



'''simple docstring'''






import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME


def  snake_case_     (					_lowerCAmelCase  :						List[Any]  )      ->   Union[str, Any]:
							UpperCAmelCase :       int 							=		[
							    '''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  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   Tuple:
							UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		emb.weight.shape
							UpperCAmelCase :       Union[str, Any] 							=		nn.Linear(_lowerCAmelCase   ,      _lowerCAmelCase   ,      bias=_lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		emb.weight.data
							return lin_layer


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						List[Any]=None  )      ->   Tuple:
							UpperCAmelCase :       List[str] 							=		{}
							for old_key in state_dict.keys():
														UpperCAmelCase :       Optional[int] 							=		old_key
														if "moe_layer.experts." in key:
																					if expert_idx is not None:
																												UpperCAmelCase :       str 							=		key.replace('''moe_layer.experts.0'''   ,      f"""ffn.experts.expert_{expert_idx}"""  )
																					else:
																												UpperCAmelCase :       List[str] 							=		key.replace('''moe_layer.experts.'''   ,      '''ffn.experts.expert_'''  )
														if "gate" in key:
																					UpperCAmelCase :       int 							=		key.replace('''.moe_layer.gate.wg'''   ,      '''.ffn.router.classifier'''  )
														if "fc2" and "experts" not in key:
																					UpperCAmelCase :       Optional[int] 							=		key.replace('''.fc2.'''   ,      '''.ffn.fc2.'''  )
														if "fc1" and "experts" not in key:
																					UpperCAmelCase :       List[Any] 							=		key.replace('''.fc1.'''   ,      '''.ffn.fc1.'''  )
														if ".encoder_attn." in key:
																					UpperCAmelCase :       Dict 							=		key.replace('''.encoder_attn.'''   ,      '''.cross_attention.'''  )
														if "encoder_attn_layer_norm" in key:
																					UpperCAmelCase :       int 							=		key.replace('''encoder_attn_layer_norm'''   ,      '''cross_attention_layer_norm'''  )
														if "final_layer_norm" in key:
																					UpperCAmelCase :       Optional[Any] 							=		key.replace('''final_layer_norm'''   ,      '''ff_layer_norm'''  )
														UpperCAmelCase :       int 							=		state_dict[old_key]
							return new_dict


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						str = WEIGHTS_NAME  )      ->   Optional[int]:
							UpperCAmelCase :       Optional[int] 							=		[]
							UpperCAmelCase :       Any 							=		0
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )

							for expert in range(_lowerCAmelCase  ):
														UpperCAmelCase :       str 							=		switch_checkpoint_path + f"""-rank-{expert}.pt"""
														if os.path.isfile(_lowerCAmelCase  ):
																					UpperCAmelCase :       Optional[Any] 							=		torch.load(_lowerCAmelCase  )['''model''']
																					remove_ignore_keys_(_lowerCAmelCase  )
																					UpperCAmelCase :       List[Any] 							=		rename_fairseq_keys(_lowerCAmelCase   ,      _lowerCAmelCase  )
																					UpperCAmelCase :       Optional[int] 							=		os.path.join(
																					    _lowerCAmelCase   ,      weights_name.replace('''.bin'''   ,      f"""-{len(_lowerCAmelCase  )+1:05d}-of-???.bin"""  )  )
																					torch.save(_lowerCAmelCase   ,      _lowerCAmelCase  )
																					sharded_state_dicts.append(expert_state.keys()  )
																					total_size += sum([value.numel() for key, value in expert_state.items()]  ) * dtype_byte_size(
																					    expert_state[list(_lowerCAmelCase  )[0]].dtype  )

    # Add the last block
							UpperCAmelCase :       Dict 							=		os.path.join(_lowerCAmelCase   ,      weights_name.replace('''.bin'''   ,      f"""-{len(_lowerCAmelCase  )+1:05d}-of-???.bin"""  )  )
							UpperCAmelCase :       Any 							=		torch.load(switch_checkpoint_path + '''-shared.pt'''  )['''model''']
							remove_ignore_keys_(_lowerCAmelCase  )
							UpperCAmelCase :       int 							=		rename_fairseq_keys(_lowerCAmelCase   ,      _lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		shared_weights['''decoder.embed_tokens.weight''']
							sharded_state_dicts.append(shared_weights.keys()  )

							# If we only have the shared weights (dummy model/experts saved on the same file)
							if len(_lowerCAmelCase  ) == 1:
														UpperCAmelCase :       List[str] 							=		os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )
														torch.save(_lowerCAmelCase   ,      _lowerCAmelCase  )
														return {weights_name: sharded_state_dicts[0]}, None
							else:
														torch.save(_lowerCAmelCase   ,      _lowerCAmelCase  )
							# Otherwise, let's build the index
							UpperCAmelCase :       str 							=		{}
							for idx, shard in enumerate(_lowerCAmelCase  ):
														UpperCAmelCase :       Optional[int] 							=		weights_name.replace('''.bin'''   ,      f"""-{idx+1:05d}-of-{len(_lowerCAmelCase  ):05d}.bin"""  )
														UpperCAmelCase :       int 							=		os.path.join(_lowerCAmelCase   ,      weights_name.replace('''.bin'''   ,      f"""-{idx+1:05d}-of-???.bin"""  )  )
														os.rename(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )  )
														for key in shard:
																					UpperCAmelCase :       Any 							=		shard_file

    # Add the metadata
							UpperCAmelCase :       List[str] 							=		{'''total_size''': total_size}
							UpperCAmelCase :       List[str] 							=		{'''metadata''': metadata, '''weight_map''': weight_map}

							with open(os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )   ,      '''w'''   ,      encoding='''utf-8'''  ) as f:
														UpperCAmelCase :       List[Any] 							=		json.dumps(_lowerCAmelCase   ,      indent=2   ,      sort_keys=_lowerCAmelCase  ) + '''\n'''
														f.write(_lowerCAmelCase  )

							return metadata, index


if __name__ == "__main__":
						UpperCamelCase__:   Tuple								=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--nllb_moe_checkpoint_path",
						    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
						    type=str,
						    required=False,
						    help="Path to a directory containing a folder per layer. Follows the original Google format.",
						)
						parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
						    type=str,
						    required=False,
						    help="Path to the output pytorch model.",
						)
						UpperCamelCase__:   List[Any]								=			parser.parse_args()
						UpperCamelCase__	,					UpperCamelCase__:   Optional[Any]								=			shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						UpperCamelCase__:   Optional[Any]								=			NllbMoeConfig.from_pretrained(
						    "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						UpperCamelCase__:   str								=			NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)










'''simple docstring'''






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       """MCTCTFeatureExtractor"""
	lowerCamelCase__			 =       """AutoTokenizer"""







	def __init__(						self			:     Dict		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		)			->		str:
								super().__init__(__snake_case		,			__snake_case		)
								UpperCAmelCase :       List[Any] 							=		self.feature_extractor
								UpperCAmelCase :       Union[str, Any] 							=		False







	def __call__(						self			:     Any		,			*__snake_case			:     List[str]		,			**__snake_case			:     Any		)			->		List[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
															return self.current_processor(*__snake_case		,			**__snake_case		)

								if "raw_speech" in kwargs:
															warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''		)
															UpperCAmelCase :       int 							=		kwargs.pop('''raw_speech'''		)
								else:
															UpperCAmelCase :       Union[str, Any] 							=		kwargs.pop('''audio'''		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		kwargs.pop('''sampling_rate'''		,			__snake_case		)
								UpperCAmelCase :       Dict 							=		kwargs.pop('''text'''		,			__snake_case		)
								if len(__snake_case		) > 0:
															UpperCAmelCase :       Any 							=		args[0]
															UpperCAmelCase :       Optional[int] 							=		args[1:]

								if audio is None and text is None:
															raise ValueError('''You need to specify either an `audio` or `text` input to process.'''		)

								if audio is not None:
															UpperCAmelCase :       List[str] 							=		self.feature_extractor(__snake_case		,			*__snake_case		,			sampling_rate=__snake_case		,			**__snake_case		)
								if text is not None:
															UpperCAmelCase :       int 							=		self.tokenizer(__snake_case		,			**__snake_case		)

								if text is None:
															return inputs
								elif audio is None:
															return encodings
								else:
															UpperCAmelCase :       str 							=		encodings['''input_ids''']
															return inputs







	def      A	(						self			:     List[Any]		,			*__snake_case			:     List[Any]		,			**__snake_case			:     List[Any]		)			->		str:
								return self.tokenizer.batch_decode(*__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[Any]		,			*__snake_case			:     int		,			**__snake_case			:     Optional[int]		)			->		Any:
								# For backward compatibility
								if self._in_target_context_manager:
															return self.current_processor.pad(*__snake_case		,			**__snake_case		)

								UpperCAmelCase :       List[Any] 							=		kwargs.pop('''input_features'''		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		kwargs.pop('''labels'''		,			__snake_case		)
								if len(__snake_case		) > 0:
															UpperCAmelCase :       List[str] 							=		args[0]
															UpperCAmelCase :       List[Any] 							=		args[1:]

								if input_features is not None:
															UpperCAmelCase :       Tuple 							=		self.feature_extractor.pad(__snake_case		,			*__snake_case		,			**__snake_case		)
								if labels is not None:
															UpperCAmelCase :       Optional[int] 							=		self.tokenizer.pad(__snake_case		,			**__snake_case		)

								if labels is None:
															return input_features
								elif input_features is None:
															return labels
								else:
															UpperCAmelCase :       List[str] 							=		labels['''input_ids''']
															return input_features







	def      A	(						self			:     Union[str, Any]		,			*__snake_case			:     Optional[Any]		,			**__snake_case			:     Optional[int]		)			->		Optional[Any]:
								return self.tokenizer.decode(*__snake_case		,			**__snake_case		)







	@contextmanager
	def      A	(						self			:     Any		)			->		Optional[int]:
								warnings.warn(
								    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
								    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
								    '''your audio inputs, or in a separate call.'''		)
								UpperCAmelCase :       Dict 							=		True
								UpperCAmelCase :       List[Any] 							=		self.tokenizer
								yield
								UpperCAmelCase :       Tuple 							=		self.feature_extractor
								UpperCAmelCase :       List[Any] 							=		False





