



"""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__      :				List[Any]						)						->   str:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= [
      "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__      :				Any						)						->   List[str]:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[Any]								= emb.weight.shape
  lowerCAmelCase_   :				str								= nn.Linear(__lowerCAmelCase    ,   __lowerCAmelCase    ,   bias=__lowerCAmelCase						)
  lowerCAmelCase_   :				Optional[int]								= emb.weight.data
  return lin_layer

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

  """simple docstring"""




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

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

  """simple docstring"""




  lowerCAmelCase_   :				Optional[int]								= []
  lowerCAmelCase_   :				Optional[Any]								= 0
  os.makedirs(__lowerCAmelCase    ,   exist_ok=__lowerCAmelCase						)

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

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

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

  return metadata, index


if __name__ == "__main__":
     lowercase__		:				List[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.""",
     )
     lowercase__		:				Dict          =	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__		:				Dict          =	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__		:				Union[str, Any]          =	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
     print("""Done""")
     model.save_pretrained(args.pytorch_dump_folder_path)









'''simple docstring'''


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase__					=   {
    'text_branch': 'text_model',
    'audio_branch': 'audio_model.audio_encoder',
    'attn': 'attention.self',
    'self.proj': 'output.dense',
    'attention.self_mask': 'attn_mask',
    'mlp.fc1': 'intermediate.dense',
    'mlp.fc2': 'output.dense',
    'norm1': 'layernorm_before',
    'norm2': 'layernorm_after',
    'bn0': 'batch_norm',
}

lowerCamelCase__					=   AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Optional[Any]	    =					create_model(
		    "HTSAT-tiny"					,       "roberta"					,       __lowerCAmelCase					,       precision="fp32"					,       device="cuda:0" if torch.cuda.is_available() else "cpu"					,       enable_fusion=__lowerCAmelCase					,       fusion_type="aff_2d" if enable_fusion else None					,       )
		return model, model_cfg






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					{}

		_UpperCAmelCase						:				str	    =					R".*sequential.(\d+).*"
		_UpperCAmelCase						:				Any	    =					R".*_projection.(\d+).*"

		for key, value in state_dict.items():
				# check if any key needs to be modified
				for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
						if key_to_modify in key:
								_UpperCAmelCase						:				Union[str, Any]	    =					key.replace(__lowerCAmelCase					,       __lowerCAmelCase      )

				if re.match(__lowerCAmelCase					,       __lowerCAmelCase      ):
						# replace sequential layers with list
						_UpperCAmelCase						:				List[Any]	    =					re.match(__lowerCAmelCase					,       __lowerCAmelCase      ).group(1      )

						_UpperCAmelCase						:				Optional[int]	    =					key.replace(F"""sequential.{sequential_layer}."""					,       F"""layers.{int(__lowerCAmelCase      )//3}.linear."""      )
				elif re.match(__lowerCAmelCase					,       __lowerCAmelCase      ):
						_UpperCAmelCase						:				List[str]	    =					int(re.match(__lowerCAmelCase					,       __lowerCAmelCase      ).group(1      )      )

						# Because in CLAP they use `nn.Sequential`...
						_UpperCAmelCase						:				str	    =					1 if projecton_layer == 0 else 2

						_UpperCAmelCase						:				Tuple	    =					key.replace(F"""_projection.{projecton_layer}."""					,       F"""_projection.linear{transformers_projection_layer}."""      )

				if "audio" and "qkv" in key:
						# split qkv into query key and value
						_UpperCAmelCase						:				List[str]	    =					value
						_UpperCAmelCase						:				Tuple	    =					mixed_qkv.size(0      ) // 3

						_UpperCAmelCase						:				Union[str, Any]	    =					mixed_qkv[:qkv_dim]
						_UpperCAmelCase						:				int	    =					mixed_qkv[qkv_dim : qkv_dim * 2]
						_UpperCAmelCase						:				Optional[int]	    =					mixed_qkv[qkv_dim * 2 :]

						_UpperCAmelCase						:				List[Any]	    =					query_layer
						_UpperCAmelCase						:				int	    =					key_layer
						_UpperCAmelCase						:				Any	    =					value_layer
				else:
						_UpperCAmelCase						:				Dict	    =					value

		return model_state_dict






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					init_clap(__lowerCAmelCase					,       enable_fusion=__lowerCAmelCase      )

		clap_model.eval()
		_UpperCAmelCase						:				List[str]	    =					clap_model.state_dict()
		_UpperCAmelCase						:				str	    =					rename_state_dict(__lowerCAmelCase      )

		_UpperCAmelCase						:				List[Any]	    =					ClapConfig()
		_UpperCAmelCase						:				str	    =					enable_fusion
		_UpperCAmelCase						:				Union[str, Any]	    =					ClapModel(__lowerCAmelCase      )

		# ignore the spectrogram embedding layer
		model.load_state_dict(__lowerCAmelCase					,       strict=__lowerCAmelCase      )

		model.save_pretrained(__lowerCAmelCase      )
		transformers_config.save_pretrained(__lowerCAmelCase      )


if __name__ == "__main__":
						lowerCamelCase__					=   argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
						lowerCamelCase__					=   parser.parse_args()

						convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)



