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__	:  Dict						=  logging.getLogger(__name__)

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

lowerCAmelCase__	:  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
lowerCAmelCase__	:  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
}
lowerCAmelCase__	:  Dict						=  sorted(arg_to_scheduler.keys())
lowerCAmelCase__	:  Any						=  '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class 					__snake_case    (     pl.LightningModule      ):




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase=None       ,			__UpperCamelCase="base"       ,			__UpperCamelCase=None       ,			__UpperCamelCase=None       ,			__UpperCamelCase=None       ,			**__UpperCamelCase       ,			)	->    Tuple:



									'''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						)
									snake_case__   :      Dict   				= 0
									snake_case__   :      Optional[int]   				= Path(self.hparams.output_dir						)
									snake_case__   :      Tuple   				= self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
													snake_case__   :      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=__UpperCamelCase       ,			**__UpperCamelCase       ,			)
									else:
													snake_case__   :      PretrainedConfig   				= config

									snake_case__   :      List[Any]   				= ('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:
													snake_case__   :      List[Any]   				= AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,			cache_dir=__UpperCamelCase       ,			)
									else:
													snake_case__   :      PreTrainedTokenizer   				= tokenizer
									snake_case__   :      str   				= MODEL_MODES[mode]
									if model is None:
													snake_case__   :      str   				= 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:
													snake_case__   :      Union[str, Any]   				= model




					def      __a     (  self       ,			*__UpperCamelCase       ,			**__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= self.model_type.from_pretrained(*__UpperCamelCase       ,			**__UpperCamelCase						)




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







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




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Tuple   				= self.model
									snake_case__   :      List[str]   				= ['bias', 'LayerNorm.weight']
									snake_case__   :      Dict   				= [
									    {
									        '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:
													snake_case__   :      Tuple   				= Adafactor(
													    __UpperCamelCase       ,			lr=self.hparams.learning_rate       ,			scale_parameter=__UpperCamelCase       ,			relative_step=__UpperCamelCase						)

									else:
													snake_case__   :      Any   				= AdamW(
													    __UpperCamelCase       ,			lr=self.hparams.learning_rate       ,			eps=self.hparams.adam_epsilon						)
									snake_case__   :      List[str]   				= optimizer

									snake_case__   :      Optional[int]   				= self.get_lr_scheduler()

									return [optimizer], [scheduler]




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







									return self.validation_step(__UpperCamelCase       ,			__UpperCamelCase						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







									return self.validation_end(__UpperCamelCase						)




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      List[str]   				= max(1       ,			self.hparams.gpus						)  # TODO: consider num_tpu_cores
									snake_case__   :      Optional[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      __a     (  self       ,			__UpperCamelCase						)	->    int:



									'''simple docstring'''







									if mode == "test":
													snake_case__   :      Union[str, Any]   				= len(self.test_dataloader().dataset						)
									else:
													snake_case__   :      Union[str, Any]   				= self.get_dataloader('train'       ,			self.hparams.train_batch_size       ,			shuffle=__UpperCamelCase						)
													snake_case__   :      List[Any]   				= len(self.train_dataloader().dataset						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase = False						)	->    Union[str, Any]:



									'''simple docstring'''







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




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									return self.train_loader




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







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




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase						)	->    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      __a     (  self       ,			__UpperCamelCase						)	->    None:



									'''simple docstring'''







									snake_case__   :      Dict   				= self.output_dir.joinpath('best_tfmr'						)
									snake_case__   :      Any   				= self.step_count
									self.model.save_pretrained(__UpperCamelCase						)
									self.tokenizer.save_pretrained(__UpperCamelCase						)




					@staticmethod
					def      __a     (  __UpperCamelCase       ,			__UpperCamelCase						)	->    Any:



									'''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 					__snake_case    (     pl.Callback      ):




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      int   				= trainer.lr_schedulers[0]['scheduler']
									snake_case__   :      Dict   				= {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
									pl_module.logger.log_metrics(__UpperCamelCase						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







									rank_zero_info('***** Validation results *****'						)
									snake_case__   :      int   				= 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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[Any]:



									'''simple docstring'''







									rank_zero_info('***** Test results *****'						)
									snake_case__   :      Tuple   				= trainer.callback_metrics
									# Log and save results to file
									snake_case__   :      Optional[Any]   				= 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 					UpperCamelCase__		(							A__     ,					A__       )  ->   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 					UpperCamelCase__		(							A__     ,					A__     ,					A__=None     ,					A__=True     ,					A__=[]     ,					A__=None     ,					A__=None     ,					**A__     ,					)  ->   Dict:
				pl.seed_everything(args.seed       )

				# init model
				snake_case__   :      List[Any]   				= Path(model.hparams.output_dir       )
				odir.mkdir(exist_ok=A__       )

				# add custom checkpoints
				if checkpoint_callback is None:
								snake_case__   :      Tuple   				= 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:
								snake_case__   :      Union[str, Any]   				= LoggingCallback()

				snake_case__   :      Tuple   				= {}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								snake_case__   :      List[Any]   				= 16
								snake_case__   :      Dict   				= args.fpaa_opt_level

				if args.gpus > 1:
								snake_case__   :      Optional[int]   				= 'ddp'

				snake_case__   :      List[Any]   				= args.accumulate_grad_batches
				snake_case__   :      List[Any]   				= extra_train_kwargs.get('accelerator'     ,					A__       )
				snake_case__   :      str   				= extra_train_kwargs.get('profiler'     ,					A__       )

				snake_case__   :      str   				= 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 typing import TYPE_CHECKING

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


lowerCAmelCase__	:  int						=  {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Union[str, Any]						=  ['''DetaImageProcessor''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Optional[int]						=  [
				    '''DETA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DetaForObjectDetection''',
				    '''DetaModel''',
				    '''DetaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				lowerCAmelCase__	:  int						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


