




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


_lowerCamelCase							=      logging.getLogger(__name__)

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

_lowerCamelCase							=      {
    '''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
_lowerCamelCase							=      {
    '''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
}
_lowerCamelCase							=      sorted(arg_to_scheduler.keys())
_lowerCamelCase							=      '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class        UpperCAmelCase__				(							pl.LightningModule				):



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase=None ,    _lowerCAmelCase="base" ,    _lowerCAmelCase=None ,    _lowerCAmelCase=None ,    _lowerCAmelCase=None ,    **_lowerCAmelCase ,    ):
            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	)
            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=__UpperCamelCase ,    **__UpperCamelCase ,    )
            else:
                   a			   =config

            a			   =("""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:
                   a			   =AutoTokenizer.from_pretrained(
                       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,    cache_dir=__UpperCamelCase ,    )
            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=__UpperCamelCase ,    )
            else:
                   a			   =model
     def        lowerCAmelCase__  (				self ,    *_lowerCAmelCase ,    **_lowerCAmelCase	):
            a			   =self.model_type.from_pretrained(*__UpperCamelCase ,    **__UpperCamelCase	)
     def        lowerCAmelCase__  (				self	):
            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	):
            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(
                       __UpperCamelCase ,    lr=self.hparams.learning_rate ,    scale_parameter=__UpperCamelCase ,    relative_step=__UpperCamelCase	)

            else:
                   a			   =AdamW(
                       __UpperCamelCase ,    lr=self.hparams.learning_rate ,    eps=self.hparams.adam_epsilon	)
            a			   =optimizer

            a			   =self.get_lr_scheduler()

            return [optimizer], [scheduler]
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            return self.validation_step(__UpperCamelCase ,    __UpperCamelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            return self.validation_end(__UpperCamelCase	)
     def        lowerCAmelCase__  (				self	):
            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 ,    _lowerCAmelCase	):






            if mode == "test":
                   a			   =len(self.test_dataloader().dataset	)
            else:
                   a			   =self.get_dataloader("""train""" ,    self.hparams.train_batch_size ,    shuffle=__UpperCamelCase	)
                   a			   =len(self.train_dataloader().dataset	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = False	):
            raise NotImplementedError("""You must implement this for your task"""	)
     def        lowerCAmelCase__  (				self	):
            return self.train_loader
     def        lowerCAmelCase__  (				self	):
            return self.get_dataloader("""dev""" ,    self.hparams.eval_batch_size ,    shuffle=__UpperCamelCase	)
     def        lowerCAmelCase__  (				self	):
            return self.get_dataloader("""test""" ,    self.hparams.eval_batch_size ,    shuffle=__UpperCamelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            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        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            a			   =self.output_dir.joinpath("""best_tfmr"""	)
            a			   =self.step_count
            self.model.save_pretrained(__UpperCamelCase	)
            self.tokenizer.save_pretrained(__UpperCamelCase	)






     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase ,    _lowerCAmelCase	):
            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				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            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(__UpperCamelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            rank_zero_info("""***** Validation results *****"""	)
            a			   =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        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            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(__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 							lowerCamelCase    (							UpperCAmelCase_	:      Optional[int]   ,       UpperCAmelCase_	:      List[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_	:      List[str]   ,       UpperCAmelCase_	:      Union[str, Any]   ,       UpperCAmelCase_	:      Dict=None   ,       UpperCAmelCase_	:      List[str]=True   ,       UpperCAmelCase_	:      List[str]=[]   ,       UpperCAmelCase_	:      Union[str, Any]=None   ,       UpperCAmelCase_	:      List[str]=None   ,       **UpperCAmelCase_	:      str   ,       )->			int:

       """simple docstring"""




       pl.seed_everything(args.seed      )

       # init model
       a			   =Path(model.hparams.output_dir      )
       odir.mkdir(exist_ok=UpperCAmelCase_      )

       # 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(UpperCAmelCase_      )
       if logging_callback is None:
              a			   =LoggingCallback()

       a			   ={}

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

       if args.gpus > 1:
              a			   ="""ddp"""

       a			   =args.accumulate_grad_batches
       a			   =extra_train_kwargs.get("""accelerator"""   ,       UpperCAmelCase_      )
       a			   =extra_train_kwargs.get("""profiler"""   ,       UpperCAmelCase_      )

       a			   =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






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a__         =     {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CvtForImageClassification''',
		    '''CvtModel''',
		    '''CvtPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCvtForImageClassification''',
		    '''TFCvtModel''',
		    '''TFCvtPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cvt import (
				    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CvtForImageClassification,
				    CvtModel,
				    CvtPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_cvt import (
				    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCvtForImageClassification,
				    TFCvtModel,
				    TFCvtPreTrainedModel,
				)


else:
		import sys

		a__         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)