




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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						[
        '''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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase		,      __UpperCamelCase  		=						emb.weight.shape
    __UpperCamelCase  		=						nn.Linear(_A					,     _A					,     bias=_A      )
    __UpperCamelCase  		=						emb.weight.data
    return lin_layer




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=None      )       ->	Any:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_ = WEIGHTS_NAME      )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						0
    os.makedirs(_A					,     exist_ok=_A      )

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

    # Add the metadata
    __UpperCamelCase  		=						{'''total_size''': total_size}
    __UpperCamelCase  		=						{'''metadata''': metadata, '''weight_map''': weight_map}

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

    return metadata, index


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

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












from collections import OrderedDict
from typing import Mapping

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


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					"big_bird"







      def __init__(     self					: str ,       snake_case					: Optional[Any]=50358 ,       snake_case					: Union[str, Any]=768 ,       snake_case					: Dict=12 ,       snake_case					: List[Any]=12 ,       snake_case					: Tuple=3072 ,       snake_case					: Tuple="gelu_new" ,       snake_case					: Dict=0.1 ,       snake_case					: Any=0.1 ,       snake_case					: List[Any]=4096 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: Union[str, Any]=0.02 ,       snake_case					: Union[str, Any]=1E-12 ,       snake_case					: List[Any]=True ,       snake_case					: List[Any]=0 ,       snake_case					: int=1 ,       snake_case					: int=2 ,       snake_case					: Any=66 ,       snake_case					: str="block_sparse" ,       snake_case					: Any=True ,       snake_case					: Tuple=False ,       snake_case					: List[Any]=64 ,       snake_case					: Dict=3 ,       snake_case					: Union[str, Any]=None ,       **snake_case					: Any ,       ):
          super().__init__(
              pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       sep_token_id=snake_case ,       **snake_case ,       )

          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						type_vocab_size
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						rescale_embeddings
          __UpperCamelCase  		=						attention_type
          __UpperCamelCase  		=						use_bias
          __UpperCamelCase  		=						block_size
          __UpperCamelCase  		=						num_random_blocks
          __UpperCamelCase  		=						classifier_dropout







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @property
      def       snake_case	(     self					: int				):
          if self.task == "multiple-choice":
              __UpperCamelCase  		=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              __UpperCamelCase  		=						{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]				)






