





# 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


UpperCAmelCase_   				=  os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
UpperCAmelCase_   				=  os.path.join(hf_cache_home, """accelerate""")
UpperCAmelCase_   				=  os.path.join(cache_dir, """default_config.yaml""")
UpperCAmelCase_   				=  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):
					UpperCAmelCase_   				=  default_yaml_config_file
else:
					UpperCAmelCase_   				=  default_json_config_file





def     lowerCamelCase__  (  UpperCamelCase__      :							List[str]    )      ->			str:



					'''simple docstring'''




					if config_file is not None:
										if not os.path.isfile(UpperCamelCase__    ):
															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:
										_snake_case						=					default_config_file
					with open(UpperCamelCase__							,     'r'							,     encoding='utf-8'    ) as f:
										if config_file.endswith('.json'    ):
															if (
															    json.load(UpperCamelCase__    ).get('compute_environment'							,     ComputeEnvironment.LOCAL_MACHINE    )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				_snake_case						=					ClusterConfig
															else:
																				_snake_case						=					SageMakerConfig
															return config_class.from_json_file(json_file=UpperCamelCase__    )
										else:
															if (
															    yaml.safe_load(UpperCamelCase__    ).get('compute_environment'							,     ComputeEnvironment.LOCAL_MACHINE    )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				_snake_case						=					ClusterConfig
															else:
																				_snake_case						=					SageMakerConfig
															return config_class.from_yaml_file(yaml_file=UpperCamelCase__    )
@dataclass
class       UpperCamelCase_		:
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					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(lowerCAmelCase_					,    lowerCAmelCase_	):
																				_snake_case						=					value.value
															if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	) and not bool(lowerCAmelCase_	):
																				_snake_case						=					None
										_snake_case						=					{k: v for k, v in result.items() if v is not None}
										return result




					@classmethod
					def 						lowerCAmelCase      (		cls					,    lowerCAmelCase_=None	)		->				List[Any]:
										_snake_case						=					default_json_config_file if json_file is None else json_file
										with open(lowerCAmelCase_					,    'r'					,    encoding='utf-8'	) as f:
															_snake_case						=					json.load(lowerCAmelCase_	)
										if "compute_environment" not in config_dict:
															_snake_case						=					ComputeEnvironment.LOCAL_MACHINE
										if "mixed_precision" not in config_dict:
															_snake_case						=					'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.
															_snake_case						=					config_dict.pop('dynamo_backend'	)
															_snake_case						=					{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
										if "use_cpu" not in config_dict:
															_snake_case						=					False
										return cls(**lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										with open(lowerCAmelCase_					,    'w'					,    encoding='utf-8'	) as f:
															_snake_case						=					json.dumps(self.to_dict()					,    indent=2					,    sort_keys=lowerCAmelCase_	) + '\n'
															f.write(lowerCAmelCase_	)




					@classmethod
					def 						lowerCAmelCase      (		cls					,    lowerCAmelCase_=None	)		->				int:
										_snake_case						=					default_yaml_config_file if yaml_file is None else yaml_file
										with open(lowerCAmelCase_					,    'r'					,    encoding='utf-8'	) as f:
															_snake_case						=					yaml.safe_load(lowerCAmelCase_	)
										if "compute_environment" not in config_dict:
															_snake_case						=					ComputeEnvironment.LOCAL_MACHINE

										if "mixed_precision" not in config_dict:
															_snake_case						=					'fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
										if isinstance(config_dict['mixed_precision']					,    lowerCAmelCase_	) and not config_dict["mixed_precision"]:
															_snake_case						=					'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.
															_snake_case						=					config_dict.pop('dynamo_backend'	)
															_snake_case						=					{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
										if "use_cpu" not in config_dict:
															_snake_case						=					False
										return cls(**lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										with open(lowerCAmelCase_					,    'w'					,    encoding='utf-8'	) as f:
															yaml.safe_dump(self.to_dict()					,    lowerCAmelCase_	)






					def 						lowerCAmelCase      (		self	)		->				Tuple:
										if isinstance(self.compute_environment					,    lowerCAmelCase_	):
															_snake_case						=					ComputeEnvironment(self.compute_environment	)
										if isinstance(self.distributed_type					,    lowerCAmelCase_	):
															if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																				_snake_case						=					SageMakerDistributedType(self.distributed_type	)
															else:
																				_snake_case						=					DistributedType(self.distributed_type	)
										if self.dynamo_config is None:
															_snake_case						=					{}





@dataclass
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						0
					lowerCAmelCase_				=						1
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						"static"
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						"main"

					# args for deepspeed_plugin
					lowerCAmelCase_				=						None
					# args for fsdp
					lowerCAmelCase_				=						None
					# args for megatron_lm
					lowerCAmelCase_				=						None
					# args for ipex
					lowerCAmelCase_				=						None
					# args for TPU
					lowerCAmelCase_				=						False

					# args for TPU pods
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None

					# args for dynamo
					lowerCAmelCase_				=						None




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										if self.deepspeed_config is None:
															_snake_case						=					{}
										if self.fsdp_config is None:
															_snake_case						=					{}
										if self.megatron_lm_config is None:
															_snake_case						=					{}
										if self.ipex_config is None:
															_snake_case						=					{}
										return super().__post_init__()





@dataclass
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						"us-east-1"
					lowerCAmelCase_				=						1
					lowerCAmelCase_				=						"all"
					lowerCAmelCase_				=						f"accelerate-sagemaker-{num_machines}"
					lowerCAmelCase_				=						SAGEMAKER_PYTORCH_VERSION
					lowerCAmelCase_				=						SAGEMAKER_TRANSFORMERS_VERSION
					lowerCAmelCase_				=						SAGEMAKER_PYTHON_VERSION
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None










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


UpperCAmelCase_   				=  logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

UpperCAmelCase_   				=  {
    """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
UpperCAmelCase_   				=  {
    """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
}
UpperCAmelCase_   				=  sorted(arg_to_scheduler.keys())
UpperCAmelCase_   				=  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""
class       UpperCamelCase_		(	pl.LightningModule       ):




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=None					,    lowerCAmelCase_="base"					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    **lowerCAmelCase_					,    )		->				Union[str, Any]:
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(lowerCAmelCase_	)
										_snake_case						=					0
										_snake_case						=					Path(self.hparams.output_dir	)
										_snake_case						=					self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
															_snake_case						=					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=lowerCAmelCase_					,    **lowerCAmelCase_					,    )
										else:
															_snake_case						=					config

										_snake_case						=					('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
										for p in extra_model_params:
															if getattr(self.hparams					,    lowerCAmelCase_					,    lowerCAmelCase_	):
																				assert hasattr(self.config					,    lowerCAmelCase_	), F'''model config doesn\'t have a `{p}` attribute'''
																				setattr(self.config					,    lowerCAmelCase_					,    getattr(self.hparams					,    lowerCAmelCase_	)	)

										if tokenizer is None:
															_snake_case						=					AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,    cache_dir=lowerCAmelCase_					,    )
										else:
															_snake_case						=					tokenizer
										_snake_case						=					MODEL_MODES[mode]
										if model is None:
															_snake_case						=					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=lowerCAmelCase_					,    )
										else:
															_snake_case						=					model




					def 						lowerCAmelCase      (		self					,    *lowerCAmelCase_					,    **lowerCAmelCase_	)		->				Union[str, Any]:
										_snake_case						=					self.model_type.from_pretrained(*lowerCAmelCase_					,    **lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					arg_to_scheduler[self.hparams.lr_scheduler]
										_snake_case						=					get_schedule_func(
										    self.opt					,    num_warmup_steps=self.hparams.warmup_steps					,    num_training_steps=self.total_steps()	)
										_snake_case						=					{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
										return scheduler




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					self.model
										_snake_case						=					['bias', 'LayerNorm.weight']
										_snake_case						=					[
										    {
										        '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:
															_snake_case						=					Adafactor(
															    lowerCAmelCase_					,    lr=self.hparams.learning_rate					,    scale_parameter=lowerCAmelCase_					,    relative_step=lowerCAmelCase_	)

										else:
															_snake_case						=					AdamW(
															    lowerCAmelCase_					,    lr=self.hparams.learning_rate					,    eps=self.hparams.adam_epsilon	)
										_snake_case						=					optimizer

										_snake_case						=					self.get_lr_scheduler()

										return [optimizer], [scheduler]




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				int:
										return self.validation_step(lowerCAmelCase_					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Union[str, Any]:
										return self.validation_end(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					max(1					,    self.hparams.gpus	)  # TODO: consider num_tpu_cores
										_snake_case						=					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				str:
										if mode == "test":
															_snake_case						=					len(self.test_dataloader().dataset	)
										else:
															_snake_case						=					self.get_dataloader('train'					,    self.hparams.train_batch_size					,    shuffle=lowerCAmelCase_	)
															_snake_case						=					len(self.train_dataloader().dataset	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_ = False	)		->				int:
										raise NotImplementedError('You must implement this for your task'	)




					def 						lowerCAmelCase      (		self	)		->				Any:
										return self.train_loader




					def 						lowerCAmelCase      (		self	)		->				Dict:
										return self.get_dataloader('dev'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										return self.get_dataloader('test'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Union[str, Any]:
										return os.path.join(
										    self.hparams.data_dir					,    'cached_{}_{}_{}'.format(
										        lowerCAmelCase_					,    list(filter(lowerCAmelCase_					,    self.hparams.model_name_or_path.split('/'	)	)	).pop()					,    str(self.hparams.max_seq_length	)					,    )					,    )




					@pl.utilities.rank_zero_only
					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				None:
										_snake_case						=					self.output_dir.joinpath('best_tfmr'	)
										_snake_case						=					self.step_count
										self.model.save_pretrained(lowerCAmelCase_	)
										self.tokenizer.save_pretrained(lowerCAmelCase_	)






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





class       UpperCamelCase_		(	pl.Callback       ):




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				List[str]:
										_snake_case						=					trainer.lr_schedulers[0]['scheduler']
										_snake_case						=					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
										pl_module.logger.log_metrics(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Dict:
										rank_zero_info('***** Validation results *****'	)
										_snake_case						=					trainer.callback_metrics
										# Log results
										for key in sorted(lowerCAmelCase_	):
															if key not in ["log", "progress_bar"]:
																				rank_zero_info('{} = {}\n'.format(lowerCAmelCase_					,    str(metrics[key]	)	)	)






					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Optional[int]:
										rank_zero_info('***** Test results *****'	)
										_snake_case						=					trainer.callback_metrics
										# Log and save results to file
										_snake_case						=					os.path.join(pl_module.hparams.output_dir					,    'test_results.txt'	)
										with open(lowerCAmelCase_					,    'w'	) as writer:
															for key in sorted(lowerCAmelCase_	):
																				if key not in ["log", "progress_bar"]:
																									rank_zero_info('{} = {}\n'.format(lowerCAmelCase_					,    str(metrics[key]	)	)	)
																									writer.write('{} = {}\n'.format(lowerCAmelCase_					,    str(metrics[key]	)	)	)





def     lowerCamelCase__  (  UpperCamelCase__      :							int							,     UpperCamelCase__      :							Optional[Any]    )      ->			None:



					'''simple docstring'''




					parser.add_argument(
					    '--output_dir'							,     default=UpperCamelCase__							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     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=UpperCamelCase__							,     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=UpperCamelCase__    )
					parser.add_argument('--max_grad_norm'							,     dest='gradient_clip_val'							,     default=1.0							,     type=UpperCamelCase__							,     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=UpperCamelCase__							,     default=1							,     help='Number of updates steps to accumulate before performing a backward/update pass.'							,     )
					parser.add_argument('--seed'							,     type=UpperCamelCase__							,     default=42							,     help='random seed for initialization'    )
					parser.add_argument(
					    '--data_dir'							,     default=UpperCamelCase__							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'							,     )





def     lowerCamelCase__  (  UpperCamelCase__      :							BaseTransformer							,     UpperCamelCase__      :							argparse.Namespace							,     UpperCamelCase__      :							Any=None							,     UpperCamelCase__      :							str=True							,     UpperCamelCase__      :							int=[]							,     UpperCamelCase__      :							Any=None							,     UpperCamelCase__      :							Optional[int]=None							,     **UpperCamelCase__      :							Optional[int]							,     )      ->			str:



					'''simple docstring'''




					pl.seed_everything(args.seed    )

					# init model
					_snake_case						=					Path(model.hparams.output_dir    )
					odir.mkdir(exist_ok=UpperCamelCase__    )

					# add custom checkpoints
					if checkpoint_callback is None:
										_snake_case						=					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(UpperCamelCase__    )
					if logging_callback is None:
										_snake_case						=					LoggingCallback()

					_snake_case						=					{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										_snake_case						=					16
										_snake_case						=					args.fpaa_opt_level

					if args.gpus > 1:
										_snake_case						=					'ddp'

					_snake_case						=					args.accumulate_grad_batches
					_snake_case						=					extra_train_kwargs.get('accelerator'							,     UpperCamelCase__    )
					_snake_case						=					extra_train_kwargs.get('profiler'							,     UpperCamelCase__    )

					_snake_case						=					pl.Trainer.from_argparse_args(
					    UpperCamelCase__							,     weights_summary=UpperCamelCase__							,     callbacks=[logging_callback] + extra_callbacks							,     logger=UpperCamelCase__							,     checkpoint_callback=UpperCamelCase__							,     **UpperCamelCase__							,     )

					if args.do_train:
										trainer.fit(UpperCamelCase__    )

					return trainer



