



"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	Union[str, Any]:
											torch.manual_seed(0						)
											snake_case_ :str						=			UNetaDModel(
											    block_out_channels=(32, 64)						,     layers_per_block=2						,     sample_size=32						,     in_channels=3						,     out_channels=3						,     down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,     up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,     )
											return model






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	str:
											snake_case_ :Union[str, Any]						=			self.dummy_uncond_unet
											snake_case_ :Tuple						=			PNDMScheduler()

											snake_case_ :str						=			PNDMPipeline(unet=snake_case						,     scheduler=snake_case						)
											pndm.to(snake_case						)
											pndm.set_progress_bar_config(disable=snake_case						)

											snake_case_ :int						=			torch.manual_seed(0						)
											snake_case_ :List[Any]						=			pndm(generator=snake_case						,     num_inference_steps=20						,     output_type="""numpy"""						).images

											snake_case_ :Optional[Any]						=			torch.manual_seed(0						)
											snake_case_ :Union[str, Any]						=			pndm(generator=snake_case						,     num_inference_steps=20						,     output_type="""numpy"""						,     return_dict=snake_case						)[0]

											snake_case_ :Optional[int]						=			image[0, -3:, -3:, -1]
											snake_case_ :str						=			image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_ :List[str]						=			np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2

@slow
@require_torch
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Union[str, Any]:
											snake_case_ :int						=			"""google/ddpm-cifar10-32"""

											snake_case_ :List[str]						=			UNetaDModel.from_pretrained(snake_case						)
											snake_case_ :Any						=			PNDMScheduler()

											snake_case_ :Tuple						=			PNDMPipeline(unet=snake_case						,     scheduler=snake_case						)
											pndm.to(snake_case						)
											pndm.set_progress_bar_config(disable=snake_case						)
											snake_case_ :str						=			torch.manual_seed(0						)
											snake_case_ :Any						=			pndm(generator=snake_case						,     output_type="""numpy"""						).images

											snake_case_ :Optional[int]						=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_ :Optional[int]						=			np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2



import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def      a      (			A__	:   Dict     )    ->      str:





      """simple docstring"""



      _lowercase	       ={}

      _lowercase	       =job['started_at']
      _lowercase	       =job['completed_at']

      _lowercase	       =date_parser.parse(A__     )
      _lowercase	       =date_parser.parse(A__     )

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

      _lowercase	       =start
      _lowercase	       =end
      _lowercase	       =duration_in_min

      return job_info
def      a      (			A__	:   Dict      ,   A__	:   str=None     )    ->      Tuple:





      """simple docstring"""



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

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

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

            for i in range(A__     ):
                  _lowercase	       =requests.get(url + F'''&page={i + 2}'''      ,   headers=A__     ).json()
                  job_time.update({job['name']: extract_time_from_single_job(A__     ) 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__":
  lowercase_									=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
  lowercase_									=   parser.parse_args()

  lowercase_									=   get_job_time(args.workflow_run_id)
  lowercase_									=   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']}")


