





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class        SCREAMING_SNAKE_CASE     (lowercase__    ,			unittest.TestCase   ):
   _UpperCamelCase					:	int				      = DDIMPipeline
   _UpperCamelCase					:	Optional[int]				      = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   _UpperCamelCase					:	Any				      = PipelineTesterMixin.required_optional_params - {
       'num_images_per_prompt',
       'latents',
       'callback',
       'callback_steps',
   }
   _UpperCamelCase					:	Any				      = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
   _UpperCamelCase					:	Any				      = False


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Dict:





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						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')    ,  )
     lowercase__			    =						DDIMScheduler()
     lowercase__			    =						{'''unet''': unet, '''scheduler''': scheduler}
     return components


   def  SCREAMING_SNAKE_CASE_							(					self    :       int    ,  a    :       str    ,  a    :       Dict=0 )->     Dict:





     """simple docstring"""







     if str(UpperCAmelCase__ ).startswith('mps' ):
       lowercase__			    =						torch.manual_seed(UpperCAmelCase__ )
     else:
       lowercase__			    =						torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
     lowercase__			    =						{
         '''batch_size''': 1,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }
     return inputs


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Tuple:





     """simple docstring"""







     lowercase__			    =						'''cpu'''

     lowercase__			    =						self.get_dummy_components()
     lowercase__			    =						self.pipeline_class(**UpperCAmelCase__ )
     pipe.to(UpperCAmelCase__ )
     pipe.set_progress_bar_config(disable=UpperCAmelCase__ )

     lowercase__			    =						self.get_dummy_inputs(UpperCAmelCase__ )
     lowercase__			    =						pipe(**UpperCAmelCase__ ).images
     lowercase__			    =						image[0, -3:, -3:, -1]

     self.assertEqual(image.shape    ,  (1, 32, 32, 3) )
     lowercase__			    =						np.array(
         [1.0_0_0E0_0, 5.7_1_7E-0_1, 4.7_1_7E-0_1, 1.0_0_0E0_0, 0.0_0_0E0_0, 1.0_0_0E0_0, 3.0_0_0E-0_4, 0.0_0_0E0_0, 9.0_0_0E-0_4] )
     lowercase__			    =						np.abs(image_slice.flatten() - expected_slice ).max()
     self.assertLessEqual(UpperCAmelCase__    ,  1E-3 )


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





     """simple docstring"""







     super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Optional[int]:





     """simple docstring"""







     super().test_save_load_local(expected_max_difference=3E-3 )


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





     """simple docstring"""







     super().test_save_load_optional_components(expected_max_difference=3E-3 )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     int:





     """simple docstring"""







     super().test_inference_batch_single_identical(expected_max_diff=3E-3 )






@slow
@require_torch_gpu
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


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





     """simple docstring"""







     lowercase__			    =						'''google/ddpm-cifar10-32'''

     lowercase__			    =						UNetaDModel.from_pretrained(UpperCAmelCase__ )
     lowercase__			    =						DDIMScheduler()

     lowercase__			    =						DDIMPipeline(unet=UpperCAmelCase__    ,  scheduler=UpperCAmelCase__ )
     ddim.to(UpperCAmelCase__ )
     ddim.set_progress_bar_config(disable=UpperCAmelCase__ )

     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						ddim(generator=UpperCAmelCase__    ,  eta=0.0    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     lowercase__			    =						np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )

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


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





     """simple docstring"""







     lowercase__			    =						'''google/ddpm-ema-bedroom-256'''

     lowercase__			    =						UNetaDModel.from_pretrained(UpperCAmelCase__ )
     lowercase__			    =						DDIMScheduler.from_pretrained(UpperCAmelCase__ )

     lowercase__			    =						DDIMPipeline(unet=UpperCAmelCase__    ,  scheduler=UpperCAmelCase__ )
     ddpm.to(UpperCAmelCase__ )
     ddpm.set_progress_bar_config(disable=UpperCAmelCase__ )

     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						ddpm(generator=UpperCAmelCase__    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)
     lowercase__			    =						np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )

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











import argparse
import json
import subprocess




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  lowercase__			    =						[]

  lowercase__			    =						(
      F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
      ' https://api.github.com/repos/huggingface/transformers/actions/runners'
  )
  lowercase__			    =						subprocess.run(_SCREAMING_SNAKE_CASE	,		shell=_SCREAMING_SNAKE_CASE	,		stdout=subprocess.PIPE	)
  lowercase__			    =						output.stdout.decode('utf-8'	)
  lowercase__			    =						json.loads(_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						status['runners']
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_SCREAMING_SNAKE_CASE	)

    # save the result so we can report them on Slack
  with open('offline_runners.txt'	,		'w'	) as fp:
    fp.write(json.dumps(_SCREAMING_SNAKE_CASE	)	)

  if len(_SCREAMING_SNAKE_CASE	) > 0:
    lowercase__			    =						'\n'.join([x['name'] for x in offline_runners]	)
    raise ValueError(F"""The following runners are offline:\n{failed}"""	)


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return values.split(','	)

      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--target_runners""",
          default=None,
          type=list_str,
          required=True,
          help="""Comma-separated list of runners to check status.""",
      )

      parser.add_argument(
          """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
      )
      lowercase_					     =	parser.parse_args()


      get_runner_status(args.target_runners, args.token)


