'''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 _SCREAMING_SNAKE_CASE(							pl.LightningModule					):




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

													SCREAMING_SNAKE_CASE__     :List[str]						    = ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
													for p in extra_model_params:
																				if getattr(self.hparams							,							UpperCamelCase_							,							UpperCamelCase_      ):
																											assert hasattr(self.config							,							UpperCamelCase_      ), f'''model config doesn\'t have a `{p}` attribute'''
																											setattr(self.config							,							UpperCamelCase_							,							getattr(self.hparams							,							UpperCamelCase_      )      )

													if tokenizer is None:
																				SCREAMING_SNAKE_CASE__     :Any						    = AutoTokenizer.from_pretrained(
																				    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,							cache_dir=UpperCamelCase_							,							)
													else:
																				SCREAMING_SNAKE_CASE__     :PreTrainedTokenizer						    = tokenizer
													SCREAMING_SNAKE_CASE__     :Tuple						    = MODEL_MODES[mode]
													if model is None:
																				SCREAMING_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=UpperCamelCase_							,							)
													else:
																				SCREAMING_SNAKE_CASE__     :str						    = model




						def      __lowerCamelCase       (    self :       str							,							*UpperCamelCase_ :       List[Any]							,							**UpperCamelCase_ :       Tuple      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.model_type.from_pretrained(*UpperCamelCase_							,							**UpperCamelCase_      )




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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.model
													SCREAMING_SNAKE_CASE__     :str						    = ['bias', 'LayerNorm.weight']
													SCREAMING_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:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = Adafactor(
																				    UpperCamelCase_							,							lr=self.hparams.learning_rate							,							scale_parameter=UpperCamelCase_							,							relative_step=UpperCamelCase_      )

													else:
																				SCREAMING_SNAKE_CASE__     :str						    = AdamW(
																				    UpperCamelCase_							,							lr=self.hparams.learning_rate							,							eps=self.hparams.adam_epsilon      )
													SCREAMING_SNAKE_CASE__     :Dict						    = optimizer

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

													return [optimizer], [scheduler]




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Union[str, Any]      )     ->				Any:
													return self.validation_step(UpperCamelCase_							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       List[str]      )     ->				Optional[Any]:
													return self.validation_end(UpperCamelCase_      )




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




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       bool = False      )     ->				Dict:
													raise NotImplementedError('You must implement this for your task'      )




						def      __lowerCamelCase       (    self :       str      )     ->				Dict:
													return self.train_loader




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




						def      __lowerCamelCase       (    self :       Dict      )     ->				List[str]:
													return self.get_dataloader('test'							,							self.hparams.eval_batch_size							,							shuffle=UpperCamelCase_      )




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




						@pl.utilities.rank_zero_only
						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       Dict[str, Any]      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.output_dir.joinpath('best_tfmr'      )
													SCREAMING_SNAKE_CASE__     :int						    = self.step_count
													self.model.save_pretrained(UpperCamelCase_      )
													self.tokenizer.save_pretrained(UpperCamelCase_      )




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




class _SCREAMING_SNAKE_CASE(							pl.Callback					):




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Optional[int]      )     ->				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 _SCREAMING_SNAKE_CASE(							pl.Callback					):




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




class _SCREAMING_SNAKE_CASE(							pl.Callback					):




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




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       pl.Trainer							,							UpperCamelCase_ :       pl.LightningModule      )     ->				Optional[int]:
													rank_zero_info('***** Validation results *****'      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = trainer.callback_metrics
													# Log results
													for key in sorted(UpperCamelCase_      ):
																				if key not in ["log", "progress_bar"]:
																											rank_zero_info('{} = {}\n'.format(UpperCamelCase_							,							str(metrics[key]      )      )      )




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


def    lowerCamelCase       (			UpperCAmelCase__				:  List[str]				,						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=4_2				,						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    lowerCamelCase       (			UpperCAmelCase__				:  BaseTransformer				,						UpperCAmelCase__				:  argparse.Namespace				,						UpperCAmelCase__				:  Tuple=None				,						UpperCAmelCase__				:  Union[str, Any]=True				,						UpperCAmelCase__				:  Optional[Any]=[]				,						UpperCAmelCase__				:  Optional[Any]=None				,						UpperCAmelCase__				:  Tuple=None				,						**UpperCAmelCase__				:  Optional[int]				,						)			->	str:


							'''simple docstring'''






							pl.seed_everything(args.seed		)

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

							# add custom checkpoints
							if checkpoint_callback is None:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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:
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = LoggingCallback()

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {}

							if args.fpaa:
														SCREAMING_SNAKE_CASE__     :Dict						    = 1_6

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

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

							SCREAMING_SNAKE_CASE__     :Any						    = 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'''







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


UpperCamelCase_      	=	logging.get_logger(__name__)

UpperCamelCase_      	=	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= 'gpt_neox'




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       int=5_04_32							,							UpperCamelCase_ :       Dict=61_44							,							UpperCamelCase_ :       int=44							,							UpperCamelCase_ :       Any=64							,							UpperCamelCase_ :       Optional[int]=2_45_76							,							UpperCamelCase_ :       str="gelu"							,							UpperCamelCase_ :       Tuple=0.25							,							UpperCamelCase_ :       Tuple=1_00_00							,							UpperCamelCase_ :       Tuple=0.0							,							UpperCamelCase_ :       Optional[int]=0.0							,							UpperCamelCase_ :       str=0.1							,							UpperCamelCase_ :       List[Any]=20_48							,							UpperCamelCase_ :       List[str]=0.02							,							UpperCamelCase_ :       Optional[int]=1e-5							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       Optional[Any]=0							,							UpperCamelCase_ :       str=2							,							UpperCamelCase_ :       str=False							,							UpperCamelCase_ :       Union[str, Any]=True							,							UpperCamelCase_ :       Tuple=None							,							**UpperCamelCase_ :       Any							,							)     ->				int:
													super().__init__(bos_token_id=UpperCamelCase_							,							eos_token_id=UpperCamelCase_							,							**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = vocab_size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_position_embeddings
													SCREAMING_SNAKE_CASE__     :List[Any]						    = hidden_size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :Dict						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :Dict						    = hidden_act
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rotary_pct
													SCREAMING_SNAKE_CASE__     :Any						    = rotary_emb_base
													SCREAMING_SNAKE_CASE__     :Dict						    = attention_dropout
													SCREAMING_SNAKE_CASE__     :Tuple						    = hidden_dropout
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = classifier_dropout
													SCREAMING_SNAKE_CASE__     :Dict						    = initializer_range
													SCREAMING_SNAKE_CASE__     :List[str]						    = layer_norm_eps
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = use_cache
													SCREAMING_SNAKE_CASE__     :Any						    = tie_word_embeddings
													SCREAMING_SNAKE_CASE__     :List[str]						    = use_parallel_residual
													SCREAMING_SNAKE_CASE__     :int						    = rope_scaling
													self._rope_scaling_validation()

													if self.hidden_size % self.num_attention_heads != 0:
																				raise ValueError(
																				    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Union[str, Any]:
													if self.rope_scaling is None:
																				return

													if not isinstance(self.rope_scaling							,							UpperCamelCase_      ) or len(self.rope_scaling      ) != 2:
																				raise ValueError(
																				    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
																				    f'''got {self.rope_scaling}'''      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.rope_scaling.get('type'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.rope_scaling.get('factor'							,							UpperCamelCase_      )
													if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																				raise ValueError(
																				    f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''      )
													if rope_scaling_factor is None or not isinstance(UpperCamelCase_							,							UpperCamelCase_      ) or rope_scaling_factor <= 1.0:
																				raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''      )






