


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 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =[
				    '''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 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =emb.weight.shape
				_SCREAMING_SNAKE_CASE			  =nn.Linear(a__	,a__	,bias=a__)
				_SCREAMING_SNAKE_CASE			  =emb.weight.data
				return lin_layer







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







def 						lowerCamelCase(   a__	,a__	,a__	,a__	,a__ = WEIGHTS_NAME):
				_SCREAMING_SNAKE_CASE			  =[]
				_SCREAMING_SNAKE_CASE			  =0
				os.makedirs(a__	,exist_ok=a__)

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

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

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

				return metadata, index


if __name__ == "__main__":
							snake_case_						:			Dict													=							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.''',
							)
							snake_case_						:			Tuple													=							parser.parse_args()
							snake_case_						,				snake_case_						:			Optional[Any]													=							shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_28,
							    args.dtype,
							)

							snake_case_						:			int													=							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)
							snake_case_						:			int													=							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


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"data2vec-text"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    bos_token_id=_a				,    eos_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =use_cache
										_SCREAMING_SNAKE_CASE			  =classifier_dropout

class 						A__							(		UpperCamelCase__  ):




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]  )