"""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  :  Tuple=				logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_a  :  Dict=				{
    "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  :  int=				{
    "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  :  Any=				sorted(arg_to_scheduler.keys())
_a  :  List[Any]=				"{" + ", ".join(arg_to_scheduler_choices) + "}"

class 	UpperCamelCase    (					pl.LightningModule		):



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

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



				def       _lowercase  (self       :   Union[str, Any]			,						*_A       :   Union[str, Any]			,						**_A       :   Any)					->			Dict:
					__snake_case    :							str   	=				self.model_type.from_pretrained(*_A			,						**_A)



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



				def       _lowercase  (self       :   Union[str, Any])					->			int:
					__snake_case    :							Any   	=				self.model
					__snake_case    :							Any   	=				['bias', 'LayerNorm.weight']
					__snake_case    :							int   	=				[
					    {
					        '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:
						__snake_case    :							Optional[Any]   	=				Adafactor(
						    _A			,						lr=self.hparams.learning_rate			,						scale_parameter=_A			,						relative_step=_A)

					else:
						__snake_case    :							Any   	=				AdamW(
						    _A			,						lr=self.hparams.learning_rate			,						eps=self.hparams.adam_epsilon)
					__snake_case    :							str   	=				optimizer

					__snake_case    :							Any   	=				self.get_lr_scheduler()

					return [optimizer], [scheduler]



				def       _lowercase  (self       :   Dict			,						_A       :   Dict			,						_A       :   int)					->			Union[str, Any]:
					return self.validation_step(_A			,						_A)



				def       _lowercase  (self       :   str			,						_A       :   Any)					->			Optional[int]:
					return self.validation_end(_A)



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



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



				def       _lowercase  (self       :   str)					->			Any:
					return self.train_loader



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



				def       _lowercase  (self       :   List[Any])					->			Tuple:
					return self.get_dataloader('test'			,						self.hparams.eval_batch_size			,						shuffle=_A)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   str)					->			List[str]:
					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       _lowercase  (self       :   List[Any]			,						_A       :   Dict[str, Any])					->			None:
					__snake_case    :							Any   	=				self.output_dir.joinpath('best_tfmr')
					__snake_case    :							str   	=				self.step_count
					self.model.save_pretrained(_A)
					self.tokenizer.save_pretrained(_A)






				@staticmethod
				def       _lowercase  (_A       :   Optional[int]			,						_A       :   Any)					->			Dict:
					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=str(Path(_A).parent / 'test_run' / 'cache')			,						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 	UpperCamelCase    (					pl.Callback		):



				def       _lowercase  (self       :   int			,						_A       :   Union[str, Any]			,						_A       :   Optional[int])					->			List[str]:
					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 	UpperCamelCase    (					pl.Callback		):



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







class 	UpperCamelCase    (					pl.Callback		):



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   Optional[Any]			,						_A       :   str)					->			List[Any]:
					__snake_case    :							List[str]   	=				trainer.lr_schedulers[0]['scheduler']
					__snake_case    :							Optional[int]   	=				{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
					pl_module.logger.log_metrics(_A)



				def       _lowercase  (self       :   str			,						_A       :   pl.Trainer			,						_A       :   pl.LightningModule)					->			List[str]:
					rank_zero_info('***** Validation results *****')
					__snake_case    :							Union[str, 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       _lowercase  (self       :   Union[str, Any]			,						_A       :   pl.Trainer			,						_A       :   pl.LightningModule)					->			Union[str, Any]:
					rank_zero_info('***** Test results *****')
					__snake_case    :							Dict   	=				trainer.callback_metrics
					# Log and save results to file
					__snake_case    :							Optional[int]   	=				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 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       List[str]						)							->    None:

	'''simple docstring'''





	parser.add_argument(
	    '--output_dir'    ,							default=str(Path(UpperCAmelCase_						).parent / 'test_run' / 'model_checkpoints'						)    ,							type=UpperCAmelCase_    ,							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=UpperCAmelCase_    ,							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=UpperCAmelCase_						)
	parser.add_argument('--max_grad_norm'    ,							dest='gradient_clip_val'    ,							default=1.0    ,							type=UpperCAmelCase_    ,							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=UpperCAmelCase_    ,							default=1    ,							help='Number of updates steps to accumulate before performing a backward/update pass.'    ,							)
	parser.add_argument('--seed'    ,							type=UpperCAmelCase_    ,							default=42    ,							help='random seed for initialization'						)
	parser.add_argument(
	    '--data_dir'    ,							default=str(Path(UpperCAmelCase_						).parent / 'test_run' / 'dummy-train-data'						)    ,							type=UpperCAmelCase_    ,							help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'    ,							)

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       BaseTransformer    ,							UpperCAmelCase_					:       argparse.Namespace    ,							UpperCAmelCase_					:       int=None    ,							UpperCAmelCase_					:       Any=True    ,							UpperCAmelCase_					:       Dict=[]    ,							UpperCAmelCase_					:       List[Any]=None    ,							UpperCAmelCase_					:       Any=None    ,							**UpperCAmelCase_					:       Optional[int]    ,							)							->    Tuple:

	'''simple docstring'''





	pl.seed_everything(args.seed						)

	# init model
	__snake_case    :							str   	=				Path(model.hparams.output_dir						)
	odir.mkdir(exist_ok=UpperCAmelCase_						)

	# add custom checkpoints
	if checkpoint_callback is None:
		__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(UpperCAmelCase_						)
	if logging_callback is None:
		__snake_case    :							Optional[Any]   	=				LoggingCallback()

	__snake_case    :							Optional[int]   	=				{}

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

	if args.gpus > 1:
		__snake_case    :							str   	=				'auto'
		__snake_case    :							Any   	=				'ddp'

	__snake_case    :							Dict   	=				args.accumulate_grad_batches
	__snake_case    :							Dict   	=				None
	__snake_case    :							str   	=				'auto'

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

	if args.do_train:
		trainer.fit(UpperCAmelCase_						)

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






"""simple docstring"""







import math

import tensorflow as tf
from packaging import version
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    Any:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							List[str]   	=				0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)    ,							x.dtype						)						))

	return x * cdf
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    Optional[Any]:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							Any   	=				tf.cast(math.pi    ,							x.dtype						)
	__snake_case    :							List[Any]   	=				tf.cast(0.044_715    ,							x.dtype						)
	__snake_case    :							Union[str, Any]   	=				0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(UpperCAmelCase_    ,							3						))						))

	return x * cdf
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				tf.convert_to_tensor(UpperCAmelCase_						)

	return x * tf.tanh(tf.math.softplus(UpperCAmelCase_						)						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    List[str]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							Dict   	=				tf.cast(0.044_715    ,							x.dtype						)
	__snake_case    :							int   	=				tf.cast(0.7_978_845_608    ,							x.dtype						)

	return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Any   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							Dict   	=				tf.cast(1.702    ,							x.dtype						)
	return x * tf.math.sigmoid(coeff * x						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    List[Any]:

	'''simple docstring'''





	return tf.clip_by_value(_gelu(UpperCAmelCase_						)    ,							-10    ,							10						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       Any=-1						)							->    int:

	'''simple docstring'''





	__snake_case	,       __snake_case    :							str   	=				tf.split(UpperCAmelCase_    ,							2    ,							axis=UpperCAmelCase_						)
	return a * tf.math.sigmoid(UpperCAmelCase_						)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):
	def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    int:

		'''simple docstring'''





		return tf.keras.activations.gelu(UpperCAmelCase_    ,							approximate=UpperCAmelCase_						)

	_a  :  Tuple=				tf.keras.activations.gelu

	_a  :  Tuple=				approximate_gelu_wrap
else:
	_a  :  Any=				_gelu
	_a  :  str=				_gelu_new


_a  :  List[str]=				{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]						)							->    List[Any]:

	'''simple docstring'''





	if activation_string in ACTaFN:
		return ACTaFN[activation_string]
	else:
		raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"						)





