
'''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








'''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






