



import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


a_   :List[Any]        =      logging.get_logger(__name__)
def        lowercase_							(A : Tuple=None		,					A : Tuple=None      ):
       return field(default_factory=lambda: default		,					metadata=A      )





@dataclass
class 					snake_case__						:






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	list_field(
         default=[]				,     metadata={
             """help""": (
                 """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                 """ of all available models"""
             )
         }				,     )

     _SCREAMING_SNAKE_CASE						=	list_field(
         default=[8]				,     metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}     )

     _SCREAMING_SNAKE_CASE						=	list_field(
         default=[8, 32, 128, 512]				,     metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}				,     )

     _SCREAMING_SNAKE_CASE						=	field(
         default=lowerCAmelCase_				,     metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}				,     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=lowerCAmelCase_				,     metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}				,     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=lowerCAmelCase_				,     metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}     )
     _SCREAMING_SNAKE_CASE						=	field(default=lowerCAmelCase_				,     metadata={"""help""": """Use FP16 to accelerate inference."""}     )
     _SCREAMING_SNAKE_CASE						=	field(default=lowerCAmelCase_				,     metadata={"""help""": """Benchmark training of model"""}     )
     _SCREAMING_SNAKE_CASE						=	field(default=lowerCAmelCase_				,     metadata={"""help""": """Verbose memory tracing"""}     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=lowerCAmelCase_				,     metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}				,     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=lowerCAmelCase_				,     metadata={
             """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
         }				,     )
     _SCREAMING_SNAKE_CASE						=	field(default=lowerCAmelCase_				,     metadata={"""help""": """Trace memory line by line"""}     )
     _SCREAMING_SNAKE_CASE						=	field(default=lowerCAmelCase_				,     metadata={"""help""": """Save result to a CSV file"""}     )
     _SCREAMING_SNAKE_CASE						=	field(default=lowerCAmelCase_				,     metadata={"""help""": """Save all print statements in a log file"""}     )
     _SCREAMING_SNAKE_CASE						=	field(default=lowerCAmelCase_				,     metadata={"""help""": """Whether to print environment information"""}     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=lowerCAmelCase_				,     metadata={
             """help""": (
                 """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                 """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                 """ for debugging / testing and on TPU."""
             )
         }				,     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=F"""inference_time_{round(time()     )}.csv"""				,     metadata={"""help""": """CSV filename used if saving time results to csv."""}				,     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=F"""inference_memory_{round(time()     )}.csv"""				,     metadata={"""help""": """CSV filename used if saving memory results to csv."""}				,     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=F"""train_time_{round(time()     )}.csv"""				,     metadata={"""help""": """CSV filename used if saving time results to csv for training."""}				,     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=F"""train_memory_{round(time()     )}.csv"""				,     metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}				,     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=F"""env_info_{round(time()     )}.csv"""				,     metadata={"""help""": """CSV filename used if saving environment information."""}				,     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=F"""log_{round(time()     )}.csv"""				,     metadata={"""help""": """Log filename used if print statements are saved in log."""}				,     )
     _SCREAMING_SNAKE_CASE						=	field(default=3				,     metadata={"""help""": """Times an experiment will be run."""}     )
     _SCREAMING_SNAKE_CASE						=	field(
         default=lowerCAmelCase_				,     metadata={
             """help""": (
                 """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                 """ model weights."""
             )
         }				,     )






     def    lowercase_   (       self  :  Optional[Any]					)   ->List[str]:
            warnings.warn(
                F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
                ' are deprecated in general and it is advised to use external Benchmarking libraries '
                ' to benchmark Transformer models.', _snake_case, )






     def    lowercase_   (       self  :  Tuple					)   ->int:
            return json.dumps(dataclasses.asdict(self					), indent=2					)






     @property
     def    lowercase_   (       self  :  Optional[Any]					)   ->List[str]:
            if len(self.models					) <= 0:
                   raise ValueError(
                       'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
                       ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'					)
            return self.models






     @property
     def    lowercase_   (       self  :  Union[str, Any]					)   ->Tuple:
            if not self.multi_process:
                   return False
            elif self.is_tpu:
                   logger.info('Multiprocessing is currently not possible on TPU.'					)
                   return False
            else:
                   return True











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        lowercase_							(A : List[str]      ):
       snake_case__							:	Tuple   	=	[
           '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        lowercase_							(A : str      ):
       snake_case__	,				snake_case__							:	Union[str, Any]   	=	emb.weight.shape
       snake_case__							:	str   	=	nn.Linear(A		,					A		,					bias=A      )
       snake_case__							:	str   	=	emb.weight.data
       return lin_layer
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]=None      ):
       snake_case__							:	Any   	=	{}
       for old_key in state_dict.keys():
              snake_case__							:	Tuple   	=	old_key
              if "moe_layer.experts." in key:
                     if expert_idx is not None:
                            snake_case__							:	int   	=	key.replace('moe_layer.experts.0'		,					F'''ffn.experts.expert_{expert_idx}'''      )
                     else:
                            snake_case__							:	Any   	=	key.replace('moe_layer.experts.'		,					'ffn.experts.expert_'      )
              if "gate" in key:
                     snake_case__							:	Dict   	=	key.replace('.moe_layer.gate.wg'		,					'.ffn.router.classifier'      )
              if "fc2" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc2.'		,					'.ffn.fc2.'      )
              if "fc1" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc1.'		,					'.ffn.fc1.'      )
              if ".encoder_attn." in key:
                     snake_case__							:	Tuple   	=	key.replace('.encoder_attn.'		,					'.cross_attention.'      )
              if "encoder_attn_layer_norm" in key:
                     snake_case__							:	Tuple   	=	key.replace('encoder_attn_layer_norm'		,					'cross_attention_layer_norm'      )
              if "final_layer_norm" in key:
                     snake_case__							:	Optional[int]   	=	key.replace('final_layer_norm'		,					'ff_layer_norm'      )
              snake_case__							:	Dict   	=	state_dict[old_key]
       return new_dict
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : List[Any]		,					A : List[str]		,					A : str = WEIGHTS_NAME      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	str   	=	0
       os.makedirs(A		,					exist_ok=A      )

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

    # Add the metadata
       snake_case__							:	int   	=	{'total_size': total_size}
       snake_case__							:	Dict   	=	{'metadata': metadata, 'weight_map': weight_map}

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

       return metadata, index


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

 a_   :List[str]        =      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_   :int        =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("Done")
 model.save_pretrained(args.pytorch_dump_folder_path)






