

'''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  a__       (    _SCREAMING_SNAKE_CASE   :       Union[str, Any]				)     ->					Optional[Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      Optional[Any]						=  [
	    "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(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)


def  a__       (    _SCREAMING_SNAKE_CASE   :       Optional[Any]				)     ->					str:


	"""simple docstring"""





	UpperCAmelCase_					,					UpperCAmelCase_     :      str						=  emb.weight.shape
	UpperCAmelCase_     :      List[Any]						=  nn.Linear(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				bias=_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Tuple						=  emb.weight.data
	return lin_layer


def  a__       (    _SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]=None				)     ->					Union[str, Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      Optional[Any]						=  {}
	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_     :      List[Any]						=  key.replace("moe_layer.experts.0"	,				F'''ffn.experts.expert_{expert_idx}'''				)
			else:
				UpperCAmelCase_     :      Tuple						=  key.replace("moe_layer.experts."	,				"ffn.experts.expert_"				)
		if "gate" in key:
			UpperCAmelCase_     :      List[Any]						=  key.replace(".moe_layer.gate.wg"	,				".ffn.router.classifier"				)
		if "fc2" and "experts" not in key:
			UpperCAmelCase_     :      Dict						=  key.replace(".fc2."	,				".ffn.fc2."				)
		if "fc1" and "experts" not in key:
			UpperCAmelCase_     :      List[str]						=  key.replace(".fc1."	,				".ffn.fc1."				)
		if ".encoder_attn." in key:
			UpperCAmelCase_     :      List[str]						=  key.replace(".encoder_attn."	,				".cross_attention."				)
		if "encoder_attn_layer_norm" in key:
			UpperCAmelCase_     :      Union[str, Any]						=  key.replace("encoder_attn_layer_norm"	,				"cross_attention_layer_norm"				)
		if "final_layer_norm" in key:
			UpperCAmelCase_     :      Any						=  key.replace("final_layer_norm"	,				"ff_layer_norm"				)
		UpperCAmelCase_     :      Optional[Any]						=  state_dict[old_key]
	return new_dict


def  a__       (    _SCREAMING_SNAKE_CASE   :       int	,				_SCREAMING_SNAKE_CASE   :       Optional[int]	,				_SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       Tuple	,				_SCREAMING_SNAKE_CASE   :       str = WEIGHTS_NAME				)     ->					str:


	"""simple docstring"""





	UpperCAmelCase_     :      Optional[Any]						=  []
	UpperCAmelCase_     :      Optional[Any]						=  0
	os.makedirs(_SCREAMING_SNAKE_CASE	,				exist_ok=_SCREAMING_SNAKE_CASE				)

	for expert in range(_SCREAMING_SNAKE_CASE				):
		UpperCAmelCase_     :      Any						=  switch_checkpoint_path + F'''-rank-{expert}.pt'''
		if os.path.isfile(_SCREAMING_SNAKE_CASE				):
			UpperCAmelCase_     :      List[Any]						=  torch.load(_SCREAMING_SNAKE_CASE				)["model"]
			remove_ignore_keys_(_SCREAMING_SNAKE_CASE				)
			UpperCAmelCase_     :      List[Any]						=  rename_fairseq_keys(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
			UpperCAmelCase_     :      Union[str, Any]						=  os.path.join(
			    _SCREAMING_SNAKE_CASE	,				weights_name.replace(".bin"	,				F'''-{len(_SCREAMING_SNAKE_CASE				)+1:05d}-of-???.bin'''				)				)
			torch.save(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
			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(_SCREAMING_SNAKE_CASE				)[0]].dtype				)

    # Add the last block
	UpperCAmelCase_     :      Tuple						=  os.path.join(_SCREAMING_SNAKE_CASE	,				weights_name.replace(".bin"	,				F'''-{len(_SCREAMING_SNAKE_CASE				)+1:05d}-of-???.bin'''				)				)
	UpperCAmelCase_     :      List[str]						=  torch.load(switch_checkpoint_path + "-shared.pt"				)["model"]
	remove_ignore_keys_(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      str						=  rename_fairseq_keys(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      List[str]						=  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(_SCREAMING_SNAKE_CASE				) == 1:
		UpperCAmelCase_     :      Dict						=  os.path.join(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
		torch.save(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
		return {weights_name: sharded_state_dicts[0]}, None
	else:
		torch.save(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	# Otherwise, let's build the index
	UpperCAmelCase_     :      Optional[Any]						=  {}
	for idx, shard in enumerate(_SCREAMING_SNAKE_CASE				):
		UpperCAmelCase_     :      str						=  weights_name.replace(".bin"	,				F'''-{idx+1:05d}-of-{len(_SCREAMING_SNAKE_CASE				):05d}.bin'''				)
		UpperCAmelCase_     :      List[Any]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				weights_name.replace(".bin"	,				F'''-{idx+1:05d}-of-???.bin'''				)				)
		os.rename(_SCREAMING_SNAKE_CASE	,				os.path.join(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)				)
		for key in shard:
			UpperCAmelCase_     :      Tuple						=  shard_file

    # Add the metadata
	UpperCAmelCase_     :      Dict						=  {"total_size": total_size}
	UpperCAmelCase_     :      List[Any]						=  {"metadata": metadata, "weight_map": weight_map}

	with open(os.path.join(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)	,				"w"	,				encoding="utf-8"				) as f:
		UpperCAmelCase_     :      Dict						=  json.dumps(_SCREAMING_SNAKE_CASE	,				indent=2	,				sort_keys=_SCREAMING_SNAKE_CASE				) + "\n"
		f.write(_SCREAMING_SNAKE_CASE				)

	return metadata, index


if __name__ == "__main__":
					_lowerCamelCase      					=     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.""",
					)
					_lowerCamelCase      					=     parser.parse_args()
					_lowerCamelCase					,      _lowerCamelCase      					=     shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    128,
					    args.dtype,
					)

					_lowerCamelCase      					=     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)
					_lowerCamelCase      					=     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print("""Done""")
					model.save_pretrained(args.pytorch_dump_folder_path)












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''ChineseCLIPImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,       __lowerCAmelCase							,       )
												__magic_name__		:Optional[Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:Tuple   =   image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.'''  )
								if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.'''  )

								super().__init__(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""

								if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''  )

								if text is not None:
												__magic_name__		:int   =   self.tokenizer(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if images is not None:
												__magic_name__		:Dict   =   self.image_processor(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if text is not None and images is not None:
												__magic_name__		:Union[str, Any]   =   image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__lowerCAmelCase  )							,       tensor_type=__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor_class






