code: 
  '''simple docstring'''
  
  
  
  import timeit
  from typing import Callable, Optional
  
  from ..configuration_utils import PretrainedConfig
  from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
  from ..utils import is_pyanvml_available, is_torch_available, logging
  from .benchmark_utils import (
      Benchmark,
      Memory,
      MemorySummary,
      measure_peak_memory_cpu,
      start_memory_tracing,
      stop_memory_tracing,
  )
  
  
  if is_torch_available():
  				import torch
  
  				from .benchmark_args import PyTorchBenchmarkArguments
  
  
  if is_pyanvml_available():
  				import pyanvml.pyanvml as nvml
  
  
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							PyTorchBenchmarkArguments
  			lowerCamelCase							:							PretrainedConfig
  			lowerCamelCase							:							str       		=  "PyTorch"
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return torch.__version__
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									if self.args.torchscript:
  															lowercase__							 =       True
  
  									lowercase__							 =       (
  									    hasattr(lowerCamelCase__       ,     """architectures"""       )
  									    and isinstance(config.architectures       ,     lowerCamelCase__       )
  									    and len(config.architectures       ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  															try:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       model_cls(lowerCamelCase__       )
  															except ImportError:
  																					raise ImportError(
  																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
  									else:
  															lowercase__							 =       MODEL_MAPPING[config.__class__](lowerCamelCase__       )
  
  									model.eval()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )
  
  									if self.args.fpaa:
  															logger.info("""Running training in Mixed Precision..."""       )
  															if not self.args.is_gpu:
  																					raise ValueError("""Mixed precision is possible only for GPU."""       )
  															# amp seems to have memory leaks so that memory usage
  															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  															model.half()
  
  									if self.args.torchscript:
  															with torch.no_grad():
  																					lowercase__							 =       torch.jit.trace(lowerCamelCase__       ,     lowerCamelCase__       )
  									else:
  															lowercase__							 =       model
  
  									def encoder_decoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       )
  															return outputs
  
  									def encoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       )
  															return outputs
  
  									lowercase__							 =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
  									return _forward
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									lowercase__							 =       (
  									    hasattr(lowerCamelCase__       ,     """architectures"""       )
  									    and isinstance(config.architectures       ,     lowerCamelCase__       )
  									    and len(config.architectures       ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  															try:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       model_cls(lowerCamelCase__       )
  															except ImportError:
  																					raise ImportError(
  																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
  									else:
  															lowercase__							 =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__       )
  
  									if self.args.torchscript:
  															raise NotImplementedError("""Training for torchscript is currently not implemented"""       )
  									else:
  															lowercase__							 =       model
  
  									model.train()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )
  
  									if self.args.fpaa:
  															logger.info("""Running training in Mixed Precision..."""       )
  															if not self.args.is_gpu:
  																					raise ValueError("""Mixed precision is possible only for GPU."""       )
  
  															# amp seems to have memory leaks so that memory usage
  															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  															model.half()
  
  									def compute_loss_and_backprob_encoder():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									def compute_loss_and_backprob_encoder_decoder():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									lowercase__							 =       (
  									    compute_loss_and_backprob_encoder_decoder
  									    if config.is_encoder_decoder
  									    else compute_loss_and_backprob_encoder
  									)
  									return _train
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									try:
  															if self.args.is_tpu or self.args.torchscript:
  																					# run additional 10 times to stabilize compilation for tpu and torchscript
  																					logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""       )
  																					timeit.repeat(
  																					    lowerCamelCase__       ,     repeat=1       ,     number=5       ,     )
  
  															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
  															lowercase__							 =       timeit.repeat(
  															    lowerCamelCase__       ,     repeat=self.args.repeat       ,     number=10       ,     )
  
  															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
  																					import torch_xla.debug.metrics as met
  
  																					self.print_fn(met.metrics_report()       )
  
  															return min(lowerCamelCase__       ) / 10.0
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A"
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		[Memory, MemorySummary]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									try:
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       start_memory_tracing("""transformers"""       )
  
  															if self.args.is_tpu:
  																					# tpu
  																					raise NotImplementedError(
  																					    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
  																					    """ `--no-memory` or `args.memory=False`"""       )
  															elif self.args.is_gpu:
  																					if not is_pyanvml_available():
  																											logger.warning(
  																											    """py3nvml not installed, we won't log GPU memory usage. """
  																											    """Install py3nvml (pip install py3nvml) to log information about GPU."""       )
  																											lowercase__							 =       """N/A"""
  																					else:
  																											logger.info(
  																											    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
  																											    """ on the same GPU."""       )
  																											# init nvml
  																											nvml.nvmlInit()
  																											func()
  																											lowercase__							 =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
  																											lowercase__							 =       nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__       )
  																											lowercase__							 =       meminfo.used
  																											lowercase__							 =       Memory(lowerCamelCase__       )
  																											# shutdown nvml
  																											nvml.nvmlShutdown()
  															else:
  																					# cpu
  																					lowercase__							 =       measure_peak_memory_cpu(lowerCamelCase__       )
  																					lowercase__							 =       Memory(lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else memory_bytes
  
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       stop_memory_tracing(lowerCamelCase__       )
  															else:
  																					lowercase__							 =       None
  
  															return memory, summary
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A", None
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from jax import random
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
  
  
  
  
  
  @flax.struct.dataclass
  class 			A							:
  			# setable values
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls()
  
  
  
  
  
  @dataclass
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							ScoreSdeVeSchedulerState
  			lowerCamelCase							:							jnp.ndarray
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  
  
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase				):
  
  
  
  			@property
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return True
  
  
  
  			@register_to_config
  			def __init__(					self       ,     lowerCamelCase__ = 2_000       ,     lowerCamelCase__ = 0.15       ,     lowerCamelCase__ = 0.01       ,     lowerCamelCase__ = 13_48.0       ,     lowerCamelCase__ = 1e-5       ,     lowerCamelCase__ = 1       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       ScoreSdeVeSchedulerState.create()
  									return self.set_sigmas(
  									    lowerCamelCase__       ,     self.config.num_train_timesteps       ,     self.config.sigma_min       ,     self.config.sigma_max       ,     self.config.sampling_eps       ,     )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = ()       ,     lowerCamelCase__ = None       )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  									lowercase__							 =       jnp.linspace(1       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return state.replace(timesteps=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sigma_min if sigma_min is not None else self.config.sigma_min
  									lowercase__							 =       sigma_max if sigma_max is not None else self.config.sigma_max
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  									if state.timesteps is None:
  															lowercase__							 =       self.set_timesteps(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									lowercase__							 =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__       )       ,     jnp.log(lowerCamelCase__       )       ,     lowerCamelCase__       )       )
  									lowercase__							 =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]       )
  
  									return state.replace(discrete_sigmas=lowerCamelCase__       ,     sigmas=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return jnp.where(timesteps == 0       ,     jnp.zeros_like(lowerCamelCase__       )       ,     state.discrete_sigmas[timesteps - 1]       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if state.timesteps is None:
  															raise ValueError(
  															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  									lowercase__							 =       timestep * jnp.ones(
  									    sample.shape[0]       ,     )
  									lowercase__							 =       (timestep * (len(state.timesteps       ) - 1)).long()
  
  									lowercase__							 =       state.discrete_sigmas[timesteps]
  									lowercase__							 =       self.get_adjacent_sigma(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       jnp.zeros_like(lowerCamelCase__       )
  									lowercase__							 =       (sigma**2 - adjacent_sigma**2) ** 0.5
  
  									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
  									# also equation 47 shows the analog from SDE models to ancestral sampling methods
  									lowercase__							 =       diffusion.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       drift - diffusion**2 * model_output
  
  									#  equation 6: sample noise for the diffusion term of
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  									lowercase__							 =       sample - drift  # subtract because `dt` is a small negative timestep
  									# TODO is the variable diffusion the correct scaling term for the noise?
  									lowercase__							 =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g
  
  									if not return_dict:
  															return (prev_sample, prev_sample_mean, state)
  
  									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__       ,     prev_sample_mean=lowerCamelCase__       ,     state=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if state.timesteps is None:
  															raise ValueError(
  															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
  									# sample noise for correction
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  
  									# compute step size from the model_output, the noise, and the snr
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  									lowercase__							 =       step_size * jnp.ones(sample.shape[0]       )
  
  									# compute corrected sample: model_output term and noise term
  									lowercase__							 =       step_size.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       sample + step_size * model_output
  									lowercase__							 =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise
  
  									if not return_dict:
  															return (prev_sample, state)
  
  									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__       ,     state=lowerCamelCase__       )
  
  
  
  			def __len__(					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.config.num_train_timesteps
  
  
  
  
  
  
  