


"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Optional[Any]			) ->      Optional[Any]:
		# Load configuration defined in the metadata file
		with open(lowerCAmelCase__			) as metadata_file:
				__a           =			json.load(lowerCAmelCase__			)
		__a           =			LukeConfig(use_entity_aware_attention=lowerCAmelCase__    ,					**metadata['''model_config''']			)

		# Load in the weights from the checkpoint_path
		__a           =			torch.load(lowerCAmelCase__    ,					map_location='''cpu'''			)['module']

		# Load the entity vocab file
		__a           =			load_original_entity_vocab(lowerCAmelCase__			)
		# add an entry for [MASK2]
		__a           =			max(entity_vocab.values()			) + 1
		config.entity_vocab_size += 1

		__a           =			XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']			)

		# Add special tokens to the token vocabulary for downstream tasks
		__a           =			AddedToken('''<ent>'''    ,					lstrip=lowerCAmelCase__    ,					rstrip=lowerCAmelCase__			)
		__a           =			AddedToken('''<ent2>'''    ,					lstrip=lowerCAmelCase__    ,					rstrip=lowerCAmelCase__			)
		tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}			)
		config.vocab_size += 2

		print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''			)
		tokenizer.save_pretrained(lowerCAmelCase__			)
		with open(os.path.join(lowerCAmelCase__    ,					'''tokenizer_config.json'''			)    ,					'''r'''			) as f:
				__a           =			json.load(lowerCAmelCase__			)
		__a           =			'MLukeTokenizer'
		with open(os.path.join(lowerCAmelCase__    ,					'''tokenizer_config.json'''			)    ,					'''w'''			) as f:
				json.dump(lowerCAmelCase__    ,					lowerCAmelCase__			)

		with open(os.path.join(lowerCAmelCase__    ,					MLukeTokenizer.vocab_files_names['''entity_vocab_file''']			)    ,					'''w'''			) as f:
				json.dump(lowerCAmelCase__    ,					lowerCAmelCase__			)

		__a           =			MLukeTokenizer.from_pretrained(lowerCAmelCase__			)

		# Initialize the embeddings of the special tokens
		__a           =			tokenizer.convert_tokens_to_ids(['''@''']			)[0]
		__a           =			tokenizer.convert_tokens_to_ids(['''#''']			)[0]

		__a           =			state_dict['embeddings.word_embeddings.weight']
		__a           =			word_emb[ent_init_index].unsqueeze(0			)
		__a           =			word_emb[enta_init_index].unsqueeze(0			)
		__a           =			torch.cat([word_emb, ent_emb, enta_emb]			)
		# add special tokens for 'entity_predictions.bias'
		for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
				__a           =			state_dict[bias_name]
				__a           =			decoder_bias[ent_init_index].unsqueeze(0			)
				__a           =			decoder_bias[enta_init_index].unsqueeze(0			)
				__a           =			torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]			)

		# Initialize the query layers of the entity-aware self-attention mechanism
		for layer_index in range(config.num_hidden_layers			):
				for matrix_name in ["query.weight", "query.bias"]:
						__a           =			f'''encoder.layer.{layer_index}.attention.self.'''
						__a           =			state_dict[prefix + matrix_name]
						__a           =			state_dict[prefix + matrix_name]
						__a           =			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
		__a           =			state_dict['entity_embeddings.entity_embeddings.weight']
		__a           =			entity_emb[entity_vocab['[MASK]']].unsqueeze(0			)
		__a           =			torch.cat([entity_emb, entity_mask_emb]			)
		# add [MASK2] for 'entity_predictions.bias'
		__a           =			state_dict['entity_predictions.bias']
		__a           =			entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0			)
		__a           =			torch.cat([entity_prediction_bias, entity_mask_bias]			)

		__a           =			LukeForMaskedLM(config=lowerCAmelCase__			).eval()

		state_dict.pop('''entity_predictions.decoder.weight'''			)
		state_dict.pop('''lm_head.decoder.weight'''			)
		state_dict.pop('''lm_head.decoder.bias'''			)
		__a           =			OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith('''lm_head'''			) or key.startswith('''entity_predictions'''			)):
						__a           =			state_dict[key]
				else:
						__a           =			state_dict[key]

		__a           =			model.load_state_dict(lowerCAmelCase__    ,					strict=lowerCAmelCase__			)

		if set(lowerCAmelCase__			) != {"luke.embeddings.position_ids"}:
				raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''			)
		if set(lowerCAmelCase__			) != {
		    "lm_head.decoder.weight",
		    "lm_head.decoder.bias",
		    "entity_predictions.decoder.weight",
		}:
				raise ValueError(f'''Unexpected missing_keys: {missing_keys}'''			)

		model.tie_weights()
		assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
		assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

		# Check outputs
		__a           =			MLukeTokenizer.from_pretrained(lowerCAmelCase__    ,					task='''entity_classification'''			)

		__a           =			'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
		__a           =			(0, 9)
		__a           =			tokenizer(lowerCAmelCase__    ,					entity_spans=[span]    ,					return_tensors='''pt'''			)

		__a           =			model(**lowerCAmelCase__			)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__a           =			torch.Size((1, 33, 768)			)
				__a           =			torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]			)

		if not (outputs.last_hidden_state.shape == expected_shape):
				raise ValueError(
				    f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''			)
		if not torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,					lowerCAmelCase__    ,					atol=1e-4			):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__a           =			torch.Size((1, 1, 768)			)
				__a           =			torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]			)

		if not (outputs.entity_last_hidden_state.shape == expected_shape):
				raise ValueError(
				    f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
				    f''' {expected_shape}'''			)
		if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]    ,					lowerCAmelCase__    ,					atol=1e-4			):
				raise ValueError

		# Verify masked word/entity prediction
		__a           =			MLukeTokenizer.from_pretrained(lowerCAmelCase__			)
		__a           =			'Tokyo is the capital of <mask>.'
		__a           =			(24, 30)
		__a           =			tokenizer(lowerCAmelCase__    ,					entity_spans=[span]    ,					return_tensors='''pt'''			)

		__a           =			model(**lowerCAmelCase__			)

		__a           =			encoding['input_ids'][0].tolist()
		__a           =			input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''			)			)
		__a           =			outputs.logits[0][mask_position_id].argmax(dim=-1			)
		assert "Japan" == tokenizer.decode(lowerCAmelCase__			)

		__a           =			outputs.entity_logits[0][0].argmax().item()
		__a           =			[
		    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
		]
		assert [e for e in multilingual_predicted_entities if e.startswith('''en:'''			)][0] == "en:Japan"

		# Finally, save our PyTorch model and tokenizer
		print('''Saving PyTorch model to {}'''.format(lowerCAmelCase__			)			)
		model.save_pretrained(lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      Tuple:
		__a           =			['[MASK]', '[PAD]', '[UNK]']

		__a           =			[json.loads(lowerCAmelCase__			) for line in open(lowerCAmelCase__			)]

		__a           =			{}
		for entry in data:
				__a           =			entry['id']
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								__a           =			entity_id
								break
						__a           =			f'''{language}:{entity_name}'''
						__a           =			entity_id
		return new_mapping


if __name__ == "__main__":
				lowercase_			     =  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
				parser.add_argument(
				    "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
				)
				parser.add_argument(
				    "--entity_vocab_path",
				    default=None,
				    type=str,
				    help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
				)
				parser.add_argument(
				    "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
				)
				lowercase_			     =  parser.parse_args()
				convert_luke_checkpoint(
				    args.checkpoint_path,
				    args.metadata_path,
				    args.entity_vocab_path,
				    args.pytorch_dump_folder_path,
				    args.model_size,
				)











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)






