
'''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					:    Tuple           = logging.get_logger(__name__)

lowerCAmelCase					:    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 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_=None						,				A_=None						,				*A_						,				**A_     )->		Any:







											'''simple docstring'''






											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   			=     self.model.config
											else:
																		UpperCamelCase   			=     config

											UpperCamelCase   			=     data_args
											UpperCamelCase   			=     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   			=     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   			=     label_smoothed_nll_loss







				def 						UpperCAmelCase_    (       self						,				A_     )->		Any:







											'''simple docstring'''






											if self.optimizer is None:
																		UpperCamelCase   			=     ['bias', 'LayerNorm.weight']
																		UpperCamelCase   			=     [
																		    {
																		        '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   			=     Adafactor if self.args.adafactor else AdamW
																		if self.args.adafactor:
																									UpperCamelCase   			=     Adafactor
																									UpperCamelCase   			=     {'scale_parameter': False, 'relative_step': False}
																		else:
																									UpperCamelCase   			=     AdamW
																									UpperCamelCase   			=     {
																									    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																									    'eps': self.args.adam_epsilon,
																									}
																		UpperCamelCase   			=     self.args.learning_rate
																		if self.sharded_ddp:
																									UpperCamelCase   			=     OSS(
																									    params=A_						,				optim=A_						,				**A_						,				)
																		else:
																									UpperCamelCase   			=     optimizer_cls(A_						,				**A_     )

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







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     arg_to_scheduler[self.args.lr_scheduler]
											if self.args.lr_scheduler == "constant":
																		UpperCamelCase   			=     schedule_func(self.optimizer     )
											elif self.args.lr_scheduler == "constant_w_warmup":
																		UpperCamelCase   			=     schedule_func(self.optimizer						,				num_warmup_steps=self.args.warmup_steps     )
											else:
																		UpperCamelCase   			=     schedule_func(
																		    self.optimizer						,				num_warmup_steps=self.args.warmup_steps						,				num_training_steps=A_     )
											return scheduler







				def 						UpperCAmelCase_    (       self     )->		Optional[torch.utils.data.Sampler]:







											'''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 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		Tuple:







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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     inputs.pop('labels'     )
											UpperCamelCase			,	UpperCamelCase   			=     self._compute_loss(A_						,				A_						,				A_     )
											return loss







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_						,				A_ = None						,				)->		Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:







											'''simple docstring'''






											UpperCamelCase   			=     self._prepare_inputs(A_     )

											UpperCamelCase   			=     {
											    '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   			=     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   			=     self._pad_tensors_to_max_len(A_						,				gen_kwargs['max_length']     )

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

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

											UpperCamelCase   			=     generated_tokens if self.args.predict_with_generate else logits

											if labels.shape[-1] < gen_kwargs["max_length"]:
																		UpperCamelCase   			=     self._pad_tensors_to_max_len(A_						,				gen_kwargs['max_length']     )

											return (loss, logits, labels)







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		Any:







											'''simple docstring'''






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






'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCAmelCase					:    Optional[Any]           = {
    'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    str           = [
						    'FALCON_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'FalconForCausalLM',
						    'FalconModel',
						    'FalconPreTrainedModel',
						    'FalconForSequenceClassification',
						    'FalconForTokenClassification',
						    'FalconForQuestionAnswering',
						]


if TYPE_CHECKING:
						from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_falcon import (
												    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FalconForCausalLM,
												    FalconForQuestionAnswering,
												    FalconForSequenceClassification,
												    FalconForTokenClassification,
												    FalconModel,
												    FalconPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




