

'''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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict      )			->      int:




				'''simple docstring'''





				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(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[Any]      )			->      List[Any]:




				'''simple docstring'''





				A__    ,				A__        =			emb.weight.shape
				A__        =			nn.Linear(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       bias=SCREAMING_SNAKE_CASE_      )
				A__        =			emb.weight.data
				return lin_layer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							Tuple=None      )			->      Optional[Any]:




				'''simple docstring'''





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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							int	,       SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							str = WEIGHTS_NAME      )			->      List[Any]:




				'''simple docstring'''





				A__        =			[]
				A__        =			0
				os.makedirs(SCREAMING_SNAKE_CASE_	,       exist_ok=SCREAMING_SNAKE_CASE_      )

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

    # Add the metadata
				A__        =			{"total_size": total_size}
				A__        =			{"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )	,       "w"	,       encoding="utf-8"      ) as f:
								A__        =			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,
	    1_2_8,
	    args.dtype,
	)

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




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Namespace      )			->      Tuple:




				'''simple docstring'''





				return ConvertCommand(
				    args.model_type	,       args.tf_checkpoint	,       args.pytorch_dump_output	,       args.config	,       args.finetuning_task_name      )


lowerCAmelCase__	   = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""





class 					a__						(					snake_case			):
			"""simple docstring"""







			@staticmethod
			def      UpperCamelCase		(    lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			parser.add_parser(
							    "convert"      ,      help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."      ,      )
							train_parser.add_argument("--model_type"      ,      type=lowercase      ,      required=lowercase      ,      help="Model's type."       )
							train_parser.add_argument(
							    "--tf_checkpoint"      ,      type=lowercase      ,      required=lowercase      ,      help="TensorFlow checkpoint path or folder."       )
							train_parser.add_argument(
							    "--pytorch_dump_output"      ,      type=lowercase      ,      required=lowercase      ,      help="Path to the PyTorch saved model output."       )
							train_parser.add_argument("--config"      ,      type=lowercase      ,      default=""      ,      help="Configuration file path or folder."       )
							train_parser.add_argument(
							    "--finetuning_task_name"      ,      type=lowercase      ,      default=lowercase      ,      help="Optional fine-tuning task name if the TF model was a finetuned model."      ,      )
							train_parser.set_defaults(func=lowercase       )


			def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      *lowercase      ,      )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			logging.get_logger("transformers-cli/converting"       )

							self._logger.info(F'Loading model {model_type}'       )
							A__        =			model_type
							A__        =			tf_checkpoint
							A__        =			pytorch_dump_output
							A__        =			config
							A__        =			finetuning_task_name


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							if self._model_type == "albert":
											try:
															from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "bert":
											try:
															from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "funnel":
											try:
															from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "t5":
											try:
															from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "gpt":
											from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
											    convert_openai_checkpoint_to_pytorch,
											)

											convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "transfo_xl":
											try:
															from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
															    convert_transfo_xl_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											if "ckpt" in self._tf_checkpoint.lower():
															A__        =			self._tf_checkpoint
															A__        =			""
											else:
															A__        =			self._tf_checkpoint
															A__        =			""
											convert_transfo_xl_checkpoint_to_pytorch(
											    lowercase      ,      self._config      ,      self._pytorch_dump_output      ,      lowercase       )
							elif self._model_type == "gpt2":
											try:
															from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
															    convert_gpta_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "xlnet":
											try:
															from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
															    convert_xlnet_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_xlnet_checkpoint_to_pytorch(
											    self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output      ,      self._finetuning_task_name       )
							elif self._model_type == "xlm":
											from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
											    convert_xlm_checkpoint_to_pytorch,
											)

											convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output       )
							elif self._model_type == "lxmert":
											from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
											    convert_lxmert_checkpoint_to_pytorch,
											)

											convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output       )
							elif self._model_type == "rembert":
											from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
											    convert_rembert_tf_checkpoint_to_pytorch,
											)

											convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							else:
											raise ValueError(
											    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"       )

