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 :     Union[str, Any]          =  logging.getLogger(__name__)

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

__lowerCamelCase :     str          =  {
    '''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 :     str          =  {
    '''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 :     Optional[int]          =  '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''






class 		a__						( pl.LightningModule ):

					def __init__(   self				:	List[Any],_A				:	argparse.Namespace,_A				:	List[str]=None,_A				:	Any="base",_A				:	List[Any]=None,_A				:	Union[str, Any]=None,_A				:	Any=None,**_A				:	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(_A   )
							SCREAMING_SNAKE_CASE_  :		int    				=   0
							SCREAMING_SNAKE_CASE_  :		int    				=   Path(self.hparams.output_dir   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
									SCREAMING_SNAKE_CASE_  :		List[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:
									SCREAMING_SNAKE_CASE_  :		PretrainedConfig    				=   config

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

					def     __UpperCamelCase       (   self				:	int,*_A				:	List[Any],**_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.model_type.from_pretrained(*_A,**_A   )

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   arg_to_scheduler[self.hparams.lr_scheduler]
							SCREAMING_SNAKE_CASE_  :		Any    				=   get_schedule_func(
							    self.opt,num_warmup_steps=self.hparams.warmup_steps,num_training_steps=self.total_steps()   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   {"scheduler": scheduler, "interval": "step", "frequency": 1}
							return scheduler

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






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

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

							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.get_lr_scheduler()

							return [optimizer], [scheduler]

					def     __UpperCamelCase       (   self				:	Any,_A				:	Union[str, Any],_A				:	Any   ):







							"""simple docstring"""






							return self.validation_step(_A,_A   )

					def     __UpperCamelCase       (   self				:	Any,_A				:	Union[str, Any]   ):







							"""simple docstring"""






							return self.validation_end(_A   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	Optional[int]   ):







							"""simple docstring"""






							if mode == "test":
									SCREAMING_SNAKE_CASE_  :		str    				=   len(self.test_dataloader().dataset   )
							else:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.get_dataloader("train",self.hparams.train_batch_size,shuffle=_A   )
									SCREAMING_SNAKE_CASE_  :		Dict    				=   len(self.train_dataloader().dataset   )

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	str,_A				:	int,_A				:	bool = False   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							return self.train_loader

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Union[str, Any],_A				:	str   ):







							"""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     __UpperCamelCase       (   self				:	List[Any],_A				:	Dict[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.output_dir.joinpath("best_tfmr"   )
							SCREAMING_SNAKE_CASE_  :		str    				=   self.step_count
							self.model.save_pretrained(_A   )
							self.tokenizer.save_pretrained(_A   )






					@staticmethod
					def     __UpperCamelCase       (   _A				:	Any,_A				:	List[Any]   ):







							"""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 ):

					def     __UpperCamelCase       (   self				:	int,_A				:	List[str],_A				:	List[Any]   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	pl.Trainer,_A				:	pl.LightningModule   ):







							"""simple docstring"""






							rank_zero_info("***** Validation results *****"   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   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     __UpperCamelCase       (   self				:	Tuple,_A				:	pl.Trainer,_A				:	pl.LightningModule   ):







							"""simple docstring"""






							rank_zero_info("***** Test results *****"   )
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   trainer.callback_metrics
							# Log and save results to file
							SCREAMING_SNAKE_CASE_  :		Union[str, 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 				_snake_case     (       lowerCAmelCase							:       Tuple       ,			lowerCAmelCase							:       List[Any]     ):
		"""simple docstring"""







		parser.add_argument(
		    "--output_dir"       ,			default=lowerCAmelCase       ,			type=lowerCAmelCase       ,			required=lowerCAmelCase       ,			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=lowerCAmelCase       ,			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=lowerCAmelCase     )
		parser.add_argument("--max_grad_norm"       ,			dest="gradient_clip_val"       ,			default=1.0       ,			type=lowerCAmelCase       ,			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=lowerCAmelCase       ,			default=1       ,			help="Number of updates steps to accumulate before performing a backward/update pass."       ,			)
		parser.add_argument("--seed"       ,			type=lowerCAmelCase       ,			default=4_2       ,			help="random seed for initialization"     )
		parser.add_argument(
		    "--data_dir"       ,			default=lowerCAmelCase       ,			type=lowerCAmelCase       ,			required=lowerCAmelCase       ,			help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."       ,			)







def 				_snake_case     (       lowerCAmelCase							:       BaseTransformer       ,			lowerCAmelCase							:       argparse.Namespace       ,			lowerCAmelCase							:       Tuple=None       ,			lowerCAmelCase							:       Optional[Any]=True       ,			lowerCAmelCase							:       Tuple=[]       ,			lowerCAmelCase							:       str=None       ,			lowerCAmelCase							:       Union[str, Any]=None       ,			**lowerCAmelCase							:       int       ,			):
		"""simple docstring"""







		pl.seed_everything(args.seed     )

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

		# add custom checkpoints
		if checkpoint_callback is None:
				SCREAMING_SNAKE_CASE_  :		str    				=   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(lowerCAmelCase     )
		if logging_callback is None:
				SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   LoggingCallback()

		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   {}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   1_6
				SCREAMING_SNAKE_CASE_  :		Tuple    				=   args.fpaa_opt_level

		if args.gpus > 1:
				SCREAMING_SNAKE_CASE_  :		List[Any]    				=   "ddp"

		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   args.accumulate_grad_batches
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   extra_train_kwargs.get("accelerator"       ,			lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   extra_train_kwargs.get("profiler"       ,			lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   pl.Trainer.from_argparse_args(
		    lowerCAmelCase       ,			weights_summary=lowerCAmelCase       ,			callbacks=[logging_callback] + extra_callbacks       ,			logger=lowerCAmelCase       ,			checkpoint_callback=lowerCAmelCase       ,			**lowerCAmelCase       ,			)

		if args.do_train:
				trainer.fit(lowerCAmelCase     )

		return trainer

import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


__lowerCamelCase :     Tuple          =  logging.get_logger(__name__)






class 		a__						( A__ ):

					def __init__(   self				:	List[Any],*_A				:	Union[str, Any],**_A				:	List[Any]   ):







							"""simple docstring"""






							warnings.warn(
							    "The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use ViTImageProcessor instead.",_A,)
							super().__init__(*_A,**_A   )
