code: 
  '''simple docstring'''
  
  
  
  from copy import deepcopy
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
  
  import torch
  from torch import nn
  from torch.utils.data import Dataset
  
  from .deepspeed import is_deepspeed_zeroa_enabled
  from .generation.configuration_utils import GenerationConfig
  from .trainer import Trainer
  from .utils import logging
  
  
  if TYPE_CHECKING:
  				from .data.data_collator import DataCollator
  				from .modeling_utils import PreTrainedModel
  				from .tokenization_utils_base import PreTrainedTokenizerBase
  				from .trainer_callback import TrainerCallback
  				from .trainer_utils import EvalPrediction, PredictionOutput
  				from .training_args import TrainingArguments
  
  
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = (None, None)       ,     lowerCamelCase__ = None       ,     )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__(
  									    model=lowerCamelCase__       ,     args=lowerCamelCase__       ,     data_collator=lowerCamelCase__       ,     train_dataset=lowerCamelCase__       ,     eval_dataset=lowerCamelCase__       ,     tokenizer=lowerCamelCase__       ,     model_init=lowerCamelCase__       ,     compute_metrics=lowerCamelCase__       ,     callbacks=lowerCamelCase__       ,     optimizers=lowerCamelCase__       ,     preprocess_logits_for_metrics=lowerCamelCase__       ,     )
  
  									# Override self.model.generation_config if a GenerationConfig is specified in args.
  									# Priority: args.generation_config > model.generation_config > default GenerationConfig.
  									if self.args.generation_config is not None:
  															lowercase__							 =       self.load_generation_config(self.args.generation_config       )
  															lowercase__							 =       gen_config
  
  
  
  			@staticmethod
  			def A__ (					lowerCamelCase__       )			->		GenerationConfig:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  															return deepcopy(lowerCamelCase__       )
  
  									# str or Path
  									lowercase__							 =       Path(lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else gen_config_arg
  									lowercase__							 =       None
  
  									# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
  									# This step is required in order to determine config_file_name
  									if pretrained_model_name.is_file():
  															lowercase__							 =       pretrained_model_name.name
  															lowercase__							 =       pretrained_model_name.parent
  									# dir path
  									elif pretrained_model_name.is_dir():
  															pass
  									# model id or URL
  									else:
  															lowercase__							 =       gen_config_arg
  
  									lowercase__							 =       GenerationConfig.from_pretrained(lowerCamelCase__       ,     lowerCamelCase__       )
  									return gen_config
  
  
  
  			def A__ (					self       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = "eval"       ,     **lowerCamelCase__       ,     )			->		Dict[str, float]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       gen_kwargs.copy()
  									if gen_kwargs.get("""max_length"""       ) is None and gen_kwargs.get("""max_new_tokens"""       ) is None:
  															lowercase__							 =       self.args.generation_max_length
  									lowercase__							 =       (
  									    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""       ) is not None else self.args.generation_num_beams
  									)
  									lowercase__							 =       gen_kwargs
  
  									return super().evaluate(lowerCamelCase__       ,     ignore_keys=lowerCamelCase__       ,     metric_key_prefix=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = "test"       ,     **lowerCamelCase__       ,     )			->		"PredictionOutput":
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       gen_kwargs.copy()
  									if gen_kwargs.get("""max_length"""       ) is None and gen_kwargs.get("""max_new_tokens"""       ) is None:
  															lowercase__							 =       self.args.generation_max_length
  									lowercase__							 =       (
  									    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""       ) is not None else self.args.generation_num_beams
  									)
  									lowercase__							 =       gen_kwargs
  
  									return super().predict(lowerCamelCase__       ,     ignore_keys=lowerCamelCase__       ,     metric_key_prefix=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     **lowerCamelCase__       ,     )			->		Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if not self.args.predict_with_generate or prediction_loss_only:
  															return super().prediction_step(
  															    lowerCamelCase__       ,     lowerCamelCase__       ,     prediction_loss_only=lowerCamelCase__       ,     ignore_keys=lowerCamelCase__       )
  
  									lowercase__							 =       """labels""" in inputs
  									lowercase__							 =       self._prepare_inputs(lowerCamelCase__       )
  
  									# XXX: adapt synced_gpus for fairscale as well
  									# Priority (handled in generate):
  									# gen_kwargs > model.generation_config > default GenerationConfig()
  
  									if len(lowerCamelCase__       ) == 0 and hasattr(self       ,     """_gen_kwargs"""       ):
  															lowercase__							 =       self._gen_kwargs.copy()
  
  									if gen_kwargs.get("""max_length"""       ) is None and gen_kwargs.get("""max_new_tokens"""       ) is None:
  															lowercase__							 =       self.model.config.max_length
  									lowercase__							 =       (
  									    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""       ) is not None else self.model.config.num_beams
  									)
  									lowercase__							 =       True if is_deepspeed_zeroa_enabled() else False
  									lowercase__							 =       (
  									    gen_kwargs["""synced_gpus"""] if gen_kwargs.get("""synced_gpus"""       ) is not None else default_synced_gpus
  									)
  
  									# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
  									# (otherwise, it would continue generating from the padded `decoder_input_ids`)
  									if (
  									    "labels" in inputs
  									    and "decoder_input_ids" in inputs
  									    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
  									):
  															lowercase__							 =       {k: v for k, v in inputs.items() if k != """decoder_input_ids"""}
  									lowercase__							 =       self.model.generate(**lowerCamelCase__       ,     **lowerCamelCase__       )
  
  									# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
  									# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
  									# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
  									if self.model.generation_config._from_model_config:
  															lowercase__							 =       False
  
  									# Retrieves GenerationConfig from model.generation_config
  									lowercase__							 =       self.model.generation_config
  									# in case the batch is shorter than max length, the output should be padded
  									if generated_tokens.shape[-1] < gen_config.max_length:
  															lowercase__							 =       self._pad_tensors_to_max_len(lowerCamelCase__       ,     gen_config.max_length       )
  									elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
  															lowercase__							 =       self._pad_tensors_to_max_len(lowerCamelCase__       ,     gen_config.max_new_tokens + 1       )
  
  									with torch.no_grad():
  															if has_labels:
  																					with self.compute_loss_context_manager():
  																											lowercase__							 =       model(**lowerCamelCase__       )
  																					if self.label_smoother is not None:
  																											lowercase__							 =       self.label_smoother(lowerCamelCase__       ,     inputs["""labels"""]       ).mean().detach()
  																					else:
  																											lowercase__							 =       (outputs["""loss"""] if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else outputs[0]).mean().detach()
  															else:
  																					lowercase__							 =       None
  
  									if self.args.prediction_loss_only:
  															return loss, None, None
  
  									if has_labels:
  															lowercase__							 =       inputs["""labels"""]
  															if labels.shape[-1] < gen_config.max_length:
  																					lowercase__							 =       self._pad_tensors_to_max_len(lowerCamelCase__       ,     gen_config.max_length       )
  															elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
  																					lowercase__							 =       self._pad_tensors_to_max_len(lowerCamelCase__       ,     gen_config.max_new_tokens + 1       )
  									else:
  															lowercase__							 =       None
  
  									return loss, generated_tokens, labels
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self.tokenizer is not None and hasattr(self.tokenizer       ,     """pad_token_id"""       ):
  															# If PAD token is not defined at least EOS token has to be defined
  															lowercase__							 =       (
  															    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
  															)
  									else:
  															if self.model.config.pad_token_id is not None:
  																					lowercase__							 =       self.model.config.pad_token_id
  															else:
  																					raise ValueError("""Pad_token_id must be set in the configuration of the model, in order to pad tensors"""       )
  
  									lowercase__							 =       pad_token_id * torch.ones(
  									    (tensor.shape[0], max_length)       ,     dtype=tensor.dtype       ,     device=tensor.device       )
  									lowercase__							 =       tensor
  									return padded_tensor
  
  
  
  
  
  
  
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
  
  
  
  
  
  
  