
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


lowercase						:      Optional[int]									= logging.get_logger(__name__)

lowercase						:      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 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:Union[str, Any]  ,     a	:List[Any]=None  ,     a	:Any=None  ,     *a	:Dict  ,     **a	:int		)	->				Dict:
								super().__init__(*a  ,     **a		)

								if config is None:
															assert isinstance(self.model  ,     a		), (
															    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
															    f' {self.model.__class__}'
															)
															__UpperCamelCase			:     Optional[Any]         =      self.model.config
								else:
															__UpperCamelCase			:     Tuple         =      config

								__UpperCamelCase			:     int         =      data_args
								__UpperCamelCase			:     Union[str, Any]         =      self.config.tgt_vocab_size if isinstance(self.config  ,     a		) 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			:     Dict         =      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			:     List[str]         =      label_smoothed_nll_loss







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:int		)	->				Union[str, Any]:
								if self.optimizer is None:
															__UpperCamelCase			:     str         =      ["bias", "LayerNorm.weight"]
															__UpperCamelCase			:     Any         =      [
															    {
															        "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			:     Dict         =      Adafactor if self.args.adafactor else AdamW
															if self.args.adafactor:
																						__UpperCamelCase			:     Dict         =      Adafactor
																						__UpperCamelCase			:     Tuple         =      {"scale_parameter": False, "relative_step": False}
															else:
																						__UpperCamelCase			:     Union[str, Any]         =      AdamW
																						__UpperCamelCase			:     List[Any]         =      {
																						    "betas": (self.args.adam_betaa, self.args.adam_betaa),
																						    "eps": self.args.adam_epsilon,
																						}
															__UpperCamelCase			:     Any         =      self.args.learning_rate
															if self.sharded_ddp:
																						__UpperCamelCase			:     Dict         =      OSS(
																						    params=a  ,     optim=a  ,     **a  ,     )
															else:
																						__UpperCamelCase			:     Tuple         =      optimizer_cls(a  ,     **a		)

								if self.lr_scheduler is None:
															__UpperCamelCase			:     int         =      self._get_lr_scheduler(a		)
								else:  # ignoring --lr_scheduler
															logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."		)







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:str		)	->				Any:
								__UpperCamelCase			:     str         =      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			:     Union[str, Any]         =      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=a		)
								return scheduler







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







	def      _lowerCamelCase       (      self	:Any  ,     a	:str  ,     a	:Optional[Any]		)	->				int:
								__UpperCamelCase			:     Dict         =      inputs.pop("labels"		)
								__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      self._compute_loss(a  ,     a  ,     a		)
								return loss







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:nn.Module  ,     a	:Dict[str, Union[torch.Tensor, Any]]  ,     a	:bool  ,     a	:Optional[List[str]] = None  ,     )	->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
								__UpperCamelCase			:     Union[str, Any]         =      self._prepare_inputs(a		)

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

								__UpperCamelCase			:     Any         =      inputs.pop("labels"		)
								with torch.no_grad():
															# compute loss on predict data
															__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      self._compute_loss(a  ,     a  ,     a		)

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

								__UpperCamelCase			:     Optional[int]         =      generated_tokens if self.args.predict_with_generate else logits

								if labels.shape[-1] < gen_kwargs["max_length"]:
															__UpperCamelCase			:     str         =      self._pad_tensors_to_max_len(a  ,     gen_kwargs["max_length"]		)

								return (loss, logits, labels)







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:Tuple  ,     a	:int		)	->				Optional[int]:
								# If PAD token is not defined at least EOS token has to be defined
								__UpperCamelCase			:     Optional[int]         =      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			:     Union[str, Any]         =      pad_token_id * torch.ones(
								    (tensor.shape[0], max_length)  ,     dtype=tensor.dtype  ,     device=tensor.device		)
								__UpperCamelCase			:     int         =      tensor
								return padded_tensor

from typing import TYPE_CHECKING

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


lowercase						:      str									= {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      str									= ['VisionEncoderDecoderModel']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      List[Any]									= ['TFVisionEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      int									= ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)