






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


SCREAMING_SNAKE_CASE								=  logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

SCREAMING_SNAKE_CASE								=  {
    '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
SCREAMING_SNAKE_CASE								=  {
    '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
}
SCREAMING_SNAKE_CASE								=  sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE								=  '{' + ', '.join(arg_to_scheduler_choices) + '}'
class 							A_      (  pl.LightningModule     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A=None							,					_A="base"							,					_A=None							,					_A=None							,					_A=None							,					**_A							,					)	->						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(_A)
     _UpperCAmelCase			: Union[str, Any]     					=			0
     _UpperCAmelCase			: List[str]     					=			Path(self.hparams.output_dir)
     _UpperCAmelCase			: Optional[int]     					=			self.hparams.cache_dir if self.hparams.cache_dir else None
     if config is None:
         _UpperCAmelCase			: Union[str, Any]     					=			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=_A							,					**_A							,					)
     else:
         _UpperCAmelCase			: PretrainedConfig     					=			config

     _UpperCAmelCase			: Optional[Any]     					=			('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
     for p in extra_model_params:
         if getattr(self.hparams							,					_A							,					_A):
             assert hasattr(self.config							,					_A), f'''model config doesn\'t have a `{p}` attribute'''
             setattr(self.config							,					_A							,					getattr(self.hparams							,					_A))

     if tokenizer is None:
         _UpperCAmelCase			: int     					=			AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,					cache_dir=_A							,					)
     else:
         _UpperCAmelCase			: PreTrainedTokenizer     					=			tokenizer
     _UpperCAmelCase			: Union[str, Any]     					=			MODEL_MODES[mode]
     if model is None:
         _UpperCAmelCase			: Tuple     					=			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=_A							,					)
     else:
         _UpperCAmelCase			: int     					=			model


 def 	snake_case__			(		self							,					*_A							,					**_A)	->						Tuple:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			self.model_type.from_pretrained(*_A							,					**_A)


 def 	snake_case__			(		self)	->						str:

     """simple docstring"""



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


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			self.model
     _UpperCAmelCase			: int     					=			['''bias''', '''LayerNorm.weight''']
     _UpperCAmelCase			: List[str]     					=			[
         {
             '''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:
         _UpperCAmelCase			: List[Any]     					=			Adafactor(
             _A							,					lr=self.hparams.learning_rate							,					scale_parameter=_A							,					relative_step=_A)

     else:
         _UpperCAmelCase			: str     					=			AdamW(
             _A							,					lr=self.hparams.learning_rate							,					eps=self.hparams.adam_epsilon)
     _UpperCAmelCase			: str     					=			optimizer

     _UpperCAmelCase			: Dict     					=			self.get_lr_scheduler()

     return [optimizer], [scheduler]


 def 	snake_case__			(		self							,					_A							,					_A)	->						Optional[int]:

     """simple docstring"""



     return self.validation_step(_A							,					_A)


 def 	snake_case__			(		self							,					_A)	->						Dict:

     """simple docstring"""



     return self.validation_end(_A)


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			max(1							,					self.hparams.gpus)  # TODO: consider num_tpu_cores
     _UpperCAmelCase			: List[Any]     					=			self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
     return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


 def 	snake_case__			(		self							,					_A)	->						int:

     """simple docstring"""



     if mode == "test":
         _UpperCAmelCase			: Tuple     					=			len(self.test_dataloader().dataset)
     else:
         _UpperCAmelCase			: int     					=			self.get_dataloader('''train'''							,					self.hparams.train_batch_size							,					shuffle=_A)
         _UpperCAmelCase			: Union[str, Any]     					=			len(self.train_dataloader().dataset)


 def 	snake_case__			(		self							,					_A							,					_A							,					_A = False)	->						int:

     """simple docstring"""



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


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

     """simple docstring"""



     return self.train_loader


 def 	snake_case__			(		self)	->						str:

     """simple docstring"""



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


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A)	->						Optional[int]:

     """simple docstring"""



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


 @pl.utilities.rank_zero_only
 def 	snake_case__			(		self							,					_A)	->						None:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			self.output_dir.joinpath('''best_tfmr''')
     _UpperCAmelCase			: str     					=			self.step_count
     self.model.save_pretrained(_A)
     self.tokenizer.save_pretrained(_A)


 @staticmethod
 def 	snake_case__			(		_A							,					_A)	->						int:

     """simple docstring"""



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



class 							A_      (  pl.Callback     ):






 '''simple docstring'''




 def 	snake_case__			(		self							,					_A							,					_A)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			trainer.lr_schedulers[0]['''scheduler''']
     _UpperCAmelCase			: Dict     					=			{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr())}
     pl_module.logger.log_metrics(_A)


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

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A)	->						Optional[Any]:

     """simple docstring"""



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



def        _lowerCamelCase		(  __A      :							Optional[Any]						,						__A      :							List[str]	) ->   None:
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        '''--output_dir'''						,						default=__A						,						type=__A						,						required=__A						,						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=__A						,						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=__A	)
    parser.add_argument('''--max_grad_norm'''						,						dest='''gradient_clip_val'''						,						default=1.0						,						type=__A						,						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=__A						,						default=1						,						help='''Number of updates steps to accumulate before performing a backward/update pass.'''						,						)
    parser.add_argument('''--seed'''						,						type=__A						,						default=42						,						help='''random seed for initialization'''	)
    parser.add_argument(
        '''--data_dir'''						,						default=__A						,						type=__A						,						required=__A						,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''						,						)


def        _lowerCamelCase		(  __A      :							BaseTransformer						,						__A      :							argparse.Namespace						,						__A      :							Union[str, Any]=None						,						__A      :							Optional[int]=True						,						__A      :							Optional[Any]=[]						,						__A      :							List[Any]=None						,						__A      :							Dict=None						,						**__A      :							Dict						,						) ->   Union[str, Any]:
    pl.seed_everything(args.seed	)

    # init model
    _UpperCAmelCase			: Union[str, Any]     					=			Path(model.hparams.output_dir	)
    odir.mkdir(exist_ok=__A	)

    # add custom checkpoints
    if checkpoint_callback is None:
        _UpperCAmelCase			: Union[str, Any]     					=			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(__A	)
    if logging_callback is None:
        _UpperCAmelCase			: Optional[Any]     					=			LoggingCallback()

    _UpperCAmelCase			: Dict     					=			{}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        _UpperCAmelCase			: str     					=			16
        _UpperCAmelCase			: Optional[int]     					=			args.fpaa_opt_level

    if args.gpus > 1:
        _UpperCAmelCase			: Union[str, Any]     					=			'''ddp'''

    _UpperCAmelCase			: Union[str, Any]     					=			args.accumulate_grad_batches
    _UpperCAmelCase			: Optional[Any]     					=			extra_train_kwargs.get('''accelerator'''						,						__A	)
    _UpperCAmelCase			: int     					=			extra_train_kwargs.get('''profiler'''						,						__A	)

    _UpperCAmelCase			: Any     					=			pl.Trainer.from_argparse_args(
        __A						,						weights_summary=__A						,						callbacks=[logging_callback] + extra_callbacks						,						logger=__A						,						checkpoint_callback=__A						,						**__A						,						)

    if args.do_train:
        trainer.fit(__A	)

    return trainer












from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   "swiftformer"


 def __init__(		self							,					_A=3							,					_A=[3, 3, 6, 4]							,					_A=[48, 56, 112, 220]							,					_A=4							,					_A=[True, True, True, True]							,					_A="gelu"							,					_A=3							,					_A=2							,					_A=1							,					_A=0.0							,					_A=True							,					_A=1e-5							,					_A=1e-5							,					**_A							,					)	->						List[Any]:

     """simple docstring"""



     super().__init__(**_A)
     _UpperCAmelCase			: Tuple     					=			num_channels
     _UpperCAmelCase			: Optional[int]     					=			depths
     _UpperCAmelCase			: Union[str, Any]     					=			embed_dims
     _UpperCAmelCase			: Dict     					=			mlp_ratio
     _UpperCAmelCase			: Tuple     					=			downsamples
     _UpperCAmelCase			: str     					=			hidden_act
     _UpperCAmelCase			: int     					=			down_patch_size
     _UpperCAmelCase			: Dict     					=			down_stride
     _UpperCAmelCase			: Dict     					=			down_pad
     _UpperCAmelCase			: Tuple     					=			drop_path_rate
     _UpperCAmelCase			: Tuple     					=			use_layer_scale
     _UpperCAmelCase			: int     					=			layer_scale_init_value
     _UpperCAmelCase			: int     					=			batch_norm_eps



class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     List[Any]	=   version.parse("1.11"     )


 @property
 def 	snake_case__			(		self)	->						Mapping[str, Mapping[int, str]]:

     """simple docstring"""



     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ])


 @property
 def 	snake_case__			(		self)	->						float:

     """simple docstring"""



     return 1e-4




