


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[Any]     ):
							a__      =[
							    '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     ):
							a__    ,     a__      =emb.weight.shape
							a__      =nn.Linear(__a    ,     __a    ,     bias=__a     )
							a__      =emb.weight.data
							return lin_layer






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






def   _lowercase(					__a				:						Optional[Any]    ,     __a				:						Dict    ,     __a				:						Optional[Any]    ,     __a				:						Tuple    ,     __a				:						str = WEIGHTS_NAME     ):
							a__      =[]
							a__      =0
							os.makedirs(__a    ,     exist_ok=__a     )

							for expert in range(__a     ):
														a__      =switch_checkpoint_path + f"""-rank-{expert}.pt"""
														if os.path.isfile(__a     ):
																					a__      =torch.load(__a     )['model']
																					remove_ignore_keys_(__a     )
																					a__      =rename_fairseq_keys(__a    ,     __a     )
																					a__      =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
							a__      =os.path.join(__a    ,     weights_name.replace('.bin'    ,     f"""-{len(__a     )+1:05d}-of-???.bin"""     )     )
							a__      =torch.load(switch_checkpoint_path + '-shared.pt'     )['model']
							remove_ignore_keys_(__a     )
							a__      =rename_fairseq_keys(__a    ,     __a     )
							a__      =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:
														a__      =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
							a__      ={}
							for idx, shard in enumerate(__a     ):
														a__      =weights_name.replace('.bin'    ,     f"""-{idx+1:05d}-of-{len(__a     ):05d}.bin"""     )
														a__      =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:
																					a__      =shard_file

    # Add the metadata
							a__      ={'total_size': total_size}
							a__      ={'metadata': metadata, 'weight_map': weight_map}

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

							return metadata, index


if __name__ == "__main__":
	_lowerCAmelCase:						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.',
	)
	_lowerCAmelCase:						List[Any]   				=       parser.parse_args()
	_lowerCAmelCase				,						_lowerCAmelCase:						Dict   				=       shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    128,
	    args.dtype,
	)

	_lowerCAmelCase:						Union[str, Any]   				=       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)
	_lowerCAmelCase:						str   				=       NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print('Done')
	model.save_pretrained(args.pytorch_dump_folder_path)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase:						str   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='big_bird'





				def __init__(				self       ,  lowercase_=50358       ,  lowercase_=768       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_=3072       ,  lowercase_="gelu_new"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=4096       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=1e-12       ,  lowercase_=True       ,  lowercase_=0       ,  lowercase_=1       ,  lowercase_=2       ,  lowercase_=66       ,  lowercase_="block_sparse"       ,  lowercase_=True       ,  lowercase_=False       ,  lowercase_=64       ,  lowercase_=3       ,  lowercase_=None       ,  **lowercase_       ,  )  -> Any:
											super().__init__(
											    pad_token_id=lowercase_       ,  bos_token_id=lowercase_       ,  eos_token_id=lowercase_       ,  sep_token_id=lowercase_       ,  **lowercase_       ,  )

											a__      =vocab_size
											a__      =max_position_embeddings
											a__      =hidden_size
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =initializer_range
											a__      =type_vocab_size
											a__      =layer_norm_eps
											a__      =use_cache

											a__      =rescale_embeddings
											a__      =attention_type
											a__      =use_bias
											a__      =block_size
											a__      =num_random_blocks
											a__      =classifier_dropout







class       lowercase_					(lowercase__   ):





				@property
				def 						__UpperCamelCase    (				self)  -> Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		a__      ={0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		a__      ={0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ])

