

'''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    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[
      "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(snake_case      ,       snake_case )





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:str						      =					emb.weight.shape
  SCREAMING_SNAKE_CASE:Optional[int]						      =					nn.Linear(snake_case      ,       snake_case      ,       bias=snake_case )
  SCREAMING_SNAKE_CASE:Dict						      =					emb.weight.data
  return lin_layer





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





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case = WEIGHTS_NAME ):
  SCREAMING_SNAKE_CASE:int						      =					[]
  SCREAMING_SNAKE_CASE:Dict						      =					0
  os.makedirs(snake_case      ,       exist_ok=snake_case )

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

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

    # Add the metadata
  SCREAMING_SNAKE_CASE:str						      =					{"total_size": total_size}
  SCREAMING_SNAKE_CASE:List[str]						      =					{"metadata": metadata, "weight_map": weight_map}

  with open(os.path.join(snake_case      ,       snake_case )      ,       "w"      ,       encoding="utf-8" ) as f:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					json.dumps(snake_case      ,       indent=2      ,       sort_keys=snake_case ) + "\n"
    f.write(snake_case )

  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_						,	A_       						=							shard_on_the_fly(
        args.nllb_moe_checkpoint_path,
        args.pytorch_dump_folder_path,
        1_28,
        args.dtype,
    )

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









'''simple docstring'''

import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A_       						=							[
    "kernels/rwkv/wkv_cuda.cu",
    "kernels/rwkv/wkv_op.cpp",
    "kernels/deformable_detr/ms_deform_attn.h",
    "kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh",
    "models/graphormer/algos_graphormer.pyx",
]





def    A_  ( snake_case ):
  # Test all the extensions added in the setup
  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.")
    A_       						=							parser.parse_args()
    if args.check_lib:
        A_       						=							importlib.import_module("transformers")
        A_       						=							Path(transformers_module.__file__).parent
    else:
        A_       						=							Path.cwd() / "build/lib/transformers"
    if not test_custom_files_are_present(transformers_path):
        raise ValueError("The built release does not contain the custom files. Fix this before going further!")






