





'''simple docstring'''


# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")






class       UpperCamelCase__  :







	def __init__(				self			:   Dict , lowerCamelCase			:   List[str] , lowerCamelCase			:   Any , lowerCamelCase			:   bool = True , lowerCamelCase			:   bool = False		):




				'''simple docstring'''
				a__     = scheduler
				a__     = optimizers if isinstance(lowerCamelCase , (list, tuple)		) else [optimizers]
				a__     = split_batches
				a__     = step_with_optimizer
				a__     = GradientState()







	def 						__a   (				self			:   List[Any] , *lowerCamelCase			:   Union[str, Any] , **lowerCamelCase			:   Optional[int]		):




				'''simple docstring'''
				if not self.step_with_optimizer:
							# No link between scheduler and optimizer -> just step
							self.scheduler.step(*lowerCamelCase , **lowerCamelCase		)
							return

				# Otherwise, first make sure the optimizer was stepped.
				if not self.gradient_state.sync_gradients:
							if self.gradient_state.adjust_scheduler:
										self.scheduler._step_count += 1
							return

				for opt in self.optimizers:
							if opt.step_was_skipped:
										return
				if self.split_batches:
							# Split batches -> the training dataloader batch size is not changed so one step per training step
							self.scheduler.step(*lowerCamelCase , **lowerCamelCase		)
				else:
							# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
							# num_processes steps per training step
							a__     = AcceleratorState().num_processes
							for _ in range(lowerCamelCase		):
										# Special case when using OneCycle and `drop_last` was not used
										if hasattr(self.scheduler , "total_steps"		):
													if self.scheduler._step_count <= self.scheduler.total_steps:
																self.scheduler.step(*lowerCamelCase , **lowerCamelCase		)
										else:
													self.scheduler.step(*lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				return self.scheduler.get_last_lr()







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				return self.scheduler.state_dict()







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				self.scheduler.load_state_dict(lowerCamelCase		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				return self.scheduler.get_lr()







	def 						__a   (				self			:   List[str] , *lowerCamelCase			:   Optional[Any] , **lowerCamelCase			:   str		):




				'''simple docstring'''
				return self.scheduler.print_lr(*lowerCamelCase , **lowerCamelCase		)










'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    Optional[int]:
			a__     = [
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[Any]:
			a__   ,				a__     = emb.weight.shape
			a__     = nn.Linear(__lowerCamelCase					,  __lowerCamelCase					,  bias=__lowerCamelCase     )
			a__     = emb.weight.data
			return lin_layer







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"] or mam_aaa["cfg"]["model"]
			a__     = mam_aaa["model"]
			remove_ignore_keys_(__lowerCamelCase     )
			a__     = state_dict["encoder.embed_tokens.weight"].shape[0]

			a__     = MaMaaaConfig(
			    vocab_size=__lowerCamelCase					,  max_position_embeddings=1024					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  encoder_layerdrop=args.encoder_layerdrop					,  decoder_layerdrop=args.decoder_layerdrop					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  )

			a__     = state_dict["decoder.embed_tokens.weight"]
			a__     = MaMaaaForConditionalGeneration(__lowerCamelCase     )
			model.model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			a__     = make_linear_from_emb(model.model.shared     )

			return model


if __name__ == "__main__":
		lowerCAmelCase_   :   Tuple       =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		lowerCAmelCase_   :   Tuple       =					parser.parse_args()
		lowerCAmelCase_   :   int       =					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)



