



'''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


snake_case_			=     logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

snake_case_			=     {
    """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
snake_case_			=     {
    """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
}
snake_case_			=     sorted(arg_to_scheduler.keys())
snake_case_			=     """{""" + """, """.join(arg_to_scheduler_choices) + """}"""


class 	a__     (				pl.LightningModule	):


	def __init__(self : Dict,					__UpperCAmelCase : argparse.Namespace,					__UpperCAmelCase : Union[str, Any]=None,					__UpperCAmelCase : Optional[int]="base",					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Optional[int]=None,					**__UpperCAmelCase : Union[str, Any],					)     ->			List[str]:

					"""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(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= 0
					SCREAMING_SNAKE_CASE  :						Tuple      					= Path(self.hparams.output_dir      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.hparams.cache_dir if self.hparams.cache_dir else None
					if config is None:
									SCREAMING_SNAKE_CASE  :						int      					= 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  :						Optional[int]      					= ('''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  :						Tuple      					= 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  :						str      					= MODEL_MODES[mode]
					if model is None:
									SCREAMING_SNAKE_CASE  :						List[str]      					= 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  :						Tuple      					= model


	def 				lowercase__  (self : Optional[int],					*__UpperCAmelCase : Optional[Any],					**__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.model_type.from_pretrained(*__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int]      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= arg_to_scheduler[self.hparams.lr_scheduler]
					SCREAMING_SNAKE_CASE  :						Tuple      					= 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 				lowercase__  (self : Any      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.model
					SCREAMING_SNAKE_CASE  :						Dict      					= ['''bias''', '''LayerNorm.weight''']
					SCREAMING_SNAKE_CASE  :						Dict      					= [
					    {
					        '''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  :						Tuple      					= Adafactor(
									    __UpperCAmelCase,					lr=self.hparams.learning_rate,					scale_parameter=__UpperCAmelCase,					relative_step=__UpperCAmelCase      )

					else:
									SCREAMING_SNAKE_CASE  :						int      					= AdamW(
									    __UpperCAmelCase,					lr=self.hparams.learning_rate,					eps=self.hparams.adam_epsilon      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= optimizer

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_lr_scheduler()

					return [optimizer], [scheduler]


	def 				lowercase__  (self : int,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[Any]      )     ->			List[Any]:

					"""simple docstring"""

					return self.validation_step(__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					return self.validation_end(__UpperCAmelCase      )


	def 				lowercase__  (self : Union[str, Any]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= max(1,					self.hparams.gpus      )  # TODO: consider num_tpu_cores
					SCREAMING_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[Any],					__UpperCAmelCase : Union[str, Any]      )     ->			List[str]:

					"""simple docstring"""

					if stage == "test":
									SCREAMING_SNAKE_CASE  :						Tuple      					= len(self.test_dataloader().dataset      )
					else:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_dataloader('''train''',					self.hparams.train_batch_size,					shuffle=__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= len(self.train_dataloader().dataset      )


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : int,					__UpperCAmelCase : bool = False      )     ->			Dict:

					"""simple docstring"""

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


	def 				lowercase__  (self : Tuple      )     ->			Tuple:

					"""simple docstring"""

					return self.train_loader


	def 				lowercase__  (self : Optional[Any]      )     ->			List[Any]:

					"""simple docstring"""

					return self.get_dataloader('''dev''',					self.hparams.eval_batch_size,					shuffle=__UpperCAmelCase      )


	def 				lowercase__  (self : str      )     ->			Union[str, Any]:

					"""simple docstring"""

					return self.get_dataloader('''test''',					self.hparams.eval_batch_size,					shuffle=__UpperCAmelCase      )


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					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 				lowercase__  (self : str,					__UpperCAmelCase : Dict[str, Any]      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.output_dir.joinpath('''best_tfmr'''      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.step_count
					self.model.save_pretrained(__UpperCAmelCase      )
					self.tokenizer.save_pretrained(__UpperCAmelCase      )






	@staticmethod
	def 				lowercase__  (__UpperCAmelCase : List[str],					__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""simple docstring"""

					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 	a__     (				pl.Callback	):


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple      )     ->			List[str]:

					"""simple docstring"""

					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 	a__     (				pl.Callback	):


	def 				lowercase__  (self : int,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					for name, param in pl_module.model.rag.named_parameters():
									if param.grad is None:
													print(__UpperCAmelCase      )



class 	a__     (				pl.Callback	):


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Dict      )     ->			Optional[int]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Any,					__UpperCAmelCase : pl.Trainer,					__UpperCAmelCase : pl.LightningModule      )     ->			Union[str, Any]:

					"""simple docstring"""

					rank_zero_info('''***** Validation results *****'''      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 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 				lowercase__  (self : int,					__UpperCAmelCase : pl.Trainer,					__UpperCAmelCase : pl.LightningModule      )     ->			Optional[Any]:

					"""simple docstring"""

					rank_zero_info('''***** Test results *****'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= trainer.callback_metrics
					# Log and save results to file
					SCREAMING_SNAKE_CASE  :						Any      					= 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       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    '''--output_dir'''    ,	default=str(Path(_SCREAMING_SNAKE_CASE       ).parent / '''test_run''' / '''model_checkpoints'''       )    ,	type=_SCREAMING_SNAKE_CASE    ,	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=_SCREAMING_SNAKE_CASE    ,	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=_SCREAMING_SNAKE_CASE       )
				parser.add_argument('''--max_grad_norm'''    ,	dest='''gradient_clip_val'''    ,	default=1.0    ,	type=_SCREAMING_SNAKE_CASE    ,	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=_SCREAMING_SNAKE_CASE    ,	default=1    ,	help='''Number of updates steps to accumulate before performing a backward/update pass.'''    ,	)
				parser.add_argument('''--seed'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default=42    ,	help='''random seed for initialization'''       )
				parser.add_argument(
				    '''--data_dir'''    ,	default=str(Path(_SCREAMING_SNAKE_CASE       ).parent / '''test_run''' / '''dummy-train-data'''       )    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''    ,	)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :BaseTransformer    ,	_SCREAMING_SNAKE_CASE      :argparse.Namespace    ,	_SCREAMING_SNAKE_CASE      :int=None    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=True    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=[]    ,	_SCREAMING_SNAKE_CASE      :Any=None    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=None    ,	**_SCREAMING_SNAKE_CASE      :Any    ,	):
				pl.seed_everything(args.seed       )

				# init model
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= Path(model.hparams.output_dir       )
				odir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )

				# 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(_SCREAMING_SNAKE_CASE       )
				if logging_callback is None:
								SCREAMING_SNAKE_CASE  :						List[str]      					= LoggingCallback()

				SCREAMING_SNAKE_CASE  :						Any      					= {}

				if args.fpaa:
								SCREAMING_SNAKE_CASE  :						int      					= 16

				if args.gpus > 1:
								SCREAMING_SNAKE_CASE  :						List[str]      					= '''auto'''
								SCREAMING_SNAKE_CASE  :						Tuple      					= '''ddp'''

				SCREAMING_SNAKE_CASE  :						Dict      					= args.accumulate_grad_batches
				SCREAMING_SNAKE_CASE  :						Tuple      					= None
				SCREAMING_SNAKE_CASE  :						List[Any]      					= '''auto'''

				SCREAMING_SNAKE_CASE  :						int      					= pl.Trainer.from_argparse_args(
				    _SCREAMING_SNAKE_CASE    ,	weights_summary=_SCREAMING_SNAKE_CASE    ,	callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]    ,	logger=_SCREAMING_SNAKE_CASE    ,	val_check_interval=1    ,	num_sanity_val_steps=2    ,	**_SCREAMING_SNAKE_CASE    ,	)

				if args.do_train:
								trainer.fit(_SCREAMING_SNAKE_CASE       )

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









'''simple docstring'''

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler


@dataclass
class 	a__     (				_lowercase	):
	__magic_name__		:    int	    =     0
	__magic_name__		:    bool	    =     False
	__magic_name__		:    float	    =     3.0



class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : str      )     ->			Union[str, Any]:

					"""simple docstring"""

					self.assertDictEqual(MockClass().to_kwargs(),					{}      )
					self.assertDictEqual(MockClass(a=2      ).to_kwargs(),					{'''a''': 2}      )
					self.assertDictEqual(MockClass(a=2,					b=__UpperCAmelCase      ).to_kwargs(),					{'''a''': 2, '''b''': True}      )
					self.assertDictEqual(MockClass(a=2,					c=2.25      ).to_kwargs(),					{'''a''': 2, '''c''': 2.25}      )


	@require_cuda
	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= GradScalerKwargs(init_scale=1024,					growth_factor=2      )
					AcceleratorState._reset_state()
					SCREAMING_SNAKE_CASE  :						Tuple      					= Accelerator(mixed_precision='''fp16''',					kwargs_handlers=[scaler_handler]      )
					print(accelerator.use_fpaa      )
					SCREAMING_SNAKE_CASE  :						str      					= accelerator.scaler

					# Check the kwargs have been applied
					self.assertEqual(scaler._init_scale,					1024.0      )
					self.assertEqual(scaler._growth_factor,					2.0      )

					# Check the other values are at the default
					self.assertEqual(scaler._backoff_factor,					0.5      )
					self.assertEqual(scaler._growth_interval,					2000      )
					self.assertEqual(scaler._enabled,					__UpperCAmelCase      )






	@require_multi_gpu
	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
					execute_subprocess_async(__UpperCAmelCase,					env=os.environ.copy()      )


if __name__ == "__main__":
	snake_case_			=     DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	snake_case_			=     Accelerator(kwargs_handlers=[ddp_scaler])
	snake_case_			=     torch.nn.Linear(100, 200)
	snake_case_			=     accelerator.prepare(model)

	# Check the values changed in kwargs
	snake_case_			=     """"""
	snake_case_			=     model.bucket_bytes_cap // (1024 * 1024)
	if observed_bucket_cap_map != 15:
		error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
	if model.find_unused_parameters is not True:
		error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

	# Check the values of the defaults
	if model.dim != 0:
		error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
	if model.broadcast_buffers is not True:
		error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
	if model.gradient_as_bucket_view is not False:
		error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)




