




'''simple docstring'''



import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


UpperCAmelCase_    :					Optional[Any]					      =						get_logger(__name__)







def      _UpperCamelCase	(_lowerCamelCase							:       Dict		)->				Tuple:




     '''simple docstring'''





     return TestCommand(
         args.dataset				,		args.name				,		args.cache_dir				,		args.data_dir				,		args.all_configs				,		args.save_info or args.save_infos				,		args.ignore_verifications				,		args.force_redownload				,		args.clear_cache				,		)







class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: int	    =     False  # to tell pytest it's not a test class


       @staticmethod
       def  lowerCAmelCase (				__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					parser.add_parser('''test'''      ,					help='''Test dataset implementation.'''					)
            test_parser.add_argument('''--name'''      ,					type=__SCREAMING_SNAKE_CASE      ,					default=__SCREAMING_SNAKE_CASE      ,					help='''Dataset processing name'''					)
            test_parser.add_argument(
                '''--cache_dir'''      ,					type=__SCREAMING_SNAKE_CASE      ,					default=__SCREAMING_SNAKE_CASE      ,					help='''Cache directory where the datasets are stored.'''      ,					)
            test_parser.add_argument(
                '''--data_dir'''      ,					type=__SCREAMING_SNAKE_CASE      ,					default=__SCREAMING_SNAKE_CASE      ,					help='''Can be used to specify a manual directory to get the files from.'''      ,					)
            test_parser.add_argument('''--all_configs'''      ,					action='''store_true'''      ,					help='''Test all dataset configurations'''					)
            test_parser.add_argument(
                '''--save_info'''      ,					action='''store_true'''      ,					help='''Save the dataset infos in the dataset card (README.md)'''					)
            test_parser.add_argument(
                '''--ignore_verifications'''      ,					action='''store_true'''      ,					help='''Run the test without checksums and splits checks.'''      ,					)
            test_parser.add_argument('''--force_redownload'''      ,					action='''store_true'''      ,					help='''Force dataset redownload'''					)
            test_parser.add_argument(
                '''--clear_cache'''      ,					action='''store_true'''      ,					help='''Remove downloaded files and cached datasets after each config test'''      ,					)
            # aliases
            test_parser.add_argument('''--save_infos'''      ,					action='''store_true'''      ,					help='''alias to save_info'''					)
            test_parser.add_argument('''dataset'''      ,					type=__SCREAMING_SNAKE_CASE      ,					help='''Name of the dataset to download'''					)
            test_parser.set_defaults(func=_test_command_factory					)


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					) ->					Any:



            '''simple docstring'''




            __snake_case         =					dataset
            __snake_case         =					name
            __snake_case         =					cache_dir
            __snake_case         =					data_dir
            __snake_case         =					all_configs
            __snake_case         =					save_infos
            __snake_case         =					ignore_verifications
            __snake_case         =					force_redownload
            __snake_case         =					clear_cache
            if clear_cache and not cache_dir:
                 print(
                     '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
                     '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
                     '''Please provide a --cache_dir that will be used to test the dataset script.'''					)
                 exit(1					)
            if save_infos:
                 __snake_case         =					True




       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            fl_logger().setLevel(__SCREAMING_SNAKE_CASE					)
            if self._name is not None and self._all_configs:
                 print('''Both parameters `config` and `all_configs` can\'t be used at once.'''					)
                 exit(1					)
            __snake_case   ,      __snake_case         =					self._dataset, self._name
            __snake_case         =					dataset_module_factory(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					import_main_class(module.module_path					)
            __snake_case         =					len(builder_cls.BUILDER_CONFIGS					) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

            def get_builders() -> Generator[DatasetBuilder, None, None]:
                 if self._all_configs and builder_cls.BUILDER_CONFIGS:
                      for i, config in enumerate(builder_cls.BUILDER_CONFIGS					):
                           if "config_name" in module.builder_kwargs:
                                yield builder_cls(
                                    cache_dir=self._cache_dir      ,					data_dir=self._data_dir      ,					**module.builder_kwargs      ,					)
                           else:
                                yield builder_cls(
                                    config_name=config.name      ,					cache_dir=self._cache_dir      ,					data_dir=self._data_dir      ,					**module.builder_kwargs      ,					)
                 else:
                      if "config_name" in module.builder_kwargs:
                           yield builder_cls(cache_dir=self._cache_dir      ,					data_dir=self._data_dir      ,					**module.builder_kwargs					)
                      else:
                           yield builder_cls(
                               config_name=__SCREAMING_SNAKE_CASE      ,					cache_dir=self._cache_dir      ,					data_dir=self._data_dir      ,					**module.builder_kwargs      ,					)

            for j, builder in enumerate(get_builders()					):
                 print(F'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''					)
                 __snake_case         =					os.path.exists(
                     os.path.join(builder.get_imported_module_dir()      ,					datasets.config.DATASETDICT_INFOS_FILENAME					)					)  # record checksums only if we need to update a (deprecated) dataset_infos.json
                 builder.download_and_prepare(
                     download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                     if not self._force_redownload
                     else DownloadMode.FORCE_REDOWNLOAD      ,					verification_mode=VerificationMode.NO_CHECKS
                     if self._ignore_verifications
                     else VerificationMode.ALL_CHECKS      ,					try_from_hf_gcs=__SCREAMING_SNAKE_CASE      ,					)
                 builder.as_dataset()
                 if self._save_infos:
                      builder._save_infos()

                 # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
                 # The dataset_infos are saved in the YAML part of the README.md

                 # Let's move it to the original directory of the dataset script, to allow the user to
                 # upload them on S3 at the same time afterwards.
                 if self._save_infos:
                      __snake_case         =					os.path.join(builder_cls.get_imported_module_dir()      ,					'''README.md'''					)
                      __snake_case         =					Path(__SCREAMING_SNAKE_CASE					).name + '''.py'''
                      __snake_case         =					os.path.join(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
                      if os.path.isfile(__SCREAMING_SNAKE_CASE					):
                           __snake_case         =					os.path.dirname(__SCREAMING_SNAKE_CASE					)
                      elif os.path.isfile(__SCREAMING_SNAKE_CASE					):
                           __snake_case         =					path
                      elif os.path.isdir(__SCREAMING_SNAKE_CASE					):  # for local directories containing only data files
                           __snake_case         =					path
                      else:  # in case of a remote dataset
                           __snake_case         =					None
                           print(F'''Dataset card saved at {dataset_readme_path}'''					)

                      # Move dataset_info back to the user
                      if dataset_dir is not None:
                           __snake_case         =					os.path.join(__SCREAMING_SNAKE_CASE      ,					'''README.md'''					)
                           copyfile(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
                           print(F'''Dataset card saved at {user_dataset_readme_path}'''					)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
                 if self._clear_cache:
                      if os.path.isdir(builder._cache_dir					):
                           logger.warning(F'''Clearing cache at {builder._cache_dir}'''					)
                           rmtree(builder._cache_dir					)
                      __snake_case         =					os.path.join(self._cache_dir      ,					datasets.config.DOWNLOADED_DATASETS_DIR					)
                      if os.path.isdir(__SCREAMING_SNAKE_CASE					):
                           logger.warning(F'''Clearing cache at {download_dir}'''					)
                           rmtree(__SCREAMING_SNAKE_CASE					)

            print('''Test successful.'''					)












'''simple docstring'''



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_    :					Tuple					      =						logging.getLogger(__name__)

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

UpperCAmelCase_    :					List[str]					      =						{
    '''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_    :					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,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCAmelCase_    :					Dict					      =						sorted(arg_to_scheduler.keys())
UpperCAmelCase_    :					List[Any]					      =						'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class       lowerCAmelCase				(	pl.LightningModule):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE="base"      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					Optional[int]:



            '''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(__SCREAMING_SNAKE_CASE					)
            __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=__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE      ,					)
            else:
                 __snake_case         =					config

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

            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=__SCREAMING_SNAKE_CASE      ,					)
            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=__SCREAMING_SNAKE_CASE      ,					)
            else:
                 __snake_case         =					model


       def  lowerCAmelCase (				self      ,					*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __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					) ->					Dict:



            '''simple docstring'''




            __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(
                     __SCREAMING_SNAKE_CASE      ,					lr=self.hparams.learning_rate      ,					scale_parameter=__SCREAMING_SNAKE_CASE      ,					relative_step=__SCREAMING_SNAKE_CASE					)

            else:
                 __snake_case         =					AdamW(
                     __SCREAMING_SNAKE_CASE      ,					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      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




            return self.validation_step(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					List[str]:



            '''simple docstring'''




            return self.validation_end(__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __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      ,					__SCREAMING_SNAKE_CASE					) ->					str:



            '''simple docstring'''




            if mode == "test":
                 __snake_case         =					len(self.test_dataloader().dataset					)
            else:
                 __snake_case         =					self.get_dataloader('''train'''      ,					self.hparams.train_batch_size      ,					shuffle=__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					len(self.train_dataloader().dataset					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = False					) ->					str:



            '''simple docstring'''




            raise NotImplementedError('''You must implement this for your task'''					)


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            return self.train_loader


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            return self.get_dataloader('''dev'''      ,					self.hparams.eval_batch_size      ,					shuffle=__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            return self.get_dataloader('''test'''      ,					self.hparams.eval_batch_size      ,					shuffle=__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            return os.path.join(
                self.hparams.data_dir      ,					'''cached_{}_{}_{}'''.format(
                    __SCREAMING_SNAKE_CASE      ,					list(filter(__SCREAMING_SNAKE_CASE      ,					self.hparams.model_name_or_path.split('''/'''					)					)					).pop()      ,					str(self.hparams.max_seq_length					)      ,					)      ,					)


       @pl.utilities.rank_zero_only
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					self.output_dir.joinpath('''best_tfmr'''					)
            __snake_case         =					self.step_count
            self.model.save_pretrained(__SCREAMING_SNAKE_CASE					)
            self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE					)




       @staticmethod
       def  lowerCAmelCase (				__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




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





class       lowerCAmelCase				(	pl.Callback):


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __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(__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




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




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            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(__SCREAMING_SNAKE_CASE      ,					'''w'''					) as writer:
                 for key in sorted(__SCREAMING_SNAKE_CASE					):
                      if key not in ["log", "progress_bar"]:
                           rank_zero_info('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE      ,					str(metrics[key]					)					)					)
                           writer.write('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE      ,					str(metrics[key]					)					)					)







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[int]				,		_lowerCamelCase							:       Optional[Any]		)->				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      _UpperCamelCase	(_lowerCamelCase							:       BaseTransformer				,		_lowerCamelCase							:       argparse.Namespace				,		_lowerCamelCase							:       Any=None				,		_lowerCamelCase							:       Union[str, Any]=True				,		_lowerCamelCase							:       Union[str, Any]=[]				,		_lowerCamelCase							:       int=None				,		_lowerCamelCase							:       Any=None				,		**_lowerCamelCase							:       Tuple				,		)->				Optional[Any]:




     '''simple docstring'''





     pl.seed_everything(args.seed		)

     # init model
     __snake_case         =					Path(model.hparams.output_dir		)
     odir.mkdir(exist_ok=_lowerCamelCase		)

     # 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(_lowerCamelCase		)
     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'''				,		_lowerCamelCase		)
     __snake_case         =					extra_train_kwargs.get('''profiler'''				,		_lowerCamelCase		)

     __snake_case         =					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






