






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

require_version('pytorch_lightning>=1.0.4')

_UpperCamelCase						:						Tuple  =					{
    '''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						:						Dict  =					{
    '''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						:						List[Any]  =					sorted(arg_to_scheduler.keys())
_UpperCamelCase						:						str  =					'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class  snake_case__      (					pl.LightningModule):




					def __init__(  self  :		Tuple				, _A  :		List[str]				, _A  :		Optional[int]=None				, _A  :		List[Any]="base"				, _A  :		List[Any]=None				, _A  :		List[str]=None				, _A  :		Any=None				, **_A  :		Dict				, )    ->							Optional[int]:
								super().__init__()
								# TODO: move to self.save_hyperparameters()
								# self.save_hyperparameters()
								# can also expand arguments into trainer signature for easier reading

								self.save_hyperparameters(snake_case__      )
								UpperCAmelCase_      :       Union[str, Any]          =       0
								UpperCAmelCase_      :       int          =       Path(self.hparams.output_dir      )
								UpperCAmelCase_      :       Dict          =       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=snake_case__				, **snake_case__				, )
								else:
											UpperCAmelCase_      :       PretrainedConfig          =       config

								UpperCAmelCase_      :       Union[str, Any]          =       ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
								for p in extra_model_params:
											if getattr(self.hparams				, snake_case__				, snake_case__      ):
														assert hasattr(self.config				, snake_case__      ), F"model config doesn\'t have a `{p}` attribute"
														setattr(self.config				, snake_case__				, getattr(self.hparams				, snake_case__      )      )

								if tokenizer is None:
											UpperCAmelCase_      :       str          =       AutoTokenizer.from_pretrained(
											    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				, cache_dir=snake_case__				, )
								else:
											UpperCAmelCase_      :       PreTrainedTokenizer          =       tokenizer
								UpperCAmelCase_      :       List[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=snake_case__				, )
								else:
											UpperCAmelCase_      :       Dict          =       model




					def 		A      (  self  :		Tuple				, *_A  :		Union[str, Any]				, **_A  :		Tuple      )    ->							int:
								UpperCAmelCase_      :       List[str]          =       self.model_type.from_pretrained(*snake_case__				, **snake_case__      )




					def 		A      (  self  :		Dict      )    ->							List[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       arg_to_scheduler[self.hparams.lr_scheduler]
								UpperCAmelCase_      :       List[str]          =       get_schedule_func(
								    self.opt				, num_warmup_steps=self.hparams.warmup_steps				, num_training_steps=self.total_steps()      )
								UpperCAmelCase_      :       Tuple          =       {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
								return scheduler




					def 		A      (  self  :		Optional[Any]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       self.model
								UpperCAmelCase_      :       Any          =       ['bias', 'LayerNorm.weight']
								UpperCAmelCase_      :       Optional[Any]          =       [
								    {
								        'params': [
								            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )
								        ],  # check this named paramters
								        '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(
											    snake_case__				, lr=self.hparams.learning_rate				, scale_parameter=snake_case__				, relative_step=snake_case__      )

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

								UpperCAmelCase_      :       Optional[Any]          =       self.get_lr_scheduler()

								return [optimizer], [scheduler]




					def 		A      (  self  :		str				, _A  :		Optional[Any]				, _A  :		Optional[int]      )    ->							Optional[int]:
								return self.validation_step(snake_case__				, snake_case__      )




					def 		A      (  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							Optional[Any]:
								return self.validation_end(snake_case__      )




					def 		A      (  self  :		Union[str, Any]      )    ->							int:
								UpperCAmelCase_      :       Optional[Any]          =       max(1				, self.hparams.gpus      )  # TODO: consider num_tpu_cores
								UpperCAmelCase_      :       Dict          =       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  :		Optional[Any]				, _A  :		Any      )    ->							List[Any]:
								if stage == "test":
											UpperCAmelCase_      :       Optional[Any]          =       len(self.test_dataloader().dataset      )
								else:
											UpperCAmelCase_      :       Any          =       self.get_dataloader('''train'''				, self.hparams.train_batch_size				, shuffle=snake_case__      )
											UpperCAmelCase_      :       Optional[Any]          =       len(self.train_dataloader().dataset      )




					def 		A      (  self  :		List[str]				, _A  :		Union[str, Any]				, _A  :		Dict				, _A  :		Union[str, Any] = False      )    ->							Union[str, Any]:
								raise NotImplementedError('''You must implement this for your task'''      )




					def 		A      (  self  :		int      )    ->							Optional[Any]:
								return self.train_loader




					def 		A      (  self  :		int      )    ->							Tuple:
								return self.get_dataloader('''dev'''				, self.hparams.eval_batch_size				, shuffle=snake_case__      )




					def 		A      (  self  :		Optional[Any]      )    ->							Optional[Any]:
								return self.get_dataloader('''test'''				, self.hparams.eval_batch_size				, shuffle=snake_case__      )




					def 		A      (  self  :		str				, _A  :		Dict      )    ->							Optional[Any]:
								return os.path.join(
								    self.hparams.data_dir				, '''cached_{}_{}_{}'''.format(
								        snake_case__				, list(filter(snake_case__				, self.hparams.model_name_or_path.split('''/'''      )      )      ).pop()				, str(self.hparams.max_seq_length      )				, )				, )




					@pl.utilities.rank_zero_only
					def 		A      (  self  :		Optional[int]				, _A  :		List[str]      )    ->							Dict:
								UpperCAmelCase_      :       int          =       self.output_dir.joinpath('''best_tfmr'''      )
								UpperCAmelCase_      :       Optional[Any]          =       self.step_count
								self.model.save_pretrained(snake_case__      )
								self.tokenizer.save_pretrained(snake_case__      )





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






class  snake_case__      (					pl.Callback):




					def 		A      (  self  :		int				, _A  :		Union[str, Any]				, _A  :		Optional[int]      )    ->							Tuple:
								if (
								    trainer.is_global_zero and trainer.global_rank == 0
								):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
											pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class  snake_case__      (					pl.Callback):




					def 		A      (  self  :		List[str]				, _A  :		Union[str, Any]				, _A  :		List[Any]      )    ->							Dict:
								# print(pl_module.model.rag)
								for name, param in pl_module.model.rag.named_parameters():
											if param.grad is None:
														print(snake_case__      )






class  snake_case__      (					pl.Callback):




					def 		A      (  self  :		Optional[Any]				, _A  :		Tuple				, _A  :		List[str]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Dict          =       trainer.lr_schedulers[0]['scheduler']
								UpperCAmelCase_      :       Union[str, Any]          =       {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
								pl_module.logger.log_metrics(snake_case__      )




					def 		A      (  self  :		List[str]				, _A  :		Tuple				, _A  :		Tuple      )    ->							List[Any]:
								rank_zero_info('''***** Validation results *****'''      )
								UpperCAmelCase_      :       Optional[Any]          =       trainer.callback_metrics
								# Log results
								for key in sorted(snake_case__      ):
											if key not in ["log", "progress_bar"]:
														rank_zero_info('''{} = {}\n'''.format(snake_case__				, str(metrics[key]      )      )      )





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






def __UpperCAmelCase   (					A					:			List[str]			,       A					:			List[str]		)    ->   None:
			parser.add_argument(
			    '''--output_dir'''			,       default=str(Path(lowerCamelCase_		).parent / '''test_run''' / '''model_checkpoints'''		)			,       type=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=str(Path(lowerCamelCase_		).parent / '''test_run''' / '''dummy-train-data'''		)			,       type=lowerCamelCase_			,       help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''			,       )







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

			# init model
			UpperCAmelCase_      :       List[str]          =       Path(model.hparams.output_dir		)
			odir.mkdir(exist_ok=lowerCamelCase_		)

			# add custom checkpoints
			if checkpoint_callback is None:
						UpperCAmelCase_      :       Dict          =       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:
						UpperCAmelCase_      :       str          =       LoggingCallback()

			UpperCAmelCase_      :       Union[str, Any]          =       {}

			if args.fpaa:
						UpperCAmelCase_      :       Optional[Any]          =       1_6

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

			UpperCAmelCase_      :       Dict          =       args.accumulate_grad_batches
			UpperCAmelCase_      :       Optional[Any]          =       None
			UpperCAmelCase_      :       Union[str, Any]          =       'auto'

			UpperCAmelCase_      :       Union[str, Any]          =       pl.Trainer.from_argparse_args(
			    lowerCamelCase_			,       weights_summary=lowerCamelCase_			,       callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]			,       logger=lowerCamelCase_			,       val_check_interval=1			,       num_sanity_val_steps=2			,       **lowerCamelCase_			,       )

			if args.do_train:
						trainer.fit(lowerCamelCase_		)

			else:
						print('''RAG modeling tests with new set functions successfuly executed!'''		)
			return trainer








import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


UpperCamelCase__ :   Dict						=							logging.get_logger(__name__)







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							warnings.warn(
							    'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use ChineseCLIPImageProcessor instead.'      ,snake_case__      ,)
							super().__init__(*snake_case__      ,**snake_case__ )



