


"""simple docstring"""






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


_lowerCamelCase     =  logging.get_logger(__name__)


@dataclass
class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					[
				    "no_inference",
				    "no_cuda",
				    "no_tpu",
				    "no_speed",
				    "no_memory",
				    "no_env_print",
				    "no_multi_process",
				]



				def __init__(						self       ,		**UpperCAmelCase__     ):
								for deprecated_arg in self.deprecated_args:
												if deprecated_arg in kwargs:
																SCREAMING_SNAKE_CASE__     							=				deprecated_arg[3:]
																SCREAMING_SNAKE_CASE__     							=				not kwargs.pop(UpperCAmelCase__     )
																logger.warning(
																    f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
																    f''' {positive_arg}={kwargs[positive_arg]}'''     )
								SCREAMING_SNAKE_CASE__     							=				kwargs.pop("tpu_name"       ,		self.tpu_name     )
								SCREAMING_SNAKE_CASE__     							=				kwargs.pop("device_idx"       ,		self.device_idx     )
								SCREAMING_SNAKE_CASE__     							=				kwargs.pop("eager_mode"       ,		self.eager_mode     )
								SCREAMING_SNAKE_CASE__     							=				kwargs.pop("use_xla"       ,		self.use_xla     )
								super().__init__(**UpperCAmelCase__     )

				_lowerCAmelCase      :      str      							=					field(
				    default=lowercase       ,  metadata={"help": "Name of TPU"}       ,  )
				_lowerCAmelCase      :      int      							=					field(
				    default=0       ,  metadata={"help": "CPU / GPU device index. Defaults to 0."}       ,  )
				_lowerCAmelCase      :      bool      							=					field(default=lowercase       ,  metadata={"help": "Benchmark models in eager model."}   )
				_lowerCAmelCase      :      bool      							=					field(
				    default=lowercase       ,  metadata={
				        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
				    }       ,  )



				@cached_property
				def   lowerCAmelCase__		(						self     ):
								requires_backends(self       ,		["tf"]     )
								SCREAMING_SNAKE_CASE__     							=				None
								if self.tpu:
												try:
																if self.tpu_name:
																				SCREAMING_SNAKE_CASE__     							=				tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
																else:
																				SCREAMING_SNAKE_CASE__     							=				tf.distribute.cluster_resolver.TPUClusterResolver()
												except ValueError:
																SCREAMING_SNAKE_CASE__     							=				None
								return tpu



				@cached_property
				def   lowerCAmelCase__		(						self     ):
								requires_backends(self       ,		["tf"]     )
								if self.is_tpu:
												tf.config.experimental_connect_to_cluster(self._setup_tpu     )
												tf.tpu.experimental.initialize_tpu_system(self._setup_tpu     )

												SCREAMING_SNAKE_CASE__     							=				tf.distribute.TPUStrategy(self._setup_tpu     )
								else:
												# currently no multi gpu is allowed
												if self.is_gpu:
																# TODO: Currently only single GPU is supported
																tf.config.set_visible_devices(self.gpu_list[self.device_idx]       ,		"GPU"     )
																SCREAMING_SNAKE_CASE__     							=				tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}'''     )
												else:
																tf.config.set_visible_devices([]       ,		"GPU"     )  # disable GPU
																SCREAMING_SNAKE_CASE__     							=				tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}'''     )

								return strategy



				@property
				def   lowerCAmelCase__		(						self     ):
								requires_backends(self       ,		["tf"]     )
								return self._setup_tpu is not None



				@property
				def   lowerCAmelCase__		(						self     ):
								requires_backends(self       ,		["tf"]     )
								return self._setup_strategy



				@property
				def   lowerCAmelCase__		(						self     ):
								requires_backends(self       ,		["tf"]     )
								return tf.config.list_physical_devices("GPU"     )



				@property
				def   lowerCAmelCase__		(						self     ):
								requires_backends(self       ,		["tf"]     )
								if self.cuda:
												return len(self.gpu_list     )
								return 0






				@property
				def   lowerCAmelCase__		(						self     ):
								return self.n_gpu > 0




"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					1



				@register_to_config
				def __init__(						self       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=20       ,		UpperCAmelCase__=1e-3     ):
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				torch.linspace(1       ,		self.config.sampling_eps       ,		UpperCAmelCase__       ,		device=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None     ):
								if self.timesteps is None:
												raise ValueError(
												    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"     )

								# TODO(Patrick) better comments + non-PyTorch
								# postprocess model score
								SCREAMING_SNAKE_CASE__     							=				(
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								SCREAMING_SNAKE_CASE__     							=				std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												SCREAMING_SNAKE_CASE__     							=				std.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-score / std

								# compute
								SCREAMING_SNAKE_CASE__     							=				-1.0 / len(self.timesteps     )

								SCREAMING_SNAKE_CASE__     							=				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
								SCREAMING_SNAKE_CASE__     							=				beta_t.flatten()
								while len(beta_t.shape     ) < len(x.shape     ):
												SCREAMING_SNAKE_CASE__     							=				beta_t.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-0.5 * beta_t * x

								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				drift - diffusion**2 * score
								SCREAMING_SNAKE_CASE__     							=				x + drift * dt

								# add noise
								SCREAMING_SNAKE_CASE__     							=				randn_tensor(x.shape       ,		layout=x.layout       ,		generator=UpperCAmelCase__       ,		device=x.device       ,		dtype=x.dtype     )
								SCREAMING_SNAKE_CASE__     							=				x_mean + diffusion * math.sqrt(-dt     ) * noise

								return x, x_mean






				def __len__(						self     ):
								return self.config.num_train_timesteps
