



"""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 				UpperCamelCase_    (    lowerCAmelCase__      :				Any						)						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				int								= [
      '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 				UpperCamelCase_    (    lowerCAmelCase__      :				int						)						->   Any:

  """simple docstring"""




  lowerCAmelCase_						,lowerCAmelCase_   :				int								= emb.weight.shape
  lowerCAmelCase_   :				Tuple								= nn.Linear(lowerCAmelCase__    ,   lowerCAmelCase__    ,   bias=lowerCAmelCase__						)
  lowerCAmelCase_   :				Any								= emb.weight.data
  return lin_layer

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				Union[str, Any]=None						)						->   List[str]:

  """simple docstring"""




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

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

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= []
  lowerCAmelCase_   :				Optional[int]								= 0
  os.makedirs(lowerCAmelCase__    ,   exist_ok=lowerCAmelCase__						)

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

    # Add the metadata
  lowerCAmelCase_   :				Tuple								= {'total_size': total_size}
  lowerCAmelCase_   :				Union[str, Any]								= {'metadata': metadata, 'weight_map': weight_map}

  with open(os.path.join(lowerCAmelCase__    ,   lowerCAmelCase__						)    ,   'w'    ,   encoding='utf-8'						) as f:
    lowerCAmelCase_   :				List[str]								= json.dumps(lowerCAmelCase__    ,   indent=2    ,   sort_keys=lowerCAmelCase__						) + '\n'
    f.write(lowerCAmelCase__						)

  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__		:				str          =	parser.parse_args()
     lowercase__    ,     lowercase__		:				str          =	shard_on_the_fly(
         args.nllb_moe_checkpoint_path,
         args.pytorch_dump_folder_path,
         1_2_8,
         args.dtype,
     )

     lowercase__		:				int          =	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)
     lowercase__		:				int          =	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
     print("""Done""")
     model.save_pretrained(args.pytorch_dump_folder_path)










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:				Tuple          =	logging.get_logger(__name__)

lowercase__		:				Any          =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """dpr"""



      def __init__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=3_0_5_2_2    ,			SCREAMING_SNAKE_CASE_				:      Any=7_6_8    ,			SCREAMING_SNAKE_CASE_				:      Dict=1_2    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1_2    ,			SCREAMING_SNAKE_CASE_				:      int=3_0_7_2    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="gelu"    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=0.02    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1E-12    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="absolute"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )

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





