






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


A__     :      Dict             =			logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

A__     :      List[Any]             =			{
    "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
A__     :      List[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
}
A__     :      Tuple             =			sorted(arg_to_scheduler.keys())
A__     :      Tuple             =			"{" + ", ".join(arg_to_scheduler_choices) + "}"
class 				_UpperCAmelCase (	pl.LightningModule     ):




				def __init__(					self						:   Dict,  lowerCamelCase						:   argparse.Namespace,  lowerCamelCase						:   List[Any]=None,  lowerCamelCase						:   Dict="base",  lowerCamelCase						:   str=None,  lowerCamelCase						:   List[str]=None,  lowerCamelCase						:   List[str]=None,  **lowerCamelCase						:   Optional[Any],  ):

								'''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(UpperCamelCase_  )
								lowercase__       			=      0
								lowercase__       			=      Path(self.hparams.output_dir  )
								lowercase__       			=      self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
												lowercase__       			=      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=UpperCamelCase_,  **UpperCamelCase_,  )
								else:
												lowercase__       			=      config

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

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




				def     lowercase__       (					self						:   int,  *lowerCamelCase						:   Any,  **lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.model_type.from_pretrained(*UpperCamelCase_,  **UpperCamelCase_  )




				def     lowercase__       (					self						:   Any  ):

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




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      self.model
								lowercase__       			=      ['''bias''', '''LayerNorm.weight''']
								lowercase__       			=      [
								    {
								        '''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:
												lowercase__       			=      Adafactor(
												    UpperCamelCase_,  lr=self.hparams.learning_rate,  scale_parameter=UpperCamelCase_,  relative_step=UpperCamelCase_  )

								else:
												lowercase__       			=      AdamW(
												    UpperCamelCase_,  lr=self.hparams.learning_rate,  eps=self.hparams.adam_epsilon  )
								lowercase__       			=      optimizer

								lowercase__       			=      self.get_lr_scheduler()

								return [optimizer], [scheduler]




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   int,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								return self.validation_step(UpperCamelCase_,  UpperCamelCase_  )




				def     lowercase__       (					self						:   Optional[int],  lowerCamelCase						:   str  ):

								'''simple docstring'''
								return self.validation_end(UpperCamelCase_  )




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								lowercase__       			=      max(1,  self.hparams.gpus  )  # TODO: consider num_tpu_cores
								lowercase__       			=      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
								return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   str  ):

								'''simple docstring'''



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




				def     lowercase__       (					self						:   List[Any],  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   bool = False  ):

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




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								return self.train_loader




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								return self.get_dataloader('''dev''',  self.hparams.eval_batch_size,  shuffle=UpperCamelCase_  )




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								return self.get_dataloader('''test''',  self.hparams.eval_batch_size,  shuffle=UpperCamelCase_  )




				def     lowercase__       (					self						:   str,  lowerCamelCase						:   Optional[int]  ):

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




				@pl.utilities.rank_zero_only
				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   Dict[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.output_dir.joinpath('''best_tfmr'''  )
								lowercase__       			=      self.step_count
								self.model.save_pretrained(UpperCamelCase_  )
								self.tokenizer.save_pretrained(UpperCamelCase_  )




				@staticmethod
				def     lowercase__       (					lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Optional[int]  ):

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



class 				_UpperCAmelCase (	pl.Callback     ):




				def     lowercase__       (					self						:   Union[str, Any],  lowerCamelCase						:   int,  lowerCamelCase						:   int  ):

								'''simple docstring'''
								lowercase__       			=      trainer.lr_schedulers[0]['''scheduler''']
								lowercase__       			=      {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
								pl_module.logger.log_metrics(UpperCamelCase_  )




				def     lowercase__       (					self						:   Dict,  lowerCamelCase						:   pl.Trainer,  lowerCamelCase						:   pl.LightningModule  ):

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




				def     lowercase__       (					self						:   Union[str, Any],  lowerCamelCase						:   pl.Trainer,  lowerCamelCase						:   pl.LightningModule  ):

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






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''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   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=True		,       lowerCamelCase_=[]		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		,       ):






				'''simple docstring'''
				pl.seed_everything(args.seed    )

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

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

				lowercase__       			=      {}

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

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

				lowercase__       			=      args.accumulate_grad_batches
				lowercase__       			=      extra_train_kwargs.get('''accelerator'''		,       __lowercase    )
				lowercase__       			=      extra_train_kwargs.get('''profiler'''		,       __lowercase    )

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

				if args.do_train:
								trainer.fit(__lowercase    )

				return trainer












from typing import TYPE_CHECKING

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


A__     :      int             =			{
    '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:
							A__     :      List[Any]             =			[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      int             =			[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Tuple             =			['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

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




