



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_   :int        =      logging.get_logger(__name__)

a_   :List[Any]        =      {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""bert"""






     def __init__(       self  :  Optional[Any], _snake_case  :  Optional[Any]=3_0_5_2_2, _snake_case  :  Any=7_6_8, _snake_case  :  Tuple=1_2, _snake_case  :  Union[str, Any]=1_2, _snake_case  :  int=3_0_7_2, _snake_case  :  List[Any]="gelu", _snake_case  :  str=0.1, _snake_case  :  Tuple=0.1, _snake_case  :  Optional[Any]=5_1_2, _snake_case  :  int=2, _snake_case  :  List[str]=0.0_2, _snake_case  :  Optional[int]=1e-12, _snake_case  :  Optional[Any]=0, _snake_case  :  Union[str, Any]="absolute", _snake_case  :  Any=True, _snake_case  :  Union[str, Any]=None, **_snake_case  :  str, )   ->str:
            super().__init__(pad_token_id=_snake_case, **_snake_case					)

            snake_case__							:	Dict   	=	vocab_size
            snake_case__							:	Union[str, Any]   	=	hidden_size
            snake_case__							:	Union[str, Any]   	=	num_hidden_layers
            snake_case__							:	List[Any]   	=	num_attention_heads
            snake_case__							:	List[str]   	=	hidden_act
            snake_case__							:	List[str]   	=	intermediate_size
            snake_case__							:	Union[str, Any]   	=	hidden_dropout_prob
            snake_case__							:	Union[str, Any]   	=	attention_probs_dropout_prob
            snake_case__							:	str   	=	max_position_embeddings
            snake_case__							:	Tuple   	=	type_vocab_size
            snake_case__							:	Tuple   	=	initializer_range
            snake_case__							:	Optional[int]   	=	layer_norm_eps
            snake_case__							:	List[Any]   	=	position_embedding_type
            snake_case__							:	Tuple   	=	use_cache
            snake_case__							:	Tuple   	=	classifier_dropout




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  Any					)   ->Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   snake_case__							:	Dict   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	int   	=	{0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                    ('token_type_ids', dynamic_axis),
                ]					)











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        lowercase_							(A : List[str]      ):
       snake_case__							:	Tuple   	=	[
           '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(A		,					A      )
def        lowercase_							(A : str      ):
       snake_case__	,				snake_case__							:	Union[str, Any]   	=	emb.weight.shape
       snake_case__							:	str   	=	nn.Linear(A		,					A		,					bias=A      )
       snake_case__							:	str   	=	emb.weight.data
       return lin_layer
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]=None      ):
       snake_case__							:	Any   	=	{}
       for old_key in state_dict.keys():
              snake_case__							:	Tuple   	=	old_key
              if "moe_layer.experts." in key:
                     if expert_idx is not None:
                            snake_case__							:	int   	=	key.replace('moe_layer.experts.0'		,					F'''ffn.experts.expert_{expert_idx}'''      )
                     else:
                            snake_case__							:	Any   	=	key.replace('moe_layer.experts.'		,					'ffn.experts.expert_'      )
              if "gate" in key:
                     snake_case__							:	Dict   	=	key.replace('.moe_layer.gate.wg'		,					'.ffn.router.classifier'      )
              if "fc2" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc2.'		,					'.ffn.fc2.'      )
              if "fc1" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc1.'		,					'.ffn.fc1.'      )
              if ".encoder_attn." in key:
                     snake_case__							:	Tuple   	=	key.replace('.encoder_attn.'		,					'.cross_attention.'      )
              if "encoder_attn_layer_norm" in key:
                     snake_case__							:	Tuple   	=	key.replace('encoder_attn_layer_norm'		,					'cross_attention_layer_norm'      )
              if "final_layer_norm" in key:
                     snake_case__							:	Optional[int]   	=	key.replace('final_layer_norm'		,					'ff_layer_norm'      )
              snake_case__							:	Dict   	=	state_dict[old_key]
       return new_dict
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : List[Any]		,					A : List[str]		,					A : str = WEIGHTS_NAME      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	str   	=	0
       os.makedirs(A		,					exist_ok=A      )

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

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

    # Add the metadata
       snake_case__							:	int   	=	{'total_size': total_size}
       snake_case__							:	Dict   	=	{'metadata': metadata, 'weight_map': weight_map}

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

       return metadata, index


if __name__ == "__main__":
 a_   :int        =      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.",
 )
 a_   :Optional[Any]        =      parser.parse_args()
 a_  ,						a_   :Optional[Any]        =      shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

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






