





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

_lowerCamelCase  :			str        =       {
    "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      (    _SCREAMING_SNAKE_CASE):







				def __init__(							self   :					int      ,     _lowerCamelCase   :					Any=None      ,     _lowerCamelCase   :					Optional[Any]=None      ,     *_lowerCamelCase   :					Tuple      ,     **_lowerCamelCase   :					str       ):



					"""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__}"""
						)
						A_  :				Optional[Any]	   =		self.model.config
					else:
						A_  :				List[str]	   =		config

					A_  :				Optional[int]	   =		data_args
					A_  :				List[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:
						A_  :				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_  :				List[Any]	   =		label_smoothed_nll_loss







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					if self.optimizer is None:
						A_  :				Dict	   =		['''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_  :				str	   =		Adafactor if self.args.adafactor else AdamW
						if self.args.adafactor:
							A_  :				str	   =		Adafactor
							A_  :				List[str]	   =		{'''scale_parameter''': False, '''relative_step''': False}
						else:
							A_  :				str	   =		AdamW
							A_  :				List[Any]	   =		{
							    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
							    '''eps''': self.args.adam_epsilon,
							}
						A_  :				Optional[int]	   =		self.args.learning_rate
						if self.sharded_ddp:
							A_  :				Optional[int]	   =		OSS(
							    params=A_      ,     optim=A_      ,     **A_      ,     )
						else:
							A_  :				Tuple	   =		optimizer_cls(A_      ,     **A_       )

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







				def 					a_  (							self   :					Dict      ,     _lowerCamelCase   :					Tuple       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					Optional[int]       ):



					"""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 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					List[Any]       ):



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







				def 					a_  (							self   :					str      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				Tuple	   =		inputs.pop('''labels'''       )
					A_   , A_  :				Optional[int]	   =		self._compute_loss(A_      ,     A_      ,     A_       )
					return loss







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Tuple = None      ,     ):



					"""simple docstring"""

					A_  :				List[Any]	   =		self._prepare_inputs(A_       )

					A_  :				List[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:
						A_  :				Union[str, Any]	   =		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"]:
							A_  :				Dict	   =		self._pad_tensors_to_max_len(A_      ,     gen_kwargs['''max_length''']       )

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

					A_  :				Optional[int]	   =		loss.mean().detach()
					if self.args.prediction_loss_only:
						return (loss, None, None)

					A_  :				Dict	   =		generated_tokens if self.args.predict_with_generate else logits

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

					return (loss, logits, labels)







				def 					a_  (							self   :					Union[str, Any]      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Tuple       ):



					"""simple docstring"""

					A_  :				str	   =		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_  :				Optional[int]	   =		pad_token_id * torch.ones(
					    (tensor.shape[0], max_length)      ,     dtype=tensor.dtype      ,     device=tensor.device       )
					A_  :				Any	   =		tensor
					return padded_tensor











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase  :			List[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Tuple        =       {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   """camembert"""







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[Any]=3_05_22      ,     _lowerCamelCase   :					List[str]=7_68      ,     _lowerCamelCase   :					Optional[Any]=12      ,     _lowerCamelCase   :					List[str]=12      ,     _lowerCamelCase   :					Any=30_72      ,     _lowerCamelCase   :					List[str]="gelu"      ,     _lowerCamelCase   :					Optional[Any]=0.1      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					str=2      ,     _lowerCamelCase   :					Optional[Any]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					str=1      ,     _lowerCamelCase   :					Union[str, Any]=0      ,     _lowerCamelCase   :					Optional[int]=2      ,     _lowerCamelCase   :					List[str]="absolute"      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					List[Any]=None      ,     **_lowerCamelCase   :					Union[str, Any]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

					A_  :				Any	   =		vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				Any	   =		num_hidden_layers
					A_  :				List[Any]	   =		num_attention_heads
					A_  :				List[Any]	   =		hidden_act
					A_  :				Optional[int]	   =		intermediate_size
					A_  :				Tuple	   =		hidden_dropout_prob
					A_  :				Union[str, Any]	   =		attention_probs_dropout_prob
					A_  :				int	   =		max_position_embeddings
					A_  :				Tuple	   =		type_vocab_size
					A_  :				Dict	   =		initializer_range
					A_  :				Tuple	   =		layer_norm_eps
					A_  :				List[Any]	   =		position_embedding_type
					A_  :				Any	   =		use_cache
					A_  :				Dict	   =		classifier_dropout

class        lowercase      (    __UpperCAmelCase):







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					if self.task == "multiple-choice":
						A_  :				Union[str, Any]	   =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
						A_  :				Dict	   =		{0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					    ]       )





