

"""simple docstring"""




import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


A_				:      Optional[int]           =			logging.get_logger(__name__)

enable_full_determinism()





class  lowerCamelCase    (SCREAMING_SNAKE_CASE__      ,SCREAMING_SNAKE_CASE__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[str]       		=				UNetaDModel
			lowerCamelCase__ :		Tuple       		=				'sample'

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   4
						SCREAMING_SNAKE_CASE__										=   3
						SCREAMING_SNAKE_CASE__										=   (3_2, 3_2)

						SCREAMING_SNAKE_CASE__										=   floats_tensor((batch_size, num_channels) + sizes							).to(A_							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor([1_0]							).to(A_							)

						return {"sample": noise, "timestep": time_step}

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     List[Any]:
						return (3, 3_2, 3_2)

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Optional[Any]:
						return (3, 3_2, 3_2)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   {
						    '''block_out_channels''': (3_2, 6_4),
						    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
						    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
						    '''attention_head_dim''': 3,
						    '''out_channels''': 3,
						    '''in_channels''': 3,
						    '''layers_per_block''': 2,
						    '''sample_size''': 3_2,
						}
						SCREAMING_SNAKE_CASE__										=   self.dummy_input
						return init_dict, inputs_dict





class  lowerCamelCase    (SCREAMING_SNAKE_CASE__      ,SCREAMING_SNAKE_CASE__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[str]       		=				UNetaDModel
			lowerCamelCase__ :		List[Any]       		=				'sample'

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   4
						SCREAMING_SNAKE_CASE__										=   4
						SCREAMING_SNAKE_CASE__										=   (3_2, 3_2)

						SCREAMING_SNAKE_CASE__										=   floats_tensor((batch_size, num_channels) + sizes							).to(A_							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor([1_0]							).to(A_							)

						return {"sample": noise, "timestep": time_step}

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Tuple:
						return (4, 3_2, 3_2)

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[Any]:
						return (4, 3_2, 3_2)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   {
						    '''sample_size''': 3_2,
						    '''in_channels''': 4,
						    '''out_channels''': 4,
						    '''layers_per_block''': 2,
						    '''block_out_channels''': (3_2, 6_4),
						    '''attention_head_dim''': 3_2,
						    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
						    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
						}
						SCREAMING_SNAKE_CASE__										=   self.dummy_input
						return init_dict, inputs_dict

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""     ,					output_loading_info=A_							)

						self.assertIsNotNone(A_							)
						self.assertEqual(len(loading_info["""missing_keys"""]							)     ,					0							)

						model.to(A_							)
						SCREAMING_SNAKE_CASE__										=   model(**self.dummy_input							).sample

						assert image is not None, "Make sure output is not None"

			@unittest.skipIf(torch_device != """cuda"""     ,					"""This test is supposed to run on GPU"""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""     ,					output_loading_info=A_							)
						model.to(A_							)
						SCREAMING_SNAKE_CASE__										=   model(**self.dummy_input							).sample

						assert image is not None, "Make sure output is not None"

			@unittest.skipIf(torch_device != """cuda"""     ,					"""This test is supposed to run on GPU"""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""     ,					output_loading_info=A_							)
						model_accelerate.to(A_							)
						model_accelerate.eval()

						SCREAMING_SNAKE_CASE__										=   torch.randn(
						    1     ,					model_accelerate.config.in_channels     ,					model_accelerate.config.sample_size     ,					model_accelerate.config.sample_size     ,					generator=torch.manual_seed(0							)     ,					)
						SCREAMING_SNAKE_CASE__										=   noise.to(A_							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor([1_0] * noise.shape[0]							).to(A_							)

						SCREAMING_SNAKE_CASE__										=   model_accelerate(A_     ,					A_							)['''sample''']

						# two models don't need to stay in the device at the same time
						del model_accelerate
						torch.cuda.empty_cache()
						gc.collect()

						SCREAMING_SNAKE_CASE__										=   UNetaDModel.from_pretrained(
						    """fusing/unet-ldm-dummy-update"""     ,					output_loading_info=A_     ,					low_cpu_mem_usage=A_							)
						model_normal_load.to(A_							)
						model_normal_load.eval()
						SCREAMING_SNAKE_CASE__										=   model_normal_load(A_     ,					A_							)['''sample''']

						assert torch_all_close(A_     ,					A_     ,					rtol=1e-3							)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							)
						model.eval()
						model.to(A_							)

						SCREAMING_SNAKE_CASE__										=   torch.randn(
						    1     ,					model.config.in_channels     ,					model.config.sample_size     ,					model.config.sample_size     ,					generator=torch.manual_seed(0							)     ,					)
						SCREAMING_SNAKE_CASE__										=   noise.to(A_							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor([1_0] * noise.shape[0]							).to(A_							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(A_     ,					A_							).sample

						SCREAMING_SNAKE_CASE__										=   output[0, -1, -3:, -3:].flatten().cpu()
						# fmt: off
						SCREAMING_SNAKE_CASE__										=   torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]							)
						# fmt: on

						self.assertTrue(torch_all_close(A_     ,					A_     ,					rtol=1e-3							)							)





class  lowerCamelCase    (SCREAMING_SNAKE_CASE__      ,SCREAMING_SNAKE_CASE__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[str]       		=				UNetaDModel
			lowerCamelCase__ :		List[Any]       		=				'sample'

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    str=(3_2, 3_2)							) ->     int:
						SCREAMING_SNAKE_CASE__										=   4
						SCREAMING_SNAKE_CASE__										=   3

						SCREAMING_SNAKE_CASE__										=   floats_tensor((batch_size, num_channels) + sizes							).to(A_							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor(batch_size * [1_0]							).to(dtype=torch.intaa     ,					device=A_							)

						return {"sample": noise, "timestep": time_step}

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Optional[int]:
						return (3, 3_2, 3_2)

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Optional[int]:
						return (3, 3_2, 3_2)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   {
						    '''block_out_channels''': [3_2, 6_4, 6_4, 6_4],
						    '''in_channels''': 3,
						    '''layers_per_block''': 1,
						    '''out_channels''': 3,
						    '''time_embedding_type''': '''fourier''',
						    '''norm_eps''': 1e-6,
						    '''mid_block_scale_factor''': math.sqrt(2.0							),
						    '''norm_num_groups''': None,
						    '''down_block_types''': [
						        '''SkipDownBlock2D''',
						        '''AttnSkipDownBlock2D''',
						        '''SkipDownBlock2D''',
						        '''SkipDownBlock2D''',
						    ],
						    '''up_block_types''': [
						        '''SkipUpBlock2D''',
						        '''SkipUpBlock2D''',
						        '''AttnSkipUpBlock2D''',
						        '''SkipUpBlock2D''',
						    ],
						}
						SCREAMING_SNAKE_CASE__										=   self.dummy_input
						return init_dict, inputs_dict

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""     ,					output_loading_info=A_							)
						self.assertIsNotNone(A_							)
						self.assertEqual(len(loading_info["""missing_keys"""]							)     ,					0							)

						model.to(A_							)
						SCREAMING_SNAKE_CASE__										=   self.dummy_input
						SCREAMING_SNAKE_CASE__										=   floats_tensor((4, 3) + (2_5_6, 2_5_6)							).to(A_							)
						SCREAMING_SNAKE_CASE__										=   noise
						SCREAMING_SNAKE_CASE__										=   model(**A_							)

						assert image is not None, "Make sure output is not None"

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""							)
						model.to(A_							)

						SCREAMING_SNAKE_CASE__										=   4
						SCREAMING_SNAKE_CASE__										=   3
						SCREAMING_SNAKE_CASE__										=   (2_5_6, 2_5_6)

						SCREAMING_SNAKE_CASE__										=   torch.ones((batch_size, num_channels) + sizes							).to(A_							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor(batch_size * [1e-4]							).to(A_							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(A_     ,					A_							).sample

						SCREAMING_SNAKE_CASE__										=   output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						SCREAMING_SNAKE_CASE__										=   torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]							)
						# fmt: on

						self.assertTrue(torch_all_close(A_     ,					A_     ,					rtol=1e-2							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     str:
						SCREAMING_SNAKE_CASE__										=   UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""							)
						model.to(A_							)

						SCREAMING_SNAKE_CASE__										=   4
						SCREAMING_SNAKE_CASE__										=   3
						SCREAMING_SNAKE_CASE__										=   (3_2, 3_2)

						SCREAMING_SNAKE_CASE__										=   torch.ones((batch_size, num_channels) + sizes							).to(A_							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor(batch_size * [1e-4]							).to(A_							)

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(A_     ,					A_							).sample

						SCREAMING_SNAKE_CASE__										=   output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						SCREAMING_SNAKE_CASE__										=   torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]							)
						# fmt: on

						self.assertTrue(torch_all_close(A_     ,					A_     ,					rtol=1e-2							)							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     str:
						pass




"""simple docstring"""




import os
from datetime import datetime as dt

from github import Github


A__ :       Tuple				= [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  Github(os.environ['''GITHUB_TOKEN''']    )
  _lowercase:      Optional[int]   			=  g.get_repo('''huggingface/diffusers'''    )
  _lowercase:      Union[str, Any]   			=  repo.get_issues(state='''open'''    )

  for issue in open_issues:
    _lowercase:      List[Any]   			=  sorted(issue.get_comments()			,					key=lambda _UpperCamelCase    : i.created_at			,					reverse=_UpperCamelCase    )
    _lowercase:      Optional[Any]   			=  comments[0] if len(_UpperCamelCase    ) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''    )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''    )
      issue.remove_from_labels('''stale'''    )
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          '''This issue has been automatically marked as stale because it has not had '''
          '''recent activity. If you think this still needs to be addressed '''
          '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
          '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''    )
      issue.add_to_labels('''stale'''    )


if __name__ == "__main__":
  main()
