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


lowercase__      :    str          =     logging.getLogger(__name__)

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

lowercase__      :    Dict          =     {
    '''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
lowercase__      :    Union[str, Any]          =     {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase__      :    int          =     sorted(arg_to_scheduler.keys())
lowercase__      :    Any          =     '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''




class        lowercase_  (  pl.LightningModule						):



	"""simple docstring"""





	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		,      )						->Dict:
								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    )
								lowerCAmelCase     		=		0
								lowerCAmelCase     		=		Path(self.hparams.output_dir    )
								lowerCAmelCase     		=		self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
															lowerCAmelCase     		=		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:
															lowerCAmelCase     		=		config

								lowerCAmelCase     		=		('''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:
															lowerCAmelCase     		=		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:
															lowerCAmelCase     		=		tokenizer
								lowerCAmelCase     		=		MODEL_MODES[mode]
								if model is None:
															lowerCAmelCase     		=		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:
															lowerCAmelCase     		=		model


	def       SCREAMING_SNAKE_CASE_    (							self		,      *__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )						->Any:
								lowerCAmelCase     		=		self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )


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


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

								else:
															lowerCAmelCase     		=		AdamW(
															    __SCREAMING_SNAKE_CASE		,      lr=self.hparams.learning_rate		,      eps=self.hparams.adam_epsilon    )
								lowerCAmelCase     		=		optimizer

								lowerCAmelCase     		=		self.get_lr_scheduler()

								return [optimizer], [scheduler]


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->int:
								return self.validation_step(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[Any]:
								return self.validation_end(__SCREAMING_SNAKE_CASE    )


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


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								if mode == "test":
															lowerCAmelCase     		=		len(self.test_dataloader().dataset    )
								else:
															lowerCAmelCase     		=		self.get_dataloader('''train'''		,      self.hparams.train_batch_size		,      shuffle=__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		len(self.train_dataloader().dataset    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE = False    )						->Optional[Any]:
								raise NotImplementedError('''You must implement this for your task'''    )


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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								return self.get_dataloader('''dev'''		,      self.hparams.eval_batch_size		,      shuffle=__SCREAMING_SNAKE_CASE    )


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


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								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       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		self.output_dir.joinpath('''best_tfmr'''    )
								lowerCAmelCase     		=		self.step_count
								self.model.save_pretrained(__SCREAMING_SNAKE_CASE    )
								self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE    )




	@staticmethod
	def       SCREAMING_SNAKE_CASE_    (							__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								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        lowercase_  (  pl.Callback						):



	"""simple docstring"""





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


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->List[Any]:
								rank_zero_info('''***** Validation results *****'''    )
								lowerCAmelCase     		=		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       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								rank_zero_info('''***** Test results *****'''    )
								lowerCAmelCase     		=		trainer.callback_metrics
								# Log and save results to file
								lowerCAmelCase     		=		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        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     None:
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							parser.add_argument(
							    '''--output_dir'''       ,  default=snake_case__       ,  type=snake_case__       ,  required=snake_case__       ,  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=snake_case__       ,  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=snake_case__							)
							parser.add_argument('''--max_grad_norm'''       ,  dest='''gradient_clip_val'''       ,  default=1.0       ,  type=snake_case__       ,  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=snake_case__       ,  default=1       ,  help='''Number of updates steps to accumulate before performing a backward/update pass.'''       ,  )
							parser.add_argument('''--seed'''       ,  type=snake_case__       ,  default=4_2       ,  help='''random seed for initialization'''							)
							parser.add_argument(
							    '''--data_dir'''       ,  default=snake_case__       ,  type=snake_case__       ,  required=snake_case__       ,  help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''       ,  )






def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=[]       ,  snake_case__=None       ,  snake_case__=None       ,  **snake_case__       ,  )					->     str:
							pl.seed_everything(args.seed							)

							# init model
							lowerCAmelCase     		=		Path(model.hparams.output_dir							)
							odir.mkdir(exist_ok=snake_case__							)

							# add custom checkpoints
							if checkpoint_callback is None:
														lowerCAmelCase     		=		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(snake_case__							)
							if logging_callback is None:
														lowerCAmelCase     		=		LoggingCallback()

							lowerCAmelCase     		=		{}

							# TODO: remove with PyTorch 1.6 since pl uses native amp
							if args.fpaa:
														lowerCAmelCase     		=		1_6
														lowerCAmelCase     		=		args.fpaa_opt_level

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

							lowerCAmelCase     		=		args.accumulate_grad_batches
							lowerCAmelCase     		=		extra_train_kwargs.get('''accelerator'''       ,  snake_case__							)
							lowerCAmelCase     		=		extra_train_kwargs.get('''profiler'''       ,  snake_case__							)

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

							if args.do_train:
														trainer.fit(snake_case__							)

							return trainer




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__      :    Dict          =     {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     [
	    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SamModel''',
	    '''SamPreTrainedModel''',
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    str          =     [
	    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSamModel''',
	    '''TFSamPreTrainedModel''',
	]
try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     ['''SamImageProcessor''']


if TYPE_CHECKING:
	from .configuration_sam import (
	    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SamConfig,
	    SamMaskDecoderConfig,
	    SamPromptEncoderConfig,
	    SamVisionConfig,
	)
	from .processing_sam import SamProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_sam import SamImageProcessor

else:
	import sys

	lowercase__      :    Dict          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



