
"""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  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			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__}"
				)
				__lowerCAmelCase			:						Any									=      self.model.config
			else:
				__lowerCAmelCase			:						int									=      config

			__lowerCAmelCase			:						Any									=      data_args
			__lowerCAmelCase			:						int									=      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:
				__lowerCAmelCase			:						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

				__lowerCAmelCase			:						int									=      label_smoothed_nll_loss

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

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

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

		def 							__lowerCamelCase							(      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 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			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
					__lowerCAmelCase			:						Optional[int]									=      model(**_SCREAMING_SNAKE_CASE       ,		use_cache=_SCREAMING_SNAKE_CASE )[0]
					__lowerCAmelCase			:						Optional[Any]									=      self.loss_fn(logits.view(-1       ,		logits.shape[-1] )       ,		labels.view(-1 ) )
				else:
					# compute usual loss via models
					__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, Any]									=      model(**_SCREAMING_SNAKE_CASE       ,		labels=_SCREAMING_SNAKE_CASE       ,		use_cache=_SCREAMING_SNAKE_CASE )[:2]
			else:
				# compute label smoothed loss
				__lowerCAmelCase			:						int									=      model(**_SCREAMING_SNAKE_CASE       ,		use_cache=_SCREAMING_SNAKE_CASE )[0]
				__lowerCAmelCase			:						Optional[int]									=      torch.nn.functional.log_softmax(_SCREAMING_SNAKE_CASE       ,		dim=-1 )
				__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, 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 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Tuple									=      inputs.pop('labels' )
			__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      self._compute_loss(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			return loss

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		):
			__lowerCAmelCase			:						Tuple									=      self._prepare_inputs(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[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:
				__lowerCAmelCase			:						Tuple									=      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"]:
					__lowerCAmelCase			:						Optional[Any]									=      self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE       ,		gen_kwargs['max_length'] )

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

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

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

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

			return (loss, logits, labels)







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			# If PAD token is not defined at least EOS token has to be defined
			__lowerCAmelCase			:						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}" )

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



import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__lowerCAmelCase	: Union[str, Any]  				={
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      ):
						if got_ver is None or want_ver is None:
												raise ValueError(
												    F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
												    F''' reinstalling {pkg}.'''      )
						if not ops[op](version.parse(lowercase__      )    ,       version.parse(lowercase__      )      ):
												raise ImportError(
												    F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'''      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__ = None      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		F'''\n{hint}''' if hint is not None else ''''''

						# non-versioned check
						if re.match(R'''^[\w_\-\d]+$'''    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		requirement, None, None
						else:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)'''    ,       lowercase__      )
												if not match:
																		raise ValueError(
																		    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
																		    F''' got {requirement}'''      )
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		match[0]
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		want_full.split(''','''      )  # there could be multiple requirements
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		{}
												for w in want_range:
																		__SCREAMING_SNAKE_CASE						:     Any					=		re.findall(R'''^([\s!=<>]{1,2})(.+)'''    ,       lowercase__      )
																		if not match:
																								raise ValueError(
																								    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
																								    F''' but got {requirement}'''      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		match[0]
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		want_ver
																		if op not in ops:
																								raise ValueError(F'''{requirement}: need one of {list(ops.keys()      )}, but got {op}'''      )

    # special case
						if pkg == "python":
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		'''.'''.join([str(lowercase__      ) for x in sys.version_info[:3]]      )
												for op, want_ver in wanted.items():
																		_compare_versions(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )
												return

						# check if any version is installed
						try:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		importlib.metadata.version(lowercase__      )
						except importlib.metadata.PackageNotFoundError:
												raise importlib.metadata.PackageNotFoundError(
												    F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}'''      )

						# check that the right version is installed if version number or a range was provided
						if want_ver is not None:
												for op, want_ver in wanted.items():
																		_compare_versions(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		'''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
						return require_version(lowercase__    ,       lowercase__      )
