"""simple docstring"""






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


UpperCamelCase     					=     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCamelCase     					=     {
    '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
UpperCamelCase     					=     {
    '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
}
UpperCamelCase     					=     sorted(arg_to_scheduler.keys())
UpperCamelCase     					=     '{' + ', '.join(arg_to_scheduler_choices) + '}'
class   UpperCAmelCase__				(    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       ,     )    ->	List[Any]:
							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 )
							a_							:			str     		=					0
							a_							:			str     		=					Path(self.hparams.output_dir )
							a_							:			Union[str, Any]     		=					self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
													a_							:			Optional[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=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE       ,     )
							else:
													a_							:			PretrainedConfig     		=					config

							a_							:			Optional[Any]     		=					("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:
													a_							:			Tuple     		=					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:
													a_							:			PreTrainedTokenizer     		=					tokenizer
							a_							:			int     		=					MODEL_MODES[mode]
							if model is None:
													a_							:			Dict     		=					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:
													a_							:			str     		=					model







	def       A    (			self       ,     *_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )    ->	Tuple:
							a_							:			Any     		=					self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )







	def       A    (			self )    ->	int:
							a_							:			str     		=					arg_to_scheduler[self.hparams.lr_scheduler]
							a_							:			Optional[Any]     		=					get_schedule_func(
							    self.opt       ,     num_warmup_steps=self.hparams.warmup_steps       ,     num_training_steps=self.total_steps() )
							a_							:			Tuple     		=					{"scheduler": scheduler, "interval": "step", "frequency": 1}
							return scheduler







	def       A    (			self )    ->	int:
							a_							:			Union[str, Any]     		=					self.model
							a_							:			Union[str, Any]     		=					["bias", "LayerNorm.weight"]
							a_							:			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:
													a_							:			Union[str, Any]     		=					Adafactor(
													    _SCREAMING_SNAKE_CASE       ,     lr=self.hparams.learning_rate       ,     scale_parameter=_SCREAMING_SNAKE_CASE       ,     relative_step=_SCREAMING_SNAKE_CASE )

							else:
													a_							:			Optional[int]     		=					AdamW(
													    _SCREAMING_SNAKE_CASE       ,     lr=self.hparams.learning_rate       ,     eps=self.hparams.adam_epsilon )
							a_							:			List[Any]     		=					optimizer

							a_							:			Union[str, Any]     		=					self.get_lr_scheduler()

							return [optimizer], [scheduler]







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	List[Any]:
							return self.validation_step(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	Optional[int]:
							return self.validation_end(_SCREAMING_SNAKE_CASE )







	def       A    (			self )    ->	int:
							a_							:			Optional[Any]     		=					max(1       ,     self.hparams.gpus )  # TODO: consider num_tpu_cores
							a_							:			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       ,     _SCREAMING_SNAKE_CASE )    ->	Any:
							if mode == "test":
													a_							:			int     		=					len(self.test_dataloader().dataset )
							else:
													a_							:			Any     		=					self.get_dataloader("train"       ,     self.hparams.train_batch_size       ,     shuffle=_SCREAMING_SNAKE_CASE )
													a_							:			str     		=					len(self.train_dataloader().dataset )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = False )    ->	List[str]:
							raise NotImplementedError("You must implement this for your task" )







	def       A    (			self )    ->	Tuple:
							return self.train_loader







	def       A    (			self )    ->	Any:
							return self.get_dataloader("dev"       ,     self.hparams.eval_batch_size       ,     shuffle=_SCREAMING_SNAKE_CASE )







	def       A    (			self )    ->	int:
							return self.get_dataloader("test"       ,     self.hparams.eval_batch_size       ,     shuffle=_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	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       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	None:
							a_							:			Tuple     		=					self.output_dir.joinpath("best_tfmr" )
							a_							:			List[str]     		=					self.step_count
							self.model.save_pretrained(_SCREAMING_SNAKE_CASE )
							self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )







	@staticmethod
	def       A    (			_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Union[str, Any]:
							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=3_2       ,     type=_SCREAMING_SNAKE_CASE )
							parser.add_argument("--eval_batch_size"       ,     default=3_2       ,     type=_SCREAMING_SNAKE_CASE )
							parser.add_argument("--adafactor"       ,     action="store_true" )




class   UpperCAmelCase__				(    pl.Callback			):


	"""simple docstring"""







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Optional[Any]:
							a_							:			List[Any]     		=					trainer.lr_schedulers[0]["scheduler"]
							a_							:			Optional[Any]     		=					{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
							pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	List[Any]:
							rank_zero_info("***** Validation results *****" )
							a_							:			Union[str, Any]     		=					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       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Any:
							rank_zero_info("***** Test results *****" )
							a_							:			int     		=					trainer.callback_metrics
							# Log and save results to file
							a_							:			Tuple     		=					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  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Dict		,			_SCREAMING_SNAKE_CASE	:Optional[int]  )				-> None:
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    "--output_dir"		,			default=_SCREAMING_SNAKE_CASE		,			type=_SCREAMING_SNAKE_CASE		,			required=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE  )
						parser.add_argument("--max_grad_norm"		,			dest="gradient_clip_val"		,			default=1.0		,			type=_SCREAMING_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=_SCREAMING_SNAKE_CASE		,			default=1		,			help="Number of updates steps to accumulate before performing a backward/update pass."		,			)
						parser.add_argument("--seed"		,			type=_SCREAMING_SNAKE_CASE		,			default=42		,			help="random seed for initialization"  )
						parser.add_argument(
						    "--data_dir"		,			default=_SCREAMING_SNAKE_CASE		,			type=_SCREAMING_SNAKE_CASE		,			required=_SCREAMING_SNAKE_CASE		,			help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."		,			)



def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:BaseTransformer		,			_SCREAMING_SNAKE_CASE	:argparse.Namespace		,			_SCREAMING_SNAKE_CASE	:List[str]=None		,			_SCREAMING_SNAKE_CASE	:Tuple=True		,			_SCREAMING_SNAKE_CASE	:int=[]		,			_SCREAMING_SNAKE_CASE	:int=None		,			_SCREAMING_SNAKE_CASE	:Tuple=None		,			**_SCREAMING_SNAKE_CASE	:str		,			)				-> Optional[int]:
						pl.seed_everything(args.seed  )

						# init model
						a_							:			Any     		=					Path(model.hparams.output_dir  )
						odir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE  )

						# add custom checkpoints
						if checkpoint_callback is None:
												a_							:			List[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(_SCREAMING_SNAKE_CASE  )
						if logging_callback is None:
												a_							:			Optional[Any]     		=					LoggingCallback()

						a_							:			Optional[Any]     		=					{}

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

						if args.gpus > 1:
												a_							:			str     		=					"ddp"

						a_							:			Optional[Any]     		=					args.accumulate_grad_batches
						a_							:			Tuple     		=					extra_train_kwargs.get("accelerator"		,			_SCREAMING_SNAKE_CASE  )
						a_							:			Optional[int]     		=					extra_train_kwargs.get("profiler"		,			_SCREAMING_SNAKE_CASE  )

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

						if args.do_train:
												trainer.fit(_SCREAMING_SNAKE_CASE  )

						return trainer





"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCamelCase     					=     {'configuration_levit': ['LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LevitConfig', 'LevitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['LevitFeatureExtractor']
				UpperCamelCase     					=     ['LevitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LevitForImageClassification',
				    'LevitForImageClassificationWithTeacher',
				    'LevitModel',
				    'LevitPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_levit import LevitFeatureExtractor
								from .image_processing_levit import LevitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_levit import (
								    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LevitForImageClassification,
								    LevitForImageClassificationWithTeacher,
								    LevitModel,
								    LevitPreTrainedModel,
								)
else:
				import sys

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




