
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 		UpperCamelCase	(      lowercase_   )		->	Optional[Any]:


		'''simple docstring'''






		lowercase__  :   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(lowercase_ , lowercase_   )

def 		UpperCamelCase	(      lowercase_   )		->	Any:


		'''simple docstring'''






		lowercase__  :   Dict    		=      emb.weight.shape
		lowercase__  :   Optional[Any]    		=      nn.Linear(lowercase_ , lowercase_ , bias=lowercase_   )
		lowercase__  :   int    		=      emb.weight.data
		return lin_layer

def 		UpperCamelCase	(      lowercase_ , lowercase_=None   )		->	List[str]:


		'''simple docstring'''






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

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = WEIGHTS_NAME   )		->	List[Any]:


		'''simple docstring'''






		lowercase__  :   Any    		=      []
		lowercase__  :   int    		=      0
		os.makedirs(lowercase_ , exist_ok=lowercase_   )

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

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

				# Add the metadata
		lowercase__  :   Any    		=      {"""total_size""": total_size}
		lowercase__  :   Optional[int]    		=      {"""metadata""": metadata, """weight_map""": weight_map}

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

		return metadata, index


if __name__ == "__main__":
					lowerCamelCase__    :		Any          =   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__    :		Optional[int]          =   parser.parse_args()
					lowerCamelCase__    :		List[Any]          =   shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    1_2_8,
					    args.dtype,
					)

					lowerCamelCase__    :		Any          =   NllbMoeConfig.from_pretrained(
					    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
					)
					config.save_pretrained(args.pytorch_dump_folder_path)
					lowerCamelCase__    :		Dict          =   NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print("""Done""")
					model.save_pretrained(args.pytorch_dump_folder_path)








from ..utils import DummyObject, requires_backends



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     int        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     Any        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     List[Any]        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     Dict        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     List[str]        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])






