



"""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_			   =					logging.get_logger(__name__)

a_			   =					{
    '''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     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		*lowercase__   ,		**lowercase__       ):
											super().__init__(*lowercase__   ,		**lowercase__       )

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

											snake_case_					:		Any 					=					data_args
											snake_case_					:		Union[str, Any] 					=					self.config.tgt_vocab_size if isinstance(self.config   ,		lowercase__       ) 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:
																snake_case_					:		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

																snake_case_					:		Optional[int] 					=					label_smoothed_nll_loss







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

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







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		str 					=					arg_to_scheduler[self.args.lr_scheduler]
											if self.args.lr_scheduler == "constant":
																snake_case_					:		Tuple 					=					schedule_func(self.optimizer       )
											elif self.args.lr_scheduler == "constant_w_warmup":
																snake_case_					:		Union[str, Any] 					=					schedule_func(self.optimizer   ,		num_warmup_steps=self.args.warmup_steps       )
											else:
																snake_case_					:		Optional[Any] 					=					schedule_func(
																    self.optimizer   ,		num_warmup_steps=self.args.warmup_steps   ,		num_training_steps=lowercase__       )
											return scheduler







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







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					inputs.pop("""labels"""       )
											snake_case_					,			snake_case_					:		Union[str, Any] 					=					self._compute_loss(lowercase__   ,		lowercase__   ,		lowercase__       )
											return loss







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		):
											snake_case_					:		int 					=					self._prepare_inputs(lowercase__       )

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

											snake_case_					:		int 					=					inputs.pop("""labels"""       )
											with torch.no_grad():
																# compute loss on predict data
																snake_case_					,			snake_case_					:		str 					=					self._compute_loss(lowercase__   ,		lowercase__   ,		lowercase__       )

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

											snake_case_					:		str 					=					generated_tokens if self.args.predict_with_generate else logits

											if labels.shape[-1] < gen_kwargs["max_length"]:
																snake_case_					:		Optional[Any] 					=					self._pad_tensors_to_max_len(lowercase__   ,		gen_kwargs["""max_length"""]       )

											return (loss, logits, labels)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											# If PAD token is not defined at least EOS token has to be defined
											snake_case_					:		Tuple 					=					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}'       )

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









"""simple docstring"""

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


a_			   =					logging.getLogger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__=-1       ):
											# in NER datasets, the last column is usually reserved for NER label
											snake_case_					:		Union[str, Any] 					=					label_idx







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[str] 					=					mode.value
											snake_case_					:		List[Any] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		Any 					=					[]
											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																snake_case_					:		str 					=					[]
																snake_case_					:		List[Any] 					=					[]
																for line in f:
																					if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																										if words:
																															examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																															guid_index += 1
																															snake_case_					:		Optional[Any] 					=					[]
																															snake_case_					:		int 					=					[]
																					else:
																										snake_case_					:		Optional[Any] 					=					line.split(""" """       )
																										words.append(splits[0]       )
																										if len(lowercase__       ) > 1:
																															labels.append(splits[self.label_idx].replace("""\n"""   ,		""""""       )       )
																										else:
																															# Examples could have no label for mode = "test"
																															labels.append("""O"""       )
																if words:
																					examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					0
											for line in test_input_reader:
																if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																					writer.write(lowercase__       )
																					if not preds_list[example_id]:
																										example_id += 1
																elif preds_list[example_id]:
																					snake_case_					:		Optional[int] 					=					line.split()[0] + """ """ + preds_list[example_id].pop(0       ) + """\n"""
																					writer.write(lowercase__       )
																else:
																					logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,		line.split()[0]       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Dict 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		List[Any] 					=					["""O"""] + labels
																return labels
											else:
																return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self       ):
											# in CONLL2003 dataset chunk column is second-to-last
											super().__init__(label_idx=-2       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Any 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		Tuple 					=					["""O"""] + labels
																return labels
											else:
																return [
																    "O",
																    "B-ADVP",
																    "B-INTJ",
																    "B-LST",
																    "B-PRT",
																    "B-NP",
																    "B-SBAR",
																    "B-VP",
																    "B-ADJP",
																    "B-CONJP",
																    "B-PP",
																    "I-ADVP",
																    "I-INTJ",
																    "I-LST",
																    "I-PRT",
																    "I-NP",
																    "I-SBAR",
																    "I-VP",
																    "I-ADJP",
																    "I-CONJP",
																    "I-PP",
																]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[Any] 					=					mode.value
											snake_case_					:		Optional[int] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		str 					=					[]

											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																for sentence in parse_incr(lowercase__       ):
																					snake_case_					:		Tuple 					=					[]
																					snake_case_					:		Any 					=					[]
																					for token in sentence:
																										words.append(token["""form"""]       )
																										labels.append(token["""upos"""]       )
																					assert len(lowercase__       ) == len(lowercase__       )
																					if words:
																										examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																										guid_index += 1
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Dict 					=					0
											for sentence in parse_incr(lowercase__       ):
																snake_case_					:		int 					=					preds_list[example_id]
																snake_case_					:		Dict 					=					""""""
																for token in sentence:
																					out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0       )}) '
																out += "\n"
																writer.write(lowercase__       )
																example_id += 1







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					return f.read().splitlines()
											else:
																return [
																    "ADJ",
																    "ADP",
																    "ADV",
																    "AUX",
																    "CCONJ",
																    "DET",
																    "INTJ",
																    "NOUN",
																    "NUM",
																    "PART",
																    "PRON",
																    "PROPN",
																    "PUNCT",
																    "SCONJ",
																    "SYM",
																    "VERB",
																    "X",
																]




