


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


snake_case				:    Union[str, Any]       				=     {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    List[str]       				=     ['''SpeechEncoderDecoderModel''']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Optional[Any]       				=     ['''FlaxSpeechEncoderDecoderModel''']

if TYPE_CHECKING:
     from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
     import sys

     snake_case				:    str       				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







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								=  logging.get_logger(__name__)

__snake_case								=  {
    """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 						lowercase__  (						_UpperCAmelCase  ):
	def __init__(			self							:   str       ,   UpperCAmelCase_							:   int=None       ,   UpperCAmelCase_							:   List[str]=None       ,   *UpperCAmelCase_							:   Union[str, Any]       ,   **UpperCAmelCase_							:   Optional[Any]							):
						super().__init__(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

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

						SCREAMING_SNAKE_CASE__													= data_args
						SCREAMING_SNAKE_CASE__													= self.config.tgt_vocab_size if isinstance(self.config       ,   UpperCAmelCase_							) 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:
											SCREAMING_SNAKE_CASE__													= torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id							)
						else:
											# dynamically import label_smoothed_nll_loss
											from utils import label_smoothed_nll_loss

											SCREAMING_SNAKE_CASE__													= label_smoothed_nll_loss
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   int							):
						if self.optimizer is None:
											SCREAMING_SNAKE_CASE__													= ['bias', 'LayerNorm.weight']
											SCREAMING_SNAKE_CASE__													= [
											    {
											        '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,
											    },
											]
											SCREAMING_SNAKE_CASE__													= Adafactor if self.args.adafactor else AdamW
											if self.args.adafactor:
																SCREAMING_SNAKE_CASE__													= Adafactor
																SCREAMING_SNAKE_CASE__													= {'scale_parameter': False, 'relative_step': False}
											else:
																SCREAMING_SNAKE_CASE__													= AdamW
																SCREAMING_SNAKE_CASE__													= {
																    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																    'eps': self.args.adam_epsilon,
																}
											SCREAMING_SNAKE_CASE__													= self.args.learning_rate
											if self.sharded_ddp:
																SCREAMING_SNAKE_CASE__													= OSS(
																    params=UpperCAmelCase_       ,   optim=UpperCAmelCase_       ,   **UpperCAmelCase_       ,   )
											else:
																SCREAMING_SNAKE_CASE__													= optimizer_cls(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if self.lr_scheduler is None:
											SCREAMING_SNAKE_CASE__													= self._get_lr_scheduler(UpperCAmelCase_							)
						else:  # ignoring --lr_scheduler
											logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'							)
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= arg_to_scheduler[self.args.lr_scheduler]
						if self.args.lr_scheduler == "constant":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer							)
						elif self.args.lr_scheduler == "constant_w_warmup":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer       ,   num_warmup_steps=self.args.warmup_steps							)
						else:
											SCREAMING_SNAKE_CASE__													= schedule_func(
											    self.optimizer       ,   num_warmup_steps=self.args.warmup_steps       ,   num_training_steps=UpperCAmelCase_							)
						return scheduler
	def      A_				(			self							:   List[str]							):
						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      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Dict							):
						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
																SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
																SCREAMING_SNAKE_CASE__													= self.loss_fn(logits.view(-1       ,   logits.shape[-1]							)       ,   labels.view(-1							)							)
											else:
																# compute usual loss via models
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   labels=UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[:2]
						else:
											# compute label smoothed loss
											SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
											SCREAMING_SNAKE_CASE__													= torch.nn.functional.log_softmax(UpperCAmelCase_       ,   dim=-1							)
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.loss_fn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.args.label_smoothing       ,   ignore_index=self.config.pad_token_id							)
						return loss, logits
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= inputs.pop('labels'							)
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._compute_loss(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						return loss
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   nn.Module       ,   UpperCAmelCase_							:   Dict[str, Union[torch.Tensor, Any]]       ,   UpperCAmelCase_							:   bool       ,   UpperCAmelCase_							:   Optional[List[str]] = None       ,   ):
						SCREAMING_SNAKE_CASE__													= self._prepare_inputs(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= {
						    '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:
											SCREAMING_SNAKE_CASE__													= self.model.generate(
											    inputs['input_ids']       ,   attention_mask=inputs['attention_mask']       ,   **UpperCAmelCase_       ,   )
											# in case the batch is shorter than max length, the output should be padded
											if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																SCREAMING_SNAKE_CASE__													= self._pad_tensors_to_max_len(UpperCAmelCase_       ,   gen_kwargs['max_length']							)

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

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

						SCREAMING_SNAKE_CASE__													= generated_tokens if self.args.predict_with_generate else logits

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

						return (loss, logits, labels)





	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   List[str]							):
						# If PAD token is not defined at least EOS token has to be defined
						SCREAMING_SNAKE_CASE__													= 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}'							)

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





