"""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


__magic_name__    				=					logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

__magic_name__    				=					{
    "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
__magic_name__    				=					{
    "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
}
__magic_name__    				=					sorted(arg_to_scheduler.keys())
__magic_name__    				=					"{" + ", ".join(arg_to_scheduler_choices) + "}"

class SCREAMING_SNAKE_CASE     (    pl.LightningModule	):







	"""simple docstring"""


	def __init__(   self		:      Dict					,   _snake_case		:      Optional[Any]					,   _snake_case		:      List[str]=None					,   _snake_case		:      List[str]="base"					,   _snake_case		:      Dict=None					,   _snake_case		:      int=None					,   _snake_case		:      Tuple=None					,   **_snake_case		:      int					,   )				->			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(lowerCamelCase__				)
				a__     				=		0
				a__     				=		Path(self.hparams.output_dir				)
				a__     				=		self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
							a__     				=		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:
							a__     				=		config

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


	def        _lowerCAmelCase				(   self		:      Optional[Any]					,   *_snake_case		:      Dict					,   **_snake_case		:      Optional[Any]				)				->			Tuple:




				'''simple docstring'''






				a__     				=		self.model_type.from_pretrained(*lowerCamelCase__					,   **lowerCamelCase__				)


	def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Any:




				'''simple docstring'''






				a__     				=		arg_to_scheduler[self.hparams.lr_scheduler]
				a__     				=		get_schedule_func(
				    self.opt					,   num_warmup_steps=self.hparams.warmup_steps					,   num_training_steps=self.total_steps()				)
				a__     				=		{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
				return scheduler


	def        _lowerCAmelCase				(   self		:      List[Any]				)				->			List[Any]:




				'''simple docstring'''






				a__     				=		self.model
				a__     				=		['bias', 'LayerNorm.weight']
				a__     				=		[
				    {
				        '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:
							a__     				=		Adafactor(
							    lowerCamelCase__					,   lr=self.hparams.learning_rate					,   scale_parameter=lowerCamelCase__					,   relative_step=lowerCamelCase__				)

				else:
							a__     				=		AdamW(
							    lowerCamelCase__					,   lr=self.hparams.learning_rate					,   eps=self.hparams.adam_epsilon				)
				a__     				=		optimizer

				a__     				=		self.get_lr_scheduler()

				return [optimizer], [scheduler]


	def        _lowerCAmelCase				(   self		:      Dict					,   _snake_case		:      str					,   _snake_case		:      List[str]				)				->			Union[str, Any]:




				'''simple docstring'''






				return self.validation_step(lowerCamelCase__					,   lowerCamelCase__				)


	def        _lowerCAmelCase				(   self		:      Any					,   _snake_case		:      int				)				->			Union[str, Any]:




				'''simple docstring'''






				return self.validation_end(lowerCamelCase__				)


	def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			int:




				'''simple docstring'''






				a__     				=		max(1					,   self.hparams.gpus				)  # TODO: consider num_tpu_cores
				a__     				=		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		:      Tuple					,   _snake_case		:      Optional[Any]				)				->			Optional[int]:




				'''simple docstring'''






				if stage == "test":
							a__     				=		len(self.test_dataloader().dataset				)
				else:
							a__     				=		self.get_dataloader('train'					,   self.hparams.train_batch_size					,   shuffle=lowerCamelCase__				)
							a__     				=		len(self.train_dataloader().dataset				)


	def        _lowerCAmelCase				(   self		:      Any					,   _snake_case		:      List[Any]					,   _snake_case		:      str					,   _snake_case		:      List[Any] = False				)				->			Dict:




				'''simple docstring'''






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


	def        _lowerCAmelCase				(   self		:      Tuple				)				->			List[str]:




				'''simple docstring'''






				return self.train_loader


	def        _lowerCAmelCase				(   self		:      str				)				->			Any:




				'''simple docstring'''






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


	def        _lowerCAmelCase				(   self		:      Dict				)				->			List[str]:




				'''simple docstring'''






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


	def        _lowerCAmelCase				(   self		:      Any					,   _snake_case		:      Union[str, Any]				)				->			int:




				'''simple docstring'''






				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		:      Optional[int]					,   _snake_case		:      Optional[int]				)				->			None:




				'''simple docstring'''






				a__     				=		self.output_dir.joinpath('best_tfmr'				)
				a__     				=		self.step_count
				self.model.save_pretrained(lowerCamelCase__				)
				self.tokenizer.save_pretrained(lowerCamelCase__				)



	@staticmethod
	def        _lowerCAmelCase				(   _snake_case		:      Optional[int]					,   _snake_case		:      Tuple				)				->			Union[str, Any]:




				'''simple docstring'''






				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 SCREAMING_SNAKE_CASE     (    pl.Callback	):







	"""simple docstring"""


	def        _lowerCAmelCase				(   self		:      Optional[Any]					,   _snake_case		:      int					,   _snake_case		:      str				)				->			Optional[Any]:




				'''simple docstring'''






				if (
				    trainer.is_global_zero and trainer.global_rank == 0
				):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
							pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.

class SCREAMING_SNAKE_CASE     (    pl.Callback	):







	"""simple docstring"""


	def        _lowerCAmelCase				(   self		:      str					,   _snake_case		:      Any					,   _snake_case		:      Tuple				)				->			Tuple:




				'''simple docstring'''






				a__     				=		trainer.lr_schedulers[0]['scheduler']
				a__     				=		{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
				pl_module.logger.log_metrics(lowerCamelCase__				)


	def        _lowerCAmelCase				(   self		:      Any					,   _snake_case		:      Tuple					,   _snake_case		:      Tuple				)				->			Any:




				'''simple docstring'''






				rank_zero_info('***** Validation results *****'				)
				a__     				=		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		:      str					,   _snake_case		:      str					,   _snake_case		:      Optional[Any]				)				->			List[Any]:




				'''simple docstring'''






				rank_zero_info('***** Test results *****'				)
				a__     				=		trainer.callback_metrics
				# Log and save results to file
				a__     				=		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__,UpperCAmelCase__					)							->     Dict:


			'''simple docstring'''


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





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__=None,UpperCAmelCase__=True,UpperCAmelCase__=None,UpperCAmelCase__=[],UpperCAmelCase__=None,UpperCAmelCase__=None,**UpperCAmelCase__,)							->     Tuple:


			'''simple docstring'''


			pl.seed_everything(args.seed					)

			# init model
			a__     				=		Path(model.hparams.output_dir					)
			odir.mkdir(exist_ok=lowercase__					)

			# add custom checkpoints
			if checkpoint_callback is None:
						a__     				=		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(lowercase__					)
			if logging_callback is None:
						a__     				=		LoggingCallback()

			a__     				=		{}

			# TODO: remove with PyTorch 1.6 since pl uses native amp
			if args.fpaa:
						a__     				=		16
						# train_params["amp_level"] = args.fp16_opt_level

			if args.gpus > 1:
						a__     				=		'auto'  # "ddp"
						a__     				=		'ddp'

			a__     				=		args.accumulate_grad_batches
			a__     				=		None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
			a__     				=		'auto'

			a__     				=		pl.Trainer.from_argparse_args(
			    lowercase__,weights_summary=lowercase__,callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()],logger=lowercase__,**lowercase__,)

			if args.do_train:
						trainer.fit(lowercase__					)

			return trainer



"""simple docstring"""



import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand





def   _lowerCamelCase ( UpperCAmelCase__					)							->     Any:


   '''simple docstring'''


   return EnvironmentCommand()




class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      ArgumentParser				)				->			int:




    '''simple docstring'''






    a__     				=		parser.add_parser('env'					,   help='Print relevant system environment info.'				)
    download_parser.set_defaults(func=_snake_case				)


 def        _lowerCAmelCase				(   self		:      List[str]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		{
        '`datasets` version': version,
        'Platform': platform.platform(),
        'Python version': platform.python_version(),
        'Huggingface_hub version': huggingface_hub.__version__,
        'PyArrow version': pyarrow.__version__,
        'Pandas version': pandas.__version__,
    }

    print('\nCopy-and-paste the text below in your GitHub issue.\n'				)
    print(self.format_dict(_snake_case				)				)

    return info



 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      Tuple				)				->			Optional[int]:




    '''simple docstring'''






    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]				) + "\n"


