


"""simple docstring"""







import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Dict					      =    (UnCLIPScheduler,)




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    **UpperCAmelCase__    :       Optional[Any]     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       {
						    "num_train_timesteps": 1_0_0_0,
						    "variance_type": "fixed_small_log",
						    "clip_sample": True,
						    "clip_sample_range": 1.0,
						    "prediction_type": "epsilon",
						}

						config.update(**UpperCAmelCase__     )
						return config




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[Any]:
						for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Any:
						for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Tuple:
						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Dict:
						for clip_sample_range in [1, 5, 1_0, 2_0]:
											self.check_over_configs(clip_sample_range=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       str     ) ->			str:
						for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			str:
						for time_step in [0, 5_0_0, 9_9_9]:
											for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
																if prev_timestep is not None and prev_timestep >= time_step:
																					continue

																self.check_over_forward(time_step=UpperCAmelCase__    ,    prev_timestep=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config(variance_type="fixed_small_log"     )
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0000E-10     )     ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ) - 0.0_549_625     )     ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.9_994_987     )     ) < 1E-5




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config(variance_type="learned_range"     )
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       0.5

						assert scheduler._get_variance(1    ,    predicted_variance=UpperCAmelCase__     ) - -10.1_712_790 < 1E-5
						assert scheduler._get_variance(4_8_7    ,    predicted_variance=UpperCAmelCase__     ) - -5.7_998_052 < 1E-5
						assert scheduler._get_variance(9_9_9    ,    predicted_variance=UpperCAmelCase__     ) - -0.0_010_011 < 1E-5




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config()
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       scheduler.timesteps

						__SCREAMING_SNAKE_CASE		     =       self.dummy_model()
						__SCREAMING_SNAKE_CASE		     =       self.dummy_sample_deter
						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )

						for i, t in enumerate(UpperCAmelCase__     ):
											# 1. predict noise residual
											__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    UpperCAmelCase__     )

											# 2. predict previous mean of sample x_t-1
											__SCREAMING_SNAKE_CASE		     =       scheduler.step(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    generator=UpperCAmelCase__     ).prev_sample

											__SCREAMING_SNAKE_CASE		     =       pred_prev_sample

						__SCREAMING_SNAKE_CASE		     =       torch.sum(torch.abs(UpperCAmelCase__     )     )
						__SCREAMING_SNAKE_CASE		     =       torch.mean(torch.abs(UpperCAmelCase__     )     )

						assert abs(result_sum.item() - 252.2_682_495     ) < 1E-2
						assert abs(result_mean.item() - 0.3_284_743     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config()
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						scheduler.set_timesteps(2_5     )

						__SCREAMING_SNAKE_CASE		     =       scheduler.timesteps

						__SCREAMING_SNAKE_CASE		     =       self.dummy_model()
						__SCREAMING_SNAKE_CASE		     =       self.dummy_sample_deter
						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )

						for i, t in enumerate(UpperCAmelCase__     ):
											# 1. predict noise residual
											__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    UpperCAmelCase__     )

											if i + 1 == timesteps.shape[0]:
																__SCREAMING_SNAKE_CASE		     =       None
											else:
																__SCREAMING_SNAKE_CASE		     =       timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											__SCREAMING_SNAKE_CASE		     =       scheduler.step(
											    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    prev_timestep=UpperCAmelCase__    ,    generator=UpperCAmelCase__     ).prev_sample

											__SCREAMING_SNAKE_CASE		     =       pred_prev_sample

						__SCREAMING_SNAKE_CASE		     =       torch.sum(torch.abs(UpperCAmelCase__     )     )
						__SCREAMING_SNAKE_CASE		     =       torch.mean(torch.abs(UpperCAmelCase__     )     )

						assert abs(result_sum.item() - 258.2_044_983     ) < 1E-2
						assert abs(result_mean.item() - 0.3_362_038     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Optional[Any]:
						pass




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




"""simple docstring"""







import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return EnvironmentCommand()




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       parser.add_parser("env"     )
						download_parser.set_defaults(func=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       huggingface_hub.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						__SCREAMING_SNAKE_CASE		     =       "NA"
						if is_torch_available():
											import torch

											__SCREAMING_SNAKE_CASE		     =       torch.__version__
											__SCREAMING_SNAKE_CASE		     =       torch.cuda.is_available()

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_transformers_available():
											import transformers

											__SCREAMING_SNAKE_CASE		     =       transformers.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_accelerate_available():
											import accelerate

											__SCREAMING_SNAKE_CASE		     =       accelerate.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_xformers_available():
											import xformers

											__SCREAMING_SNAKE_CASE		     =       xformers.__version__

						__SCREAMING_SNAKE_CASE		     =       {
						    "`diffusers` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
						    "Huggingface_hub version": hub_version,
						    "Transformers version": transformers_version,
						    "Accelerate version": accelerate_version,
						    "xFormers version": xformers_version,
						    "Using GPU in script?": "<fill in>",
						    "Using distributed or parallel set-up in script?": "<fill in>",
						}

						print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"     )
						print(self.format_dict(UpperCAmelCase__     )     )

						return info




	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       Optional[Any]     ) ->			str:
						return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]     ) + "\n"
