





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     SCREAMING_SNAKE_CASE__ (						a__      ):



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,*__lowerCamelCase,**__lowerCamelCase ):
        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__											=						self.model.config
        else:
               A__											=						config

        A__											=						data_args
        A__											=						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__											=						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__											=						label_smoothed_nll_loss



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

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



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
               A__											=						schedule_func(self.optimizer )
        elif self.args.lr_scheduler == "constant_w_warmup":
               A__											=						schedule_func(self.optimizer,num_warmup_steps=self.args.warmup_steps )
        else:
               A__											=						schedule_func(
                   self.optimizer,num_warmup_steps=self.args.warmup_steps,num_training_steps=lowerCAmelCase__ )
        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,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        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__											=						model(**lowerCAmelCase__,use_cache=lowerCAmelCase__ )[0]
                      A__											=						self.loss_fn(logits.view(-1,logits.shape[-1] ),labels.view(-1 ) )
               else:
                      # compute usual loss via models
                      A__											=						model(**lowerCAmelCase__,labels=lowerCAmelCase__,use_cache=lowerCAmelCase__ )[:2]
        else:
               # compute label smoothed loss
               A__											=						model(**lowerCAmelCase__,use_cache=lowerCAmelCase__ )[0]
               A__											=						torch.nn.functional.log_softmax(lowerCAmelCase__,dim=-1 )
               A__											=						self.loss_fn(lowerCAmelCase__,lowerCAmelCase__,self.args.label_smoothing,ignore_index=self.config.pad_token_id )
        return loss, logits



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						inputs.pop('''labels''' )
        A__											=						self._compute_loss(lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__ )
        return loss



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,):
        A__											=						self._prepare_inputs(lowerCAmelCase__ )

        A__											=						{
            "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__											=						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__											=						self._pad_tensors_to_max_len(lowerCAmelCase__,gen_kwargs['''max_length'''] )

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

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

        A__											=						generated_tokens if self.args.predict_with_generate else logits

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

        return (loss, logits, labels)




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        # If PAD token is not defined at least EOS token has to be defined
        A__											=						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__											=						pad_token_id * torch.ones(
            (tensor.shape[0], max_length),dtype=tensor.dtype,device=tensor.device )
        A__											=						tensor
        return padded_tensor












"""simple docstring"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser






def       _SCREAMING_SNAKE_CASE		(	) ->Dict:






	'''simple docstring'''






	a      :		Any	=				ArgumentParser("Accelerate CLI tool"    ,       usage="accelerate <command> [<args>]"    ,       allow_abbrev=_lowercase					)
	a      :		List[Any]	=				parser.add_subparsers(help="accelerate command helpers"					)

	# Register commands
	get_config_parser(subparsers=_lowercase					)
	env_command_parser(subparsers=_lowercase					)
	launch_command_parser(subparsers=_lowercase					)
	tpu_command_parser(subparsers=_lowercase					)
	test_command_parser(subparsers=_lowercase					)

	# Let's go
	a      :		List[Any]	=				parser.parse_args()

	if not hasattr(_lowercase    ,       "func"					):
		parser.print_help()
		exit(1					)

	# Run
	args.func(_lowercase					)


if __name__ == "__main__":
		main()


