





"""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						: Dict     	=			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				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					*lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> int:
								super().__init__(*lowerCAmelCase__      ,					**lowerCAmelCase__      )

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

								a      :		Dict	=				data_args
								a      :		Tuple	=				self.config.tgt_vocab_size if isinstance(self.config      ,					lowerCAmelCase__      ) 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      :		List[Any]	=				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      :		Any	=				label_smoothed_nll_loss






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

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






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Dict:
								a      :		Any	=				arg_to_scheduler[self.args.lr_scheduler]
								if self.args.lr_scheduler == "constant":
									a      :		List[Any]	=				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      :		Dict	=				schedule_func(
									    self.optimizer      ,					num_warmup_steps=self.args.warmup_steps      ,					num_training_steps=lowerCAmelCase__      )
								return scheduler






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






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> List[Any]:
								a      :		int	=				inputs.pop("labels"      )
								a,       a      :		Tuple	=				self._compute_loss(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )
								return loss






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					)	-> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
								a      :		Dict	=				self._prepare_inputs(lowerCAmelCase__      )

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

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

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

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

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

								return (loss, logits, labels)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> str:
								# If PAD token is not defined at least EOS token has to be defined
								a      :		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}"""      )

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









"""simple docstring"""




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


a						: List[str]     	=			logging.get_logger(__name__)

a						: List[Any]     	=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a						: str     	=			{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

a						: Tuple     	=			{'''allegro/herbert-base-cased''': 514}
a						: Optional[int]     	=			{}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					str      					=VOCAB_FILES_NAMES
							lowerCamelCase   :					Dict      					=PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase   :					Dict      					=PRETRAINED_INIT_CONFIGURATION
							lowerCamelCase   :					int      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase   :					Optional[Any]      					=HerbertTokenizer






							def __init__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="<unk>"      ,					lowerCAmelCase__="<pad>"      ,					lowerCAmelCase__="<mask>"      ,					lowerCAmelCase__="</s>"      ,					**lowerCAmelCase__      ,					)	-> Optional[int]:
								super().__init__(
								    lowerCAmelCase__      ,					lowerCAmelCase__      ,					tokenizer_file=lowerCAmelCase__      ,					cls_token=lowerCAmelCase__      ,					unk_token=lowerCAmelCase__      ,					pad_token=lowerCAmelCase__      ,					mask_token=lowerCAmelCase__      ,					sep_token=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Optional[Any]	=				[self.cls_token_id]
								a      :		Any	=				[self.sep_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep

								return cls + token_ids_a + sep + token_ids_a + sep






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      )	-> List[int]:
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowerCAmelCase__      ,					token_ids_a=lowerCAmelCase__      ,					already_has_special_tokens=lowerCAmelCase__      )

								if token_ids_a is None:
									return [1] + ([0] * len(lowerCAmelCase__      )) + [1]
								return [1] + ([0] * len(lowerCAmelCase__      )) + [1] + ([0] * len(lowerCAmelCase__      )) + [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Dict	=				[self.sep_token_id]
								a      :		Tuple	=				[self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep      ) * [0]
								return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Tuple[str]:
								a      :		List[str]	=				self._tokenizer.model.save(lowerCAmelCase__      ,					name=lowerCAmelCase__      )
								return tuple(lowerCAmelCase__      )


