



import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Optional[Any]						,       _lowercase    :       List[str])							->	Optional[int]:







			"""simple docstring"""
			# Initialise PyTorch model
			a__       :						Tuple							=  FunnelConfig.from_json_file(_lowercase)
			print(F'''Building PyTorch model from configuration: {config}''')
			a__       :						str							=  FunnelBaseModel(_lowercase) if base_model else FunnelModel(_lowercase)

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(_lowercase						,       _lowercase						,       _lowercase)

			# Save pytorch-model
			print(F'''Save PyTorch model to {pytorch_dump_path}''')
			torch.save(model.state_dict()						,       _lowercase)


if __name__ == "__main__":
		_lowercase		:				Tuple      		=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
		)
		_lowercase		:				List[str]      		=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
		)









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      lowerCAmelCase_					(  _lowercase    :       List[str])							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						List[str]							=  [
			    """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      lowerCAmelCase_					(  _lowercase    :       List[Any])							->	Optional[Any]:







			"""simple docstring"""
			a__	,						a__       :						Union[str, Any]							=  emb.weight.shape
			a__       :						str							=  nn.Linear(_lowercase						,       _lowercase						,       bias=_lowercase)
			a__       :						Any							=  emb.weight.data
			return lin_layer

def      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       int=None)							->	List[Any]:







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

def      lowerCAmelCase_					(  _lowercase    :       Tuple						,       _lowercase    :       Optional[int]						,       _lowercase    :       Any						,       _lowercase    :       Dict						,       _lowercase    :       str = WEIGHTS_NAME)							->	Tuple:







			"""simple docstring"""
			a__       :						Tuple							=  []
			a__       :						Optional[Any]							=  0
			os.makedirs(_lowercase						,       exist_ok=_lowercase)

			for expert in range(_lowercase):
						a__       :						str							=  switch_checkpoint_path + F'''-rank-{expert}.pt'''
						if os.path.isfile(_lowercase):
									a__       :						List[str]							=  torch.load(_lowercase)["""model"""]
									remove_ignore_keys_(_lowercase)
									a__       :						Tuple							=  rename_fairseq_keys(_lowercase						,       _lowercase)
									a__       :						str							=  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
			a__       :						int							=  os.path.join(_lowercase						,       weights_name.replace(""".bin"""						,       F'''-{len(_lowercase)+1:05d}-of-???.bin'''))
			a__       :						Union[str, Any]							=  torch.load(switch_checkpoint_path + """-shared.pt""")["""model"""]
			remove_ignore_keys_(_lowercase)
			a__       :						List[str]							=  rename_fairseq_keys(_lowercase						,       _lowercase)
			a__       :						int							=  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:
						a__       :						Optional[int]							=  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
			a__       :						List[str]							=  {}
			for idx, shard in enumerate(_lowercase):
						a__       :						Union[str, Any]							=  weights_name.replace(""".bin"""						,       F'''-{idx+1:05d}-of-{len(_lowercase):05d}.bin''')
						a__       :						List[str]							=  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:
									a__       :						Tuple							=  shard_file

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

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

			return metadata, index


if __name__ == "__main__":
		_lowercase		:				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.",
		)
		_lowercase		:				Tuple      		=parser.parse_args()
		_lowercase		,					_lowercase		:				List[Any]      		=shard_on_the_fly(
		    args.nllb_moe_checkpoint_path,
		    args.pytorch_dump_folder_path,
		    128,
		    args.dtype,
		)

		_lowercase		:				int      		=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)
		_lowercase		:				List[str]      		=NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
		print("Done")
		model.save_pretrained(args.pytorch_dump_folder_path)




