'''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_    ( snake_case_	:			Optional[Any]      )   ->				Optional[int]:


     '''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(__lowerCamelCase ,   __lowerCamelCase      )





def      lowerCAmelCase_    ( snake_case_	:			Tuple      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       emb.weight.shape
     UpperCAmelCase_	   =       nn.Linear(__lowerCamelCase ,   __lowerCamelCase ,   bias=__lowerCamelCase      )
     UpperCAmelCase_	   =       emb.weight.data
     return lin_layer





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int]=None      )   ->				Dict:


     '''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      lowerCAmelCase_    ( snake_case_	:			Optional[Any] ,   snake_case_	:			Dict ,   snake_case_	:			Optional[int] ,   snake_case_	:			Dict ,   snake_case_	:			Any = WEIGHTS_NAME      )   ->				Tuple:


     '''simple docstring'''




     UpperCAmelCase_	   =       []
     UpperCAmelCase_	   =       0
     os.makedirs(__lowerCamelCase ,   exist_ok=__lowerCamelCase      )

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

    # Add the last block
     UpperCAmelCase_	   =       os.path.join(__lowerCamelCase ,   weights_name.replace(".bin" ,   f"""-{len(__lowerCamelCase      )+1:05d}-of-???.bin"""      )      )
     UpperCAmelCase_	   =       torch.load(switch_checkpoint_path + "-shared.pt"      )["model"]
     remove_ignore_keys_(__lowerCamelCase      )
     UpperCAmelCase_	   =       rename_fairseq_keys(__lowerCamelCase ,   __lowerCamelCase      )
     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(__lowerCamelCase      ) == 1:
          UpperCAmelCase_	   =       os.path.join(__lowerCamelCase ,   __lowerCamelCase      )
          torch.save(__lowerCamelCase ,   __lowerCamelCase      )
          return {weights_name: sharded_state_dicts[0]}, None
     else:
          torch.save(__lowerCamelCase ,   __lowerCamelCase      )
     # Otherwise, let's build the index
     UpperCAmelCase_	   =       {}
     for idx, shard in enumerate(__lowerCamelCase      ):
          UpperCAmelCase_	   =       weights_name.replace(".bin" ,   f"""-{idx+1:05d}-of-{len(__lowerCamelCase      ):05d}.bin"""      )
          UpperCAmelCase_	   =       os.path.join(__lowerCamelCase ,   weights_name.replace(".bin" ,   f"""-{idx+1:05d}-of-???.bin"""      )      )
          os.rename(__lowerCamelCase ,   os.path.join(__lowerCamelCase ,   __lowerCamelCase      )      )
          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(__lowerCamelCase ,   __lowerCamelCase      ) ,   "w" ,   encoding="utf-8"      ) as f:
          UpperCAmelCase_	   =       json.dumps(__lowerCamelCase ,   indent=2 ,   sort_keys=__lowerCamelCase      ) + "\n"
          f.write(__lowerCamelCase      )

     return metadata, index


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					str  =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.',
    )
    SCREAMING_SNAKE_CASE_:					Optional[Any]  =parser.parse_args()
    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_:					Optional[Any]  =shard_on_the_fly(
        args.nllb_moe_checkpoint_path,
        args.pytorch_dump_folder_path,
        1_28,
        args.dtype,
    )

    SCREAMING_SNAKE_CASE_:					Any  =NllbMoeConfig.from_pretrained(
        'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
    )
    config.save_pretrained(args.pytorch_dump_folder_path)
    SCREAMING_SNAKE_CASE_:					Tuple  =NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
    print('Done')
    model.save_pretrained(args.pytorch_dump_folder_path)





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_:					int  ={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					List[str]  =['MaskFormerFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['MaskFormerImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Dict  =[
        'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MaskFormerForInstanceSegmentation',
        'MaskFormerModel',
        'MaskFormerPreTrainedModel',
    ]
    SCREAMING_SNAKE_CASE_:					List[str]  =[
        'MaskFormerSwinBackbone',
        'MaskFormerSwinModel',
        'MaskFormerSwinPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Dict  =_LazyModule(__name__, globals()['__file__'], _import_structure)




