import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    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.utils.versions import require_version


__snake_case			      =    logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

__snake_case			      =    {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__snake_case			      =    {
    '''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,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__snake_case			      =    sorted(arg_to_scheduler.keys())
__snake_case			      =    '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class 			__snake_case   (     pl.LightningModule   ):
 def __init__(  self		,   snake_case__		,   snake_case__=None		,   snake_case__="base"		,   snake_case__=None		,   snake_case__=None		,   snake_case__=None		,   **snake_case__		,   )	->							List[str]:
       '''simple docstring'''





       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(snake_case__						)
       UpperCAmelCase    : Union[str, Any]      =0
       UpperCAmelCase    : List[Any]      =Path(self.hparams.output_dir						)
       UpperCAmelCase    : List[str]      =self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
             UpperCAmelCase    : Union[str, Any]      =AutoConfig.from_pretrained(
                 self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,   **({'''num_labels''': num_labels} if num_labels is not None else {})		,   cache_dir=snake_case__		,   **snake_case__		,   )
       else:
             UpperCAmelCase    : PretrainedConfig      =config

       UpperCAmelCase    : List[str]      =('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
       for p in extra_model_params:
             if getattr(self.hparams		,   snake_case__		,   snake_case__						):
                   assert hasattr(self.config		,   snake_case__						), f'''model config doesn\'t have a `{p}` attribute'''
                   setattr(self.config		,   snake_case__		,   getattr(self.hparams		,   snake_case__						)						)

       if tokenizer is None:
             UpperCAmelCase    : Dict      =AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,   cache_dir=snake_case__		,   )
       else:
             UpperCAmelCase    : PreTrainedTokenizer      =tokenizer
       UpperCAmelCase    : Dict      =MODEL_MODES[mode]
       if model is None:
             UpperCAmelCase    : str      =self.model_type.from_pretrained(
                 self.hparams.model_name_or_path		,   from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path						)		,   config=self.config		,   cache_dir=snake_case__		,   )
       else:
             UpperCAmelCase    : Union[str, Any]      =model
 def 	UpperCAmelCase__       (  self		,   *snake_case__		,   **snake_case__						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      =self.model_type.from_pretrained(*snake_case__		,   **snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =arg_to_scheduler[self.hparams.lr_scheduler]
       UpperCAmelCase    : Dict      =get_schedule_func(
           self.opt		,   num_warmup_steps=self.hparams.warmup_steps		,   num_training_steps=self.total_steps()						)
       UpperCAmelCase    : int      ={'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
       return scheduler
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =self.model
       UpperCAmelCase    : Dict      =['''bias''', '''LayerNorm.weight''']
       UpperCAmelCase    : Dict      =[
           {
               '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
               '''weight_decay''': self.hparams.weight_decay,
           },
           {
               '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
               '''weight_decay''': 0.0,
           },
       ]
       if self.hparams.adafactor:
             UpperCAmelCase    : Any      =Adafactor(
                 snake_case__		,   lr=self.hparams.learning_rate		,   scale_parameter=snake_case__		,   relative_step=snake_case__						)

       else:
             UpperCAmelCase    : Optional[int]      =AdamW(
                 snake_case__		,   lr=self.hparams.learning_rate		,   eps=self.hparams.adam_epsilon						)
       UpperCAmelCase    : int      =optimizer

       UpperCAmelCase    : Union[str, Any]      =self.get_lr_scheduler()

       return [optimizer], [scheduler]
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__						)	->							Tuple:
       '''simple docstring'''





       return self.validation_step(snake_case__		,   snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Optional[int]:
       '''simple docstring'''





       return self.validation_end(snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       UpperCAmelCase    : str      =max(1		,   self.hparams.gpus						)  # TODO: consider num_tpu_cores
       UpperCAmelCase    : Dict      =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
       return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Union[str, Any]:
       '''simple docstring'''





       if mode == "test":
             UpperCAmelCase    : str      =len(self.test_dataloader().dataset						)
       else:
             UpperCAmelCase    : Union[str, Any]      =self.get_dataloader('''train'''		,   self.hparams.train_batch_size		,   shuffle=snake_case__						)
             UpperCAmelCase    : str      =len(self.train_dataloader().dataset						)
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__ = False						)	->							List[str]:
       '''simple docstring'''





       raise NotImplementedError('''You must implement this for your task'''						)
 def 	UpperCAmelCase__       (  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return self.train_loader
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       return self.get_dataloader('''dev'''		,   self.hparams.eval_batch_size		,   shuffle=snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       return self.get_dataloader('''test'''		,   self.hparams.eval_batch_size		,   shuffle=snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Tuple:
       '''simple docstring'''





       return os.path.join(
           self.hparams.data_dir		,   '''cached_{}_{}_{}'''.format(
               snake_case__		,   list(filter(snake_case__		,   self.hparams.model_name_or_path.split('''/'''						)						)						).pop()		,   str(self.hparams.max_seq_length						)		,   )		,   )
 @pl.utilities.rank_zero_only
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							None:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =self.output_dir.joinpath('''best_tfmr'''						)
       UpperCAmelCase    : int      =self.step_count
       self.model.save_pretrained(snake_case__						)
       self.tokenizer.save_pretrained(snake_case__						)






 @staticmethod
 def 	UpperCAmelCase__       (  snake_case__		,   snake_case__						)	->							Any:
       '''simple docstring'''





       parser.add_argument(
           '''--model_name_or_path'''		,   default=snake_case__		,   type=snake_case__		,   required=snake_case__		,   help='''Path to pretrained model or model identifier from huggingface.co/models'''		,   )
       parser.add_argument(
           '''--config_name'''		,   default=''''''		,   type=snake_case__		,   help='''Pretrained config name or path if not the same as model_name'''						)
       parser.add_argument(
           '''--tokenizer_name'''		,   default=snake_case__		,   type=snake_case__		,   help='''Pretrained tokenizer name or path if not the same as model_name'''		,   )
       parser.add_argument(
           '''--cache_dir'''		,   default=''''''		,   type=snake_case__		,   help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''		,   )
       parser.add_argument(
           '''--encoder_layerdrop'''		,   type=snake_case__		,   help='''Encoder layer dropout probability (Optional). Goes into model.config'''		,   )
       parser.add_argument(
           '''--decoder_layerdrop'''		,   type=snake_case__		,   help='''Decoder layer dropout probability (Optional). Goes into model.config'''		,   )
       parser.add_argument(
           '''--dropout'''		,   type=snake_case__		,   help='''Dropout probability (Optional). Goes into model.config'''		,   )
       parser.add_argument(
           '''--attention_dropout'''		,   type=snake_case__		,   help='''Attention dropout probability (Optional). Goes into model.config'''		,   )
       parser.add_argument('''--learning_rate'''		,   default=5e-5		,   type=snake_case__		,   help='''The initial learning rate for Adam.'''						)
       parser.add_argument(
           '''--lr_scheduler'''		,   default='''linear'''		,   choices=snake_case__		,   metavar=snake_case__		,   type=snake_case__		,   help='''Learning rate scheduler'''		,   )
       parser.add_argument('''--weight_decay'''		,   default=0.0		,   type=snake_case__		,   help='''Weight decay if we apply some.'''						)
       parser.add_argument('''--adam_epsilon'''		,   default=1e-8		,   type=snake_case__		,   help='''Epsilon for Adam optimizer.'''						)
       parser.add_argument('''--warmup_steps'''		,   default=0		,   type=snake_case__		,   help='''Linear warmup over warmup_steps.'''						)
       parser.add_argument('''--num_workers'''		,   default=4		,   type=snake_case__		,   help='''kwarg passed to DataLoader'''						)
       parser.add_argument('''--num_train_epochs'''		,   dest='''max_epochs'''		,   default=3		,   type=snake_case__						)
       parser.add_argument('''--train_batch_size'''		,   default=32		,   type=snake_case__						)
       parser.add_argument('''--eval_batch_size'''		,   default=32		,   type=snake_case__						)
       parser.add_argument('''--adafactor'''		,   action='''store_true'''						)







class 			__snake_case   (     pl.Callback   ):
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__						)	->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Tuple      =trainer.lr_schedulers[0]['''scheduler''']
       UpperCAmelCase    : Dict      ={f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
       pl_module.logger.log_metrics(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__						)	->							Optional[int]:
       '''simple docstring'''





       rank_zero_info('''***** Validation results *****'''						)
       UpperCAmelCase    : Union[str, Any]      =trainer.callback_metrics
       # Log results
       for key in sorted(snake_case__						):
             if key not in ["log", "progress_bar"]:
                   rank_zero_info('''{} = {}\n'''.format(snake_case__		,   str(metrics[key]						)						)						)






 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__						)	->							Tuple:
       '''simple docstring'''





       rank_zero_info('''***** Test results *****'''						)
       UpperCAmelCase    : List[str]      =trainer.callback_metrics
       # Log and save results to file
       UpperCAmelCase    : List[Any]      =os.path.join(pl_module.hparams.output_dir		,   '''test_results.txt'''						)
       with open(snake_case__		,   '''w'''						) as writer:
             for key in sorted(snake_case__						):
                   if key not in ["log", "progress_bar"]:
                         rank_zero_info('''{} = {}\n'''.format(snake_case__		,   str(metrics[key]						)						)						)
                         writer.write('''{} = {}\n'''.format(snake_case__		,   str(metrics[key]						)						)						)





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  None:






      '''simple docstring'''





      parser.add_argument(
          '''--output_dir'''		,					default=__lowerCAmelCase		,					type=__lowerCAmelCase		,					required=__lowerCAmelCase		,					help='''The output directory where the model predictions and checkpoints will be written.'''		,					)
      parser.add_argument(
          '''--fp16'''		,					action='''store_true'''		,					help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''		,					)

      parser.add_argument(
          '''--fp16_opt_level'''		,					type=__lowerCAmelCase		,					default='''O2'''		,					help=(
              '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
              '''See details at https://nvidia.github.io/apex/amp.html'''
          )		,					)
      parser.add_argument('''--n_tpu_cores'''		,					dest='''tpu_cores'''		,					type=__lowerCAmelCase						)
      parser.add_argument('''--max_grad_norm'''		,					dest='''gradient_clip_val'''		,					default=1.0		,					type=__lowerCAmelCase		,					help='''Max gradient norm'''						)
      parser.add_argument('''--do_train'''		,					action='''store_true'''		,					help='''Whether to run training.'''						)
      parser.add_argument('''--do_predict'''		,					action='''store_true'''		,					help='''Whether to run predictions on the test set.'''						)
      parser.add_argument(
          '''--gradient_accumulation_steps'''		,					dest='''accumulate_grad_batches'''		,					type=__lowerCAmelCase		,					default=1		,					help='''Number of updates steps to accumulate before performing a backward/update pass.'''		,					)
      parser.add_argument('''--seed'''		,					type=__lowerCAmelCase		,					default=42		,					help='''random seed for initialization'''						)
      parser.add_argument(
          '''--data_dir'''		,					default=__lowerCAmelCase		,					type=__lowerCAmelCase		,					required=__lowerCAmelCase		,					help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''		,					)





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase=None		,					__lowerCAmelCase=True		,					__lowerCAmelCase=[]		,					__lowerCAmelCase=None		,					__lowerCAmelCase=None		,					**__lowerCAmelCase		,					)->  Optional[int]:






      '''simple docstring'''





      pl.seed_everything(args.seed						)

      # init model
      UpperCAmelCase    : List[Any]      =Path(model.hparams.output_dir						)
      odir.mkdir(exist_ok=__lowerCAmelCase						)

      # add custom checkpoints
      if checkpoint_callback is None:
            UpperCAmelCase    : Any      =pl.callbacks.ModelCheckpoint(
                filepath=args.output_dir		,					prefix='''checkpoint'''		,					monitor='''val_loss'''		,					mode='''min'''		,					save_top_k=1						)
      if early_stopping_callback:
            extra_callbacks.append(__lowerCAmelCase						)
      if logging_callback is None:
            UpperCAmelCase    : int      =LoggingCallback()

      UpperCAmelCase    : Any      ={}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            UpperCAmelCase    : List[str]      =16
            UpperCAmelCase    : List[Any]      =args.fpaa_opt_level

      if args.gpus > 1:
            UpperCAmelCase    : str      ='''ddp'''

      UpperCAmelCase    : str      =args.accumulate_grad_batches
      UpperCAmelCase    : List[Any]      =extra_train_kwargs.get('''accelerator'''		,					__lowerCAmelCase						)
      UpperCAmelCase    : Optional[int]      =extra_train_kwargs.get('''profiler'''		,					__lowerCAmelCase						)

      UpperCAmelCase    : str      =pl.Trainer.from_argparse_args(
          __lowerCAmelCase		,					weights_summary=__lowerCAmelCase		,					callbacks=[logging_callback] + extra_callbacks		,					logger=__lowerCAmelCase		,					checkpoint_callback=__lowerCAmelCase		,					**__lowerCAmelCase		,					)

      if args.do_train:
            trainer.fit(__lowerCAmelCase						)

      return trainer

# 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.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


__snake_case			      =    os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
__snake_case			      =    os.path.join(hf_cache_home, '''accelerate''')
__snake_case			      =    os.path.join(cache_dir, '''default_config.yaml''')
__snake_case			      =    os.path.join(cache_dir, '''default_config.yaml''')

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
     __snake_case			      =    default_yaml_config_file
else:
     __snake_case			      =    default_json_config_file





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Tuple:






      '''simple docstring'''





      if config_file is not None:
            if not os.path.isfile(__lowerCAmelCase						):
                  raise FileNotFoundError(
                      f'''The passed configuration file `{config_file}` does not exist. '''
                      '''Please pass an existing file to `accelerate launch`, or use the the default one '''
                      '''created through `accelerate config` and run `accelerate launch` '''
                      '''without the `--config_file` argument.'''						)
      else:
            UpperCAmelCase    : Union[str, Any]      =default_config_file
      with open(__lowerCAmelCase		,					'''r'''		,					encoding='''utf-8'''						) as f:
            if config_file.endswith('''.json'''						):
                  if (
                      json.load(__lowerCAmelCase						).get('''compute_environment'''		,					ComputeEnvironment.LOCAL_MACHINE						)
                      == ComputeEnvironment.LOCAL_MACHINE
                  ):
                        UpperCAmelCase    : List[str]      =ClusterConfig
                  else:
                        UpperCAmelCase    : List[Any]      =SageMakerConfig
                  return config_class.from_json_file(json_file=__lowerCAmelCase						)
            else:
                  if (
                      yaml.safe_load(__lowerCAmelCase						).get('''compute_environment'''		,					ComputeEnvironment.LOCAL_MACHINE						)
                      == ComputeEnvironment.LOCAL_MACHINE
                  ):
                        UpperCAmelCase    : Union[str, Any]      =ClusterConfig
                  else:
                        UpperCAmelCase    : List[Any]      =SageMakerConfig
                  return config_class.from_yaml_file(yaml_file=__lowerCAmelCase						)







@dataclass
class 			__snake_case   :
 __lowerCamelCase    :   ComputeEnvironment
 __lowerCamelCase    :   Union[DistributedType, SageMakerDistributedType]
 __lowerCamelCase    :   str
 __lowerCamelCase    :   bool
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =self.__dict__
       # For serialization, it's best to convert Enums to strings (or their underlying value type).
       for key, value in result.items():
             if isinstance(snake_case__		,   snake_case__						):
                   UpperCAmelCase    : Any      =value.value
             if isinstance(snake_case__		,   snake_case__						) and not bool(snake_case__						):
                   UpperCAmelCase    : Union[str, Any]      =None
       UpperCAmelCase    : Dict      ={k: v for k, v in result.items() if v is not None}
       return result
 @classmethod
 def 	UpperCAmelCase__       (  cls		,   snake_case__=None						)	->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase    : Tuple      =default_json_config_file if json_file is None else json_file
       with open(snake_case__		,   '''r'''		,   encoding='''utf-8'''						) as f:
             UpperCAmelCase    : Optional[int]      =json.load(snake_case__						)
       if "compute_environment" not in config_dict:
             UpperCAmelCase    : Optional[int]      =ComputeEnvironment.LOCAL_MACHINE
       if "mixed_precision" not in config_dict:
             UpperCAmelCase    : Optional[int]      ='''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
       if "fp16" in config_dict:  # Convert the config to the new format.
             del config_dict["fp16"]
       if "dynamo_backend" in config_dict:  # Convert the config to the new format.
             UpperCAmelCase    : Optional[Any]      =config_dict.pop('''dynamo_backend'''						)
             UpperCAmelCase    : List[Any]      ={} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
       if "use_cpu" not in config_dict:
             UpperCAmelCase    : Dict      =False
       return cls(**snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       with open(snake_case__		,   '''w'''		,   encoding='''utf-8'''						) as f:
             UpperCAmelCase    : Dict      =json.dumps(self.to_dict()		,   indent=2		,   sort_keys=snake_case__						) + '''\n'''
             f.write(snake_case__						)
 @classmethod
 def 	UpperCAmelCase__       (  cls		,   snake_case__=None						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =default_yaml_config_file if yaml_file is None else yaml_file
       with open(snake_case__		,   '''r'''		,   encoding='''utf-8'''						) as f:
             UpperCAmelCase    : List[str]      =yaml.safe_load(snake_case__						)
       if "compute_environment" not in config_dict:
             UpperCAmelCase    : str      =ComputeEnvironment.LOCAL_MACHINE

       if "mixed_precision" not in config_dict:
             UpperCAmelCase    : Optional[Any]      ='''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
       if isinstance(config_dict['''mixed_precision''']		,   snake_case__						) and not config_dict["mixed_precision"]:
             UpperCAmelCase    : Any      ='''no'''
       if "fp16" in config_dict:  # Convert the config to the new format.
             del config_dict["fp16"]
       if "dynamo_backend" in config_dict:  # Convert the config to the new format.
             UpperCAmelCase    : List[str]      =config_dict.pop('''dynamo_backend'''						)
             UpperCAmelCase    : str      ={} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
       if "use_cpu" not in config_dict:
             UpperCAmelCase    : Optional[int]      =False
       return cls(**snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							List[str]:
       '''simple docstring'''





       with open(snake_case__		,   '''w'''		,   encoding='''utf-8'''						) as f:
             yaml.safe_dump(self.to_dict()		,   snake_case__						)






 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       if isinstance(self.compute_environment		,   snake_case__						):
             UpperCAmelCase    : Any      =ComputeEnvironment(self.compute_environment						)
       if isinstance(self.distributed_type		,   snake_case__						):
             if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                   UpperCAmelCase    : int      =SageMakerDistributedType(self.distributed_type						)
             else:
                   UpperCAmelCase    : int      =DistributedType(self.distributed_type						)
       if self.dynamo_config is None:
             UpperCAmelCase    : Optional[int]      ={}







@dataclass
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   int
 __lowerCamelCase    :   int     =      0
 __lowerCamelCase    :   int     =      1
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   Optional[int]     =      None
 __lowerCamelCase    :   Optional[str]     =      "static"
 __lowerCamelCase    :   Optional[bool]     =      False
 __lowerCamelCase    :   str     =      "main"

 # args for deepspeed_plugin
 __lowerCamelCase    :   dict     =      None
 # args for fsdp
 __lowerCamelCase    :   dict     =      None
 # args for megatron_lm
 __lowerCamelCase    :   dict     =      None
 # args for ipex
 __lowerCamelCase    :   dict     =      None
 # args for TPU
 __lowerCamelCase    :   bool     =      False

 # args for TPU pods
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   bool     =      False
 __lowerCamelCase    :   bool     =      False
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   List[str]     =      None
 __lowerCamelCase    :   List[str]     =      None
 __lowerCamelCase    :   List[str]     =      None

 # args for dynamo
 __lowerCamelCase    :   dict     =      None
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       if self.deepspeed_config is None:
             UpperCAmelCase    : Union[str, Any]      ={}
       if self.fsdp_config is None:
             UpperCAmelCase    : Any      ={}
       if self.megatron_lm_config is None:
             UpperCAmelCase    : List[Any]      ={}
       if self.ipex_config is None:
             UpperCAmelCase    : int      ={}
       return super().__post_init__()







@dataclass
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   str
 __lowerCamelCase    :   str
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   str     =      "us-east-1"
 __lowerCamelCase    :   int     =      1
 __lowerCamelCase    :   str     =      "all"
 __lowerCamelCase    :   str     =      F'accelerate-sagemaker-{num_machines}'
 __lowerCamelCase    :   str     =      SAGEMAKER_PYTORCH_VERSION
 __lowerCamelCase    :   str     =      SAGEMAKER_TRANSFORMERS_VERSION
 __lowerCamelCase    :   str     =      SAGEMAKER_PYTHON_VERSION
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   dict     =      None
 __lowerCamelCase    :   dict     =      None
