


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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class  snake_case_     :
					# setable values
					UpperCAmelCase__      :					Optional[int]					   =    None
					UpperCAmelCase__      :					Optional[jnp.ndarray]					   =    None
					UpperCAmelCase__      :					Optional[jnp.ndarray]					   =    None  # sigma(t_i)
					@classmethod
					def 				lowerCamelCase__(   cls     :Optional[int]   )      ->					Optional[Any]:
												return cls()







@dataclass
class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					jnp.ndarray
					UpperCAmelCase__      :					jnp.ndarray
					UpperCAmelCase__      :					KarrasVeSchedulerState







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					@property
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Optional[Any]:
												return True
					@register_to_config
					def __init__(   self     :Tuple    ,__snake_case     :float = 0.02    ,__snake_case     :float = 1_00    ,__snake_case     :float = 1.0_07    ,__snake_case     :float = 80    ,__snake_case     :float = 0.05    ,__snake_case     :float = 50    ,)      ->					str:
												pass
					def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
												return KarrasVeSchedulerState.create()
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :KarrasVeSchedulerState    ,__snake_case     :int    ,__snake_case     :Tuple = ()   )      ->					KarrasVeSchedulerState:
												a__    =  jnp.arange(0    ,__snake_case   )[::-1].copy()
												a__    =  [
												    (
												        self.config.sigma_max**2
												        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
												    )
												    for i in timesteps
												]

												return state.replace(
												    num_inference_steps=__snake_case    ,schedule=jnp.array(__snake_case    ,dtype=jnp.floataa   )    ,timesteps=__snake_case    ,)
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :KarrasVeSchedulerState    ,__snake_case     :jnp.ndarray    ,__snake_case     :float    ,__snake_case     :random.KeyArray    ,)      ->					Tuple[jnp.ndarray, float]:
												if self.config.s_min <= sigma <= self.config.s_max:
																			a__    =  min(self.config.s_churn / state.num_inference_steps    ,2**0.5 - 1   )
												else:
																			a__    =  0

												# sample eps ~ N(0, S_noise^2 * I)
												a__    =  random.split(__snake_case    ,num=1   )
												a__    =  self.config.s_noise * random.normal(key=__snake_case    ,shape=sample.shape   )
												a__    =  sigma + gamma * sigma
												a__    =  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

												return sample_hat, sigma_hat
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :KarrasVeSchedulerState    ,__snake_case     :jnp.ndarray    ,__snake_case     :float    ,__snake_case     :float    ,__snake_case     :jnp.ndarray    ,__snake_case     :bool = True    ,)      ->					Union[FlaxKarrasVeOutput, Tuple]:
												a__    =  sample_hat + sigma_hat * model_output
												a__    =  (sample_hat - pred_original_sample) / sigma_hat
												a__    =  sample_hat + (sigma_prev - sigma_hat) * derivative

												if not return_dict:
																			return (sample_prev, derivative, state)

												return FlaxKarrasVeOutput(prev_sample=__snake_case    ,derivative=__snake_case    ,state=__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :KarrasVeSchedulerState    ,__snake_case     :jnp.ndarray    ,__snake_case     :float    ,__snake_case     :float    ,__snake_case     :jnp.ndarray    ,__snake_case     :jnp.ndarray    ,__snake_case     :jnp.ndarray    ,__snake_case     :bool = True    ,)      ->					Union[FlaxKarrasVeOutput, Tuple]:
												a__    =  sample_prev + sigma_prev * model_output
												a__    =  (sample_prev - pred_original_sample) / sigma_prev
												a__    =  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

												if not return_dict:
																			return (sample_prev, derivative, state)

												return FlaxKarrasVeOutput(prev_sample=__snake_case    ,derivative=__snake_case    ,state=__snake_case   )



					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :KarrasVeSchedulerState    ,__snake_case     :int    ,__snake_case     :str    ,__snake_case     :List[str]   )      ->					Optional[int]:
												raise NotImplementedError()








from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
						from fairscale.optim import OSS


snake_case							: Any      =   logging.get_logger(__name__)

snake_case							: Tuple      =   {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}







class  snake_case_     (lowerCamelCase_   ):
					def __init__(   self     :str    ,__snake_case     :Dict=None    ,__snake_case     :int=None    ,*__snake_case     :str    ,**__snake_case     :Union[str, Any]   )      ->					Tuple:
												super().__init__(*__snake_case    ,**__snake_case   )

												if config is None:
																			assert isinstance(self.model    ,__snake_case   ), (
																			    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
																			    F' {self.model.__class__}'
																			)
																			a__    =  self.model.config
												else:
																			a__    =  config

												a__    =  data_args
												a__    =  self.config.tgt_vocab_size if isinstance(self.config    ,__snake_case   ) else self.config.vocab_size

												if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
																			assert self.config.pad_token_id is not None, (
																			    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
																			    " calculation or doing label smoothing."
																			)

												if self.config.pad_token_id is None and self.config.eos_token_id is not None:
																			logger.warning(
																			    F'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
																			    ' padding..'   )

												if self.args.label_smoothing == 0:
																			a__    =  torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id   )
												else:
																			# dynamically import label_smoothed_nll_loss
																			from utils import label_smoothed_nll_loss

																			a__    =  label_smoothed_nll_loss
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :int   )      ->					Tuple:
												if self.optimizer is None:
																			a__    =  ['bias', 'LayerNorm.weight']
																			a__    =  [
																			    {
																			        'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay   )],
																			        'weight_decay': self.args.weight_decay,
																			    },
																			    {
																			        'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay   )],
																			        'weight_decay': 0.0,
																			    },
																			]
																			a__    =  Adafactor if self.args.adafactor else AdamW
																			if self.args.adafactor:
																										a__    =  Adafactor
																										a__    =  {'scale_parameter': False, 'relative_step': False}
																			else:
																										a__    =  AdamW
																										a__    =  {
																										    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																										    'eps': self.args.adam_epsilon,
																										}
																			a__    =  self.args.learning_rate
																			if self.sharded_ddp:
																										a__    =  OSS(
																										    params=__snake_case    ,optim=__snake_case    ,**__snake_case    ,)
																			else:
																										a__    =  optimizer_cls(__snake_case    ,**__snake_case   )

												if self.lr_scheduler is None:
																			a__    =  self._get_lr_scheduler(__snake_case   )
												else:  # ignoring --lr_scheduler
																			logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'   )
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :List[str]   )      ->					Union[str, Any]:
												a__    =  arg_to_scheduler[self.args.lr_scheduler]
												if self.args.lr_scheduler == "constant":
																			a__    =  schedule_func(self.optimizer   )
												elif self.args.lr_scheduler == "constant_w_warmup":
																			a__    =  schedule_func(self.optimizer    ,num_warmup_steps=self.args.warmup_steps   )
												else:
																			a__    =  schedule_func(
																			    self.optimizer    ,num_warmup_steps=self.args.warmup_steps    ,num_training_steps=__snake_case   )
												return scheduler
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Optional[torch.utils.data.Sampler]:
												if isinstance(self.train_dataset    ,torch.utils.data.IterableDataset   ):
																			return None
												elif is_torch_tpu_available():
																			return get_tpu_sampler(self.train_dataset   )
												else:
																			if self.args.sortish_sampler:
																										self.train_dataset.make_sortish_sampler(
																										    self.args.per_device_train_batch_size    ,distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)    ,)

																			return (
																			    RandomSampler(self.train_dataset   )
																			    if self.args.local_rank == -1
																			    else DistributedSampler(self.train_dataset   )
																			)
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Optional[int]    ,__snake_case     :List[Any]    ,__snake_case     :Any   )      ->					Optional[Any]:
												if self.args.label_smoothing == 0:
																			if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																										# force training to ignore pad token
																										a__    =  model(**__snake_case    ,use_cache=__snake_case   )[0]
																										a__    =  self.loss_fn(logits.view(-1    ,logits.shape[-1]   )    ,labels.view(-1   )   )
																			else:
																										# compute usual loss via models
																										a__   ,      a__    =  model(**__snake_case    ,labels=__snake_case    ,use_cache=__snake_case   )[:2]
												else:
																			# compute label smoothed loss
																			a__    =  model(**__snake_case    ,use_cache=__snake_case   )[0]
																			a__    =  torch.nn.functional.log_softmax(__snake_case    ,dim=-1   )
																			a__   ,      a__    =  self.loss_fn(__snake_case    ,__snake_case    ,self.args.label_smoothing    ,ignore_index=self.config.pad_token_id   )
												return loss, logits
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Dict    ,__snake_case     :Optional[int]   )      ->					Any:
												a__    =  inputs.pop('labels'   )
												a__   ,      a__    =  self._compute_loss(__snake_case    ,__snake_case    ,__snake_case   )
												return loss
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :nn.Module    ,__snake_case     :Dict[str, Union[torch.Tensor, Any]]    ,__snake_case     :bool    ,__snake_case     :Optional[List[str]] = None    ,)      ->					Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
												a__    =  self._prepare_inputs(__snake_case   )

												a__    =  {
												    'max_length': self.data_args.val_max_target_length
												    if self.data_args is not None
												    else self.config.max_length,
												    'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
												}

												if self.args.predict_with_generate and not self.args.prediction_loss_only:
																			a__    =  self.model.generate(
																			    inputs['input_ids']    ,attention_mask=inputs['attention_mask']    ,**__snake_case    ,)
																			# in case the batch is shorter than max length, the output should be padded
																			if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																										a__    =  self._pad_tensors_to_max_len(__snake_case    ,gen_kwargs['max_length']   )

												a__    =  inputs.pop('labels'   )
												with torch.no_grad():
																			# compute loss on predict data
																			a__   ,      a__    =  self._compute_loss(__snake_case    ,__snake_case    ,__snake_case   )

												a__    =  loss.mean().detach()
												if self.args.prediction_loss_only:
																			return (loss, None, None)

												a__    =  generated_tokens if self.args.predict_with_generate else logits

												if labels.shape[-1] < gen_kwargs["max_length"]:
																			a__    =  self._pad_tensors_to_max_len(__snake_case    ,gen_kwargs['max_length']   )

												return (loss, logits, labels)



					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :Optional[Any]    ,__snake_case     :Union[str, Any]   )      ->					int:
												# If PAD token is not defined at least EOS token has to be defined
												a__    =  self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

												if pad_token_id is None:
																			raise ValueError(
																			    'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
																			    F' padded to `max_length`={max_length}'   )

												a__    =  pad_token_id * torch.ones(
												    (tensor.shape[0], max_length)    ,dtype=tensor.dtype    ,device=tensor.device   )
												a__    =  tensor
												return padded_tensor




