


'''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


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {
    '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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			List[str]=None  ,						__SCREAMING_SNAKE_CASE      :			Any=None  ,						*__SCREAMING_SNAKE_CASE      :			int  ,						**__SCREAMING_SNAKE_CASE      :			Any					)			->			Optional[Any]:
							super().__init__(*__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)

							if config is None:
											assert isinstance(self.model  ,						__SCREAMING_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_		:  int											=      self.model.config
							else:
											a_		:  Optional[int]											=      config

							a_		:  Dict											=      data_args
							a_		:  Optional[Any]											=      self.config.tgt_vocab_size if isinstance(self.config  ,						__SCREAMING_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_		:  Optional[int]											=      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_		:  Dict											=      label_smoothed_nll_loss






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

							if self.lr_scheduler is None:
											a_		:  Optional[int]											=      self._get_lr_scheduler(__SCREAMING_SNAKE_CASE					)
							else:  # ignoring --lr_scheduler
											logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			List[str]:
							a_		:  str											=      arg_to_scheduler[self.args.lr_scheduler]
							if self.args.lr_scheduler == "constant":
											a_		:  Tuple											=      schedule_func(self.optimizer					)
							elif self.args.lr_scheduler == "constant_w_warmup":
											a_		:  Dict											=      schedule_func(self.optimizer  ,						num_warmup_steps=self.args.warmup_steps					)
							else:
											a_		:  Any											=      schedule_func(
											    self.optimizer  ,						num_warmup_steps=self.args.warmup_steps  ,						num_training_steps=__SCREAMING_SNAKE_CASE					)
							return scheduler






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			int:
							a_		:  Tuple											=      inputs.pop('''labels'''					)
							a_		:  Union[str, Any]											=      self._compute_loss(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							return loss






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			nn.Module  ,						__SCREAMING_SNAKE_CASE      :			Dict[str, Union[torch.Tensor, Any]]  ,						__SCREAMING_SNAKE_CASE      :			bool  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[str]] = None  ,						)			->			Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
							a_		:  Optional[int]											=      self._prepare_inputs(__SCREAMING_SNAKE_CASE					)

							a_		:  List[Any]											=      {
							    '''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_		:  str											=      self.model.generate(
											    inputs['''input_ids''']  ,						attention_mask=inputs['''attention_mask''']  ,						**__SCREAMING_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_		:  Dict											=      self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE  ,						gen_kwargs['''max_length''']					)

							a_		:  Tuple											=      inputs.pop('''labels'''					)
							with torch.no_grad():
											# compute loss on predict data
											a_		:  Union[str, Any]											=      self._compute_loss(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

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

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

							if labels.shape[-1] < gen_kwargs["max_length"]:
											a_		:  Union[str, Any]											=      self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE  ,						gen_kwargs['''max_length''']					)

							return (loss, logits, labels)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			List[str]:
							# If PAD token is not defined at least EOS token has to be defined
							a_		:  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}'					)

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












'''simple docstring'''







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE_       )
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				field(default="image-classification"	,   metadata={"include_in_asdict_even_if_is_default": True}       )
			snake_case__  =				Features({"image": Image()}       )
			snake_case__  =				Features({"labels": ClassLabel}       )
			snake_case__  =				"image"
			snake_case__  =				"labels"






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							if self.label_column not in features:
											raise ValueError(f'Column {self.label_column} is not present in features.'					)
							if not isinstance(features[self.label_column]  ,						__SCREAMING_SNAKE_CASE					):
											raise ValueError(f'Column {self.label_column} is not a ClassLabel.'					)
							a_		:  Optional[int]											=      copy.deepcopy(self					)
							a_		:  int											=      self.label_schema.copy()
							a_		:  Tuple											=      features[self.label_column]
							a_		:  str											=      label_schema
							return task_template







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}




