
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


SCREAMING_SNAKE_CASE							:int							 =				logging.get_logger(__name__)

enable_full_determinism()





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     UNetaDModel
	snake_case_					    =     "sample"






	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= 4
					__A   			= 3
					__A   			= (32, 32)

					__A   			= floats_tensor((batch_size, num_channels) + sizes ).to(A )
					__A   			= torch.tensor([10] ).to(A )

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






	@property
	def 				UpperCamelCase_  (       self					: Any ):
					return (3, 32, 32)






	@property
	def 				UpperCamelCase_  (       self					: Any ):
					return (3, 32, 32)







	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= {
					    "block_out_channels": (32, 64),
					    "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": 32,
					}
					__A   			= self.dummy_input
					return init_dict, inputs_dict





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     UNetaDModel
	snake_case_					    =     "sample"






	@property
	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= 4
					__A   			= 4
					__A   			= (32, 32)

					__A   			= floats_tensor((batch_size, num_channels) + sizes ).to(A )
					__A   			= torch.tensor([10] ).to(A )

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






	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					return (4, 32, 32)






	@property
	def 				UpperCamelCase_  (       self					: int ):
					return (4, 32, 32)






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= {
					    "sample_size": 32,
					    "in_channels": 4,
					    "out_channels": 4,
					    "layers_per_block": 2,
					    "block_out_channels": (32, 64),
					    "attention_head_dim": 32,
					    "down_block_types": ("DownBlock2D", "DownBlock2D"),
					    "up_block_types": ("UpBlock2D", "UpBlock2D"),
					}
					__A   			= self.dummy_input
					return init_dict, inputs_dict






	def 				UpperCamelCase_  (       self					: Dict ):
					__A			,							__A   			= 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 )
					__A   			= 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 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A			,							__A   			= UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"							,output_loading_info=A )
					model.to(A )
					__A   			= 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 				UpperCamelCase_  (       self					: Union[str, Any] ):
					# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
					__A			,							__A   			= UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"							,output_loading_info=A )
					model_accelerate.to(A )
					model_accelerate.eval()

					__A   			= torch.randn(
					    1							,model_accelerate.config.in_channels							,model_accelerate.config.sample_size							,model_accelerate.config.sample_size							,generator=torch.manual_seed(0 )							,)
					__A   			= noise.to(A )
					__A   			= torch.tensor([10] * noise.shape[0] ).to(A )

					__A   			= 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()

					__A			,							__A   			= 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()
					__A   			= model_normal_load(A							,A )["sample"]

					assert torch_all_close(A							,A							,rtol=1E-3 )







	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" )
					model.eval()
					model.to(A )

					__A   			= torch.randn(
					    1							,model.config.in_channels							,model.config.sample_size							,model.config.sample_size							,generator=torch.manual_seed(0 )							,)
					__A   			= noise.to(A )
					__A   			= torch.tensor([10] * noise.shape[0] ).to(A )

					with torch.no_grad():
									__A   			= model(A							,A ).sample

					__A   			= output[0, -1, -3:, -3:].flatten().cpu()
					# fmt: off
					__A   			= torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00] )
					# fmt: on

					self.assertTrue(torch_all_close(A							,A							,rtol=1E-3 ) )





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     UNetaDModel
	snake_case_					    =     "sample"






	@property
	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: List[str]=(32, 32) ):
					__A   			= 4
					__A   			= 3

					__A   			= floats_tensor((batch_size, num_channels) + sizes ).to(A )
					__A   			= torch.tensor(batch_size * [10] ).to(dtype=torch.intaa							,device=A )

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






	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					return (3, 32, 32)






	@property
	def 				UpperCamelCase_  (       self					: List[Any] ):
					return (3, 32, 32)






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= {
					    "block_out_channels": [32, 64, 64, 64],
					    "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",
					    ],
					}
					__A   			= self.dummy_input
					return init_dict, inputs_dict






	@slow
	def 				UpperCamelCase_  (       self					: List[str] ):
					__A			,							__A   			= 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 )
					__A   			= self.dummy_input
					__A   			= floats_tensor((4, 3) + (2_56, 2_56) ).to(A )
					__A   			= noise
					__A   			= model(**A )

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






	@slow
	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" )
					model.to(A )

					__A   			= 4
					__A   			= 3
					__A   			= (2_56, 2_56)

					__A   			= torch.ones((batch_size, num_channels) + sizes ).to(A )
					__A   			= torch.tensor(batch_size * [1E-4] ).to(A )

					with torch.no_grad():
									__A   			= model(A							,A ).sample

					__A   			= output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					__A   			= torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08] )
					# fmt: on

					self.assertTrue(torch_all_close(A							,A							,rtol=1E-2 ) )






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update" )
					model.to(A )

					__A   			= 4
					__A   			= 3
					__A   			= (32, 32)

					__A   			= torch.ones((batch_size, num_channels) + sizes ).to(A )
					__A   			= torch.tensor(batch_size * [1E-4] ).to(A )

					with torch.no_grad():
									__A   			= model(A							,A ).sample

					__A   			= output[0, -3:, -3:, -1].flatten().cpu()
					# fmt: off
					__A   			= torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56] )
					# fmt: on

					self.assertTrue(torch_all_close(A							,A							,rtol=1E-2 ) )







	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					# not required for this model
					pass


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 						UpperCAmelCase			(	a_						)		->	str:







				"""simple docstring"""



				__A   			= {}

				__A   			= job["started_at"]
				__A   			= job["completed_at"]

				__A   			= date_parser.parse(a_						)
				__A   			= date_parser.parse(a_						)

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

				__A   			= start
				__A   			= end
				__A   			= duration_in_min

				return job_info


def 						UpperCAmelCase			(	a_		,   a_=None						)		->	str:







				"""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(a_		,   headers=a_						).json()
				__A   			= {}

				try:
								job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)
								__A   			= math.ceil((result["total_count"] - 1_0_0) / 1_0_0						)

								for i in range(a_						):
												__A   			= 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__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE							:Optional[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"]}''')
