

"""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


_A						=			logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_A						=			{
    '''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
_A						=			{
    '''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
}
_A						=			sorted(arg_to_scheduler.keys())
_A						=			'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''






class 	_lowercase       (    pl.LightningModule		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_="base"		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_		,			)		->							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(_SCREAMING_SNAKE_CASE )
			lowerCamelCase					:      List[Any]          =   0
			lowerCamelCase					:      Any          =   Path(self.hparams.output_dir )
			lowerCamelCase					:      Union[str, Any]          =   self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
					lowerCamelCase					:      Tuple          =   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:
					lowerCamelCase					:      PretrainedConfig          =   config

			lowerCamelCase					:      int          =   ("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:
					lowerCamelCase					:      List[Any]          =   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:
					lowerCamelCase					:      PreTrainedTokenizer          =   tokenizer
			lowerCamelCase					:      Tuple          =   MODEL_MODES[mode]
			if model is None:
					lowerCamelCase					:      Union[str, Any]          =   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:
					lowerCamelCase					:      Optional[int]          =   model




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[Any]:
			lowerCamelCase					:      Any          =   self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE		,			**_SCREAMING_SNAKE_CASE )




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




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

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

			lowerCamelCase					:      List[str]          =   self.get_lr_scheduler()

			return [optimizer], [scheduler]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Optional[Any]:
			return self.validation_step(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Tuple:
			return self.validation_end(_SCREAMING_SNAKE_CASE )




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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = False )		->							int:
			raise NotImplementedError('You must implement this for your task' )




	def       _UpperCamelCase   (			self )		->							List[str]:
			return self.train_loader




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			return self.get_dataloader('dev'		,			self.hparams.eval_batch_size		,			shuffle=_SCREAMING_SNAKE_CASE )




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			return self.get_dataloader('test'		,			self.hparams.eval_batch_size		,			shuffle=_SCREAMING_SNAKE_CASE )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Any:
			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       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							None:
			lowerCamelCase					:      int          =   self.output_dir.joinpath('best_tfmr' )
			lowerCamelCase					:      Union[str, Any]          =   self.step_count
			self.model.save_pretrained(_SCREAMING_SNAKE_CASE )
			self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )




	@staticmethod
	def       _UpperCamelCase   (			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[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=str(Path(_SCREAMING_SNAKE_CASE ).parent / 'test_run' / 'cache' )		,			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=32		,			type=_SCREAMING_SNAKE_CASE )
			parser.add_argument('--eval_batch_size'		,			default=32		,			type=_SCREAMING_SNAKE_CASE )
			parser.add_argument('--adafactor'		,			action='store_true' )






class 	_lowercase       (    pl.Callback		):




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Optional[Any]:
			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 	_lowercase       (    pl.Callback		):




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			# print(pl_module.model.rag)
			for name, param in pl_module.model.rag.named_parameters():
					if param.grad is None:
							print(_SCREAMING_SNAKE_CASE )






class 	_lowercase       (    pl.Callback		):




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Dict          =   trainer.lr_schedulers[0]["scheduler"]
			lowerCamelCase					:      Any          =   {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
			pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[str]:
			rank_zero_info('***** Validation results *****' )
			lowerCamelCase					:      int          =   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       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			rank_zero_info('***** Test results *****' )
			lowerCamelCase					:      Optional[int]          =   trainer.callback_metrics
			# Log and save results to file
			lowerCamelCase					:      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 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		parser.add_argument(
		    '--output_dir',						default=str(Path(_a      ).parent / 'test_run' / 'model_checkpoints'      ),						type=_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=str(Path(_a      ).parent / 'test_run' / 'dummy-train-data'      ),						type=_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_,						):





		'''simple docstring'''


		pl.seed_everything(args.seed      )

		# init model
		lowerCamelCase					:      str          =   Path(model.hparams.output_dir      )
		odir.mkdir(exist_ok=_a      )

		# add custom checkpoints
		if checkpoint_callback is None:
				lowerCamelCase					:      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:
				lowerCamelCase					:      List[Any]          =   LoggingCallback()

		lowerCamelCase					:      Union[str, Any]          =   {}

		if args.fpaa:
				lowerCamelCase					:      Optional[int]          =   16

		if args.gpus > 1:
				lowerCamelCase					:      List[Any]          =   "auto"
				lowerCamelCase					:      Dict          =   "ddp"

		lowerCamelCase					:      List[Any]          =   args.accumulate_grad_batches
		lowerCamelCase					:      Dict          =   None
		lowerCamelCase					:      List[str]          =   "auto"

		lowerCamelCase					:      List[str]          =   pl.Trainer.from_argparse_args(
		    _a,						weights_summary=_a,						callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback],						logger=_a,						val_check_interval=1,						num_sanity_val_steps=2,						**_a,						)

		if args.do_train:
				trainer.fit(_a      )

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








"""simple docstring"""

from ....configuration_utils import PretrainedConfig
from ....utils import logging


_A						=			logging.get_logger(__name__)

_A						=			{
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'trajectory_transformer'
	lowercase_				=     ['past_key_values']
	lowercase_				=     {
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}




	def __init__(			self		,			UpperCAmelCase_=100		,			UpperCAmelCase_=5		,			UpperCAmelCase_=1		,			UpperCAmelCase_=1		,			UpperCAmelCase_=249		,			UpperCAmelCase_=6		,			UpperCAmelCase_=17		,			UpperCAmelCase_=25		,			UpperCAmelCase_=4		,			UpperCAmelCase_=4		,			UpperCAmelCase_=128		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0006		,			UpperCAmelCase_=512		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=1		,			UpperCAmelCase_=True		,			UpperCAmelCase_=1		,			UpperCAmelCase_=50256		,			UpperCAmelCase_=50256		,			**UpperCAmelCase_		,			)		->							List[Any]:
			lowerCamelCase					:      int          =   vocab_size
			lowerCamelCase					:      List[str]          =   action_weight
			lowerCamelCase					:      List[Any]          =   reward_weight
			lowerCamelCase					:      List[str]          =   value_weight
			lowerCamelCase					:      Tuple          =   max_position_embeddings
			lowerCamelCase					:      List[str]          =   block_size
			lowerCamelCase					:      Any          =   action_dim
			lowerCamelCase					:      List[Any]          =   observation_dim
			lowerCamelCase					:      Any          =   transition_dim
			lowerCamelCase					:      int          =   learning_rate
			lowerCamelCase					:      Union[str, Any]          =   n_layer
			lowerCamelCase					:      Tuple          =   n_head
			lowerCamelCase					:      Any          =   n_embd
			lowerCamelCase					:      Union[str, Any]          =   embd_pdrop
			lowerCamelCase					:      Optional[int]          =   attn_pdrop
			lowerCamelCase					:      int          =   resid_pdrop
			lowerCamelCase					:      Optional[int]          =   initializer_range
			lowerCamelCase					:      List[str]          =   layer_norm_eps
			lowerCamelCase					:      Any          =   kaiming_initializer_range
			lowerCamelCase					:      str          =   use_cache
			super().__init__(pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			**UpperCAmelCase_ )






