


'''simple docstring'''
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     (							snake_case__ ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       *UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							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__}'''
												)
												UpperCamelCase__       :List[Any]							 =			self.model.config
							else:
												UpperCamelCase__       :Union[str, Any]							 =			config

							UpperCamelCase__       :Any							 =			data_args
							UpperCamelCase__       :Tuple							 =			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:
												UpperCamelCase__       :str							 =			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id							)
							else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												UpperCamelCase__       :Optional[int]							 =			label_smoothed_nll_loss




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							if self.optimizer is None:
												UpperCamelCase__       :str							 =			["bias", "LayerNorm.weight"]
												UpperCamelCase__       :Tuple							 =			[
												    {
												        "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,
												    },
												]
												UpperCamelCase__       :List[Any]							 =			Adafactor if self.args.adafactor else AdamW
												if self.args.adafactor:
																	UpperCamelCase__       :str							 =			Adafactor
																	UpperCamelCase__       :Tuple							 =			{"scale_parameter": False, "relative_step": False}
												else:
																	UpperCamelCase__       :List[str]							 =			AdamW
																	UpperCamelCase__       :List[Any]							 =			{
																	    "betas": (self.args.adam_betaa, self.args.adam_betaa),
																	    "eps": self.args.adam_epsilon,
																	}
												UpperCamelCase__       :str							 =			self.args.learning_rate
												if self.sharded_ddp:
																	UpperCamelCase__       :List[Any]							 =			OSS(
																	    params=UpperCAmelCase_   ,       optim=UpperCAmelCase_   ,       **UpperCAmelCase_   ,       )
												else:
																	UpperCamelCase__       :Tuple							 =			optimizer_cls(UpperCAmelCase_   ,       **UpperCAmelCase_							)

							if self.lr_scheduler is None:
												UpperCamelCase__       :List[str]							 =			self._get_lr_scheduler(UpperCAmelCase_							)
							else:  # ignoring --lr_scheduler
												logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			arg_to_scheduler[self.args.lr_scheduler]
							if self.args.lr_scheduler == "constant":
												UpperCamelCase__       :Dict							 =			schedule_func(self.optimizer							)
							elif self.args.lr_scheduler == "constant_w_warmup":
												UpperCamelCase__       :Optional[int]							 =			schedule_func(self.optimizer   ,       num_warmup_steps=self.args.warmup_steps							)
							else:
												UpperCamelCase__       :Tuple							 =			schedule_func(
												    self.optimizer   ,       num_warmup_steps=self.args.warmup_steps   ,       num_training_steps=UpperCAmelCase_							)
							return scheduler




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							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   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							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
																	UpperCamelCase__       :str							 =			model(**UpperCAmelCase_   ,       use_cache=UpperCAmelCase_							)[0]
																	UpperCamelCase__       :Any							 =			self.loss_fn(logits.view(-1   ,       logits.shape[-1]							)   ,       labels.view(-1							)							)
												else:
																	# compute usual loss via models
																	UpperCamelCase__       :Dict							 =			model(**UpperCAmelCase_   ,       labels=UpperCAmelCase_   ,       use_cache=UpperCAmelCase_							)[:2]
							else:
												# compute label smoothed loss
												UpperCamelCase__       :Optional[int]							 =			model(**UpperCAmelCase_   ,       use_cache=UpperCAmelCase_							)[0]
												UpperCamelCase__       :str							 =			torch.nn.functional.log_softmax(UpperCAmelCase_   ,       dim=-1							)
												UpperCamelCase__       :int							 =			self.loss_fn(UpperCAmelCase_   ,       UpperCAmelCase_   ,       self.args.label_smoothing   ,       ignore_index=self.config.pad_token_id							)
							return loss, logits




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			inputs.pop('''labels'''							)
							UpperCamelCase__       :Dict							 =			self._compute_loss(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_							)
							return loss




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self._prepare_inputs(UpperCAmelCase_							)

							UpperCamelCase__       :str							 =			{
							    "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:
												UpperCamelCase__       :List[str]							 =			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"]:
																	UpperCamelCase__       :int							 =			self._pad_tensors_to_max_len(UpperCAmelCase_   ,       gen_kwargs['''max_length''']							)

							UpperCamelCase__       :Dict							 =			inputs.pop('''labels'''							)
							with torch.no_grad():
												# compute loss on predict data
												UpperCamelCase__       :Optional[int]							 =			self._compute_loss(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_							)

							UpperCamelCase__       :Optional[Any]							 =			loss.mean().detach()
							if self.args.prediction_loss_only:
												return (loss, None, None)

							UpperCamelCase__       :Union[str, Any]							 =			generated_tokens if self.args.predict_with_generate else logits

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

							return (loss, logits, labels)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			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}'''							)

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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import _LazyModule


__snake_case											=					{'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)