



import argparse
from collections import defaultdict

import yaml


a_   :List[str]        =      "docs/source/en/_toctree.yml"
def        lowercase_							(A : Tuple      ):
       snake_case__							:	Tuple   	=	defaultdict(A      )
       for doc in model_doc:
              counts[doc["local"]] += 1
       snake_case__							:	Optional[Any]   	=	[key for key, value in counts.items() if value > 1]

       snake_case__							:	Dict   	=	[]
       for duplicate_key in duplicates:
              snake_case__							:	List[str]   	=	list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}      )
              if len(A      ) > 1:
                     raise ValueError(
                         F'''{duplicate_key} is present several times in the documentation table of content at '''
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'      )
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}      )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]      )

       # Sort
       return sorted(A		,					key=lambda A      : s["title"].lower()      )
def        lowercase_							(A : Tuple=False      ):
       with open(A		,					encoding='utf-8'      ) as f:
              snake_case__							:	List[Any]   	=	yaml.safe_load(f.read()      )

       # Get to the API doc
       snake_case__							:	Union[str, Any]   	=	0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       snake_case__							:	int   	=	content[api_idx]['sections']

       # Then to the model doc
       snake_case__							:	Any   	=	0
       while api_doc[model_idx]["title"] != "Models":
              model_idx += 1

       snake_case__							:	Dict   	=	api_doc[model_idx]['sections']

       snake_case__							:	int   	=	[(idx, section) for idx, section in enumerate(A      ) if 'sections' in section]
       snake_case__							:	List[str]   	=	False
       for idx, modality_doc in modalities_docs:
              snake_case__							:	int   	=	modality_doc['sections']
              snake_case__							:	List[Any]   	=	clean_model_doc_toc(A      )

              if old_modality_doc != new_modality_doc:
                     snake_case__							:	Any   	=	True
                     if overwrite:
                            snake_case__							:	Union[str, Any]   	=	new_modality_doc

       if diff:
              if overwrite:
                     snake_case__							:	Any   	=	model_doc
                     snake_case__							:	Union[str, Any]   	=	api_doc
                     with open(A		,					'w'		,					encoding='utf-8'      ) as f:
                            f.write(yaml.dump(A		,					allow_unicode=A      )      )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'      )


if __name__ == "__main__":
 a_   :Optional[int]        =      argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 a_   :List[str]        =      parser.parse_args()

 check_model_doc(args.fix_and_overwrite)











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)






