCase_							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_							,      header_text=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,      )







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


														"""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 )

														_a     					=    (
														    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
														    * self.image_processor_tester.num_channels
														) + 2

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					_a     					=    image_processor(
																					    image_inputs[0]							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (1, max_patch, expected_hidden_dim)							,      )

																					# Test batched
																					_a     					=    image_processor(
																					    lowerCAmelCase_							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,      )







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


														"""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     					=    (
														    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
														    * self.image_processor_tester.num_channels
														) + 2

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					_a     					=    image_processor(
																					    image_inputs[0]							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (1, max_patch, expected_hidden_dim)							,      )

																					# Test batched
																					_a     					=    image_processor(
																					    lowerCAmelCase_							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,      )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					,reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'					,)
@require_torch
@require_vision
class       A   (		_a					,unittest.TestCase   ):
							lowercase_          =    PixaStructImageProcessor if is_vision_available() else None







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


														"""simple docstring"""


														_a     					=    PixaStructImageProcessingTester(self							,      num_channels=4 )
														_a     					=    3







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


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







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


														"""simple docstring"""


														_a     					=    self.image_processing_class(**self.image_processor_dict )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''do_normalize''' ) )
														self.assertTrue(hasattr(lowerCAmelCase_							,      '''do_convert_rgb''' ) )







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


														"""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     					=    (
														    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
														    * (self.image_processor_tester.num_channels - 1)
														) + 2

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					_a     					=    image_processor(
																					    image_inputs[0]							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (1, max_patch, expected_hidden_dim)							,      )

																					# Test batched
																					_a     					=    image_processor(
																					    lowerCAmelCase_							,      return_tensors='''pt'''							,      max_patches=lowerCAmelCase_ ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape							,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,      )







'''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']}''')

