
'''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


a__						:      Any   				=				logging.get_logger(__name__)

a__						:      Union[str, Any]   				=				{
    '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        UpperCamelCase__     (			UpperCamelCase_):






 def __init__(	self     :str		, _A     :List[str]=None		, _A     :Any=None		, *_A     :int		, **_A     :Tuple	) ->	Any:







       '''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__}'
             )
             __A						=	self.model.config
       else:
             __A						=	config

       __A						=	data_args
       __A						=	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:
             __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 		lowercase_  (	self     :Union[str, Any]		, _A     :int	) ->	Optional[int]:







       '''simple docstring'''



       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=UpperCamelCase__		, optim=UpperCamelCase__		, **UpperCamelCase__		, )
             else:
                   __A						=	optimizer_cls(UpperCamelCase__		, **UpperCamelCase__	)

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






 def 		lowercase_  (	self     :Tuple		, _A     :int	) ->	List[str]:







       '''simple docstring'''



       __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=UpperCamelCase__	)
       return scheduler






 def 		lowercase_  (	self     :Any	) ->	int:







       '''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 		lowercase_  (	self     :Optional[int]		, _A     :Union[str, Any]		, _A     :List[str]		, _A     :List[Any]	) ->	List[Any]:







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






 def 		lowercase_  (	self     :Union[str, Any]		, _A     :Union[str, Any]		, _A     :Optional[int]	) ->	Any:







       '''simple docstring'''



       __A						=	inputs.pop('labels'	)
       __A						=	self._compute_loss(UpperCamelCase__		, UpperCamelCase__		, UpperCamelCase__	)
       return loss






 def 		lowercase_  (	self     :Any		, _A     :nn.Module		, _A     :Dict[str, Union[torch.Tensor, Any]]		, _A     :bool		, _A     :Optional[List[str]] = None		, ) ->	Optional[int]:







       '''simple docstring'''



       __A						=	self._prepare_inputs(UpperCamelCase__	)

       __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']		, **UpperCamelCase__		, )
             # 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(UpperCamelCase__		, gen_kwargs['max_length']	)

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

       __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(UpperCamelCase__		, gen_kwargs['max_length']	)

       return (loss, logits, labels)






 def 		lowercase_  (	self     :List[str]		, _A     :Union[str, Any]		, _A     :Union[str, Any]	) ->	Optional[Any]:







       '''simple docstring'''



       __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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase		:       Any									=     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     ['MBartTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Dict									=     ['MBartTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MBartForCausalLM',
					    'MBartForConditionalGeneration',
					    'MBartForQuestionAnswering',
					    'MBartForSequenceClassification',
					    'MBartModel',
					    'MBartPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'TFMBartForConditionalGeneration',
					    'TFMBartModel',
					    'TFMBartPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'FlaxMBartForConditionalGeneration',
					    'FlaxMBartForQuestionAnswering',
					    'FlaxMBartForSequenceClassification',
					    'FlaxMBartModel',
					    'FlaxMBartPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart import MBartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart_fast import MBartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mbart import (
										    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MBartForCausalLM,
										    MBartForConditionalGeneration,
										    MBartForQuestionAnswering,
										    MBartForSequenceClassification,
										    MBartModel,
										    MBartPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_mbart import (
										    FlaxMBartForConditionalGeneration,
										    FlaxMBartForQuestionAnswering,
										    FlaxMBartForSequenceClassification,
										    FlaxMBartModel,
										    FlaxMBartPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





