


'''simple docstring'''






import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :int				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str] ):







		'''simple docstring'''
		# Load configuration defined in the metadata file
		with open(lowerCamelCase_ ) as metadata_file:
				snake_case_		:							Tuple			       =						json.load(lowerCamelCase_ )
		snake_case_		:							Any			       =						LukeConfig(use_entity_aware_attention=lowerCamelCase_				,      **metadata["""model_config"""] )

		# Load in the weights from the checkpoint_path
		snake_case_		:							str			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )

		# Load the entity vocab file
		snake_case_		:							Optional[Any]			       =						load_entity_vocab(lowerCamelCase_ )

		snake_case_		:							Union[str, Any]			       =						RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""] )

		# Add special tokens to the token vocabulary for downstream tasks
		snake_case_		:							Optional[Any]			       =						AddedToken("""<ent>"""				,      lstrip=lowerCamelCase_				,      rstrip=lowerCamelCase_ )
		snake_case_		:							str			       =						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_				,      LukeTokenizer.vocab_files_names["""entity_vocab_file"""] )				,      """w""" ) as f:
				json.dump(lowerCamelCase_				,      lowerCamelCase_ )

		snake_case_		:							Dict			       =						LukeTokenizer.from_pretrained(lowerCamelCase_ )

		# Initialize the embeddings of the special tokens
		snake_case_		:							Optional[Any]			       =						state_dict["""embeddings.word_embeddings.weight"""]
		snake_case_		:							Any			       =						word_emb[tokenizer.convert_tokens_to_ids(["""@"""] )[0]].unsqueeze(0 )
		snake_case_		:							Any			       =						word_emb[tokenizer.convert_tokens_to_ids(["""#"""] )[0]].unsqueeze(0 )
		snake_case_		:							Any			       =						torch.cat([word_emb, ent_emb, enta_emb] )

		# 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"]:
						snake_case_		:							Dict			       =						F'''encoder.layer.{layer_index}.attention.self.'''
						snake_case_		:							str			       =						state_dict[prefix + matrix_name]
						snake_case_		:							int			       =						state_dict[prefix + matrix_name]
						snake_case_		:							Tuple			       =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
		snake_case_		:							List[str]			       =						state_dict["""entity_embeddings.entity_embeddings.weight"""]
		snake_case_		:							Tuple			       =						entity_emb[entity_vocab["""[MASK]"""]]

		snake_case_		:							Optional[int]			       =						LukeModel(config=lowerCamelCase_ ).eval()

		snake_case_       ,      snake_case_		:							Tuple			       =						model.load_state_dict(lowerCamelCase_				,      strict=lowerCamelCase_ )
		if not (len(lowerCamelCase_ ) == 1 and missing_keys[0] == "embeddings.position_ids"):
				raise ValueError(F'''Missing keys {", ".join(lowerCamelCase_ )}. Expected only missing embeddings.position_ids''' )
		if not (all(key.startswith("""entity_predictions""" ) or key.startswith("""lm_head""" ) for key in unexpected_keys )):
				raise ValueError(
				    """Unexpected keys"""
				    F''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}''' )

		# Check outputs
		snake_case_		:							Optional[int]			       =						LukeTokenizer.from_pretrained(lowerCamelCase_				,      task="""entity_classification""" )

		snake_case_		:							List[Any]			       =						(
		    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
		    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
		)
		snake_case_		:							List[Any]			       =						(39, 42)
		snake_case_		:							Optional[Any]			       =						tokenizer(lowerCamelCase_				,      entity_spans=[span]				,      add_prefix_space=lowerCamelCase_				,      return_tensors="""pt""" )

		snake_case_		:							List[str]			       =						model(**lowerCamelCase_ )

		# Verify word hidden states
		if model_size == "large":
				snake_case_		:							Dict			       =						torch.Size((1, 42, 10_24) )
				snake_case_		:							Union[str, Any]			       =						torch.tensor(
				    [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]] )
		else:  # base
				snake_case_		:							Any			       =						torch.Size((1, 42, 7_68) )
				snake_case_		:							Any			       =						torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]] )

		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":
				snake_case_		:							List[str]			       =						torch.Size((1, 1, 10_24) )
				snake_case_		:							Optional[Any]			       =						torch.tensor([[0.0_466, -0.0_106, -0.0_179]] )
		else:  # base
				snake_case_		:							Optional[int]			       =						torch.Size((1, 1, 7_68) )
				snake_case_		:							int			       =						torch.tensor([[0.1_457, 0.1_044, 0.0_174]] )

		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

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





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_		:							str			       =						{}
		with open(lowerCamelCase_				,      """r"""				,      encoding="""utf-8""" ) as f:
				for index, line in enumerate(lowerCamelCase_ ):
						snake_case_       ,      snake_case_		:							Any			       =						line.rstrip().split("""\t""" )
						snake_case_		:							Union[str, Any]			       =						index

		return entity_vocab


if __name__ == "__main__":
							__A	:	List[Any]     =     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.'
							)
							__A	:	Tuple     =     parser.parse_args()
							convert_luke_checkpoint(
							    args.checkpoint_path,
							    args.metadata_path,
							    args.entity_vocab_path,
							    args.pytorch_dump_folder_path,
							    args.model_size,
							)



'''simple docstring'''






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      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		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(lowerCamelCase_				,      lowerCamelCase_ )





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_       ,      snake_case_		:							Dict			       =						emb.weight.shape
		snake_case_		:							str			       =						nn.Linear(lowerCamelCase_				,      lowerCamelCase_				,      bias=lowerCamelCase_ )
		snake_case_		:							str			       =						emb.weight.data
		return lin_layer





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str]=None ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						{}
		for old_key in state_dict.keys():
				snake_case_		:							Optional[int]			       =						old_key
				if "moe_layer.experts." in key:
						if expert_idx is not None:
								snake_case_		:							Dict			       =						key.replace("""moe_layer.experts.0"""				,      F'''ffn.experts.expert_{expert_idx}''' )
						else:
								snake_case_		:							str			       =						key.replace("""moe_layer.experts."""				,      """ffn.experts.expert_""" )
				if "gate" in key:
						snake_case_		:							List[str]			       =						key.replace(""".moe_layer.gate.wg"""				,      """.ffn.router.classifier""" )
				if "fc2" and "experts" not in key:
						snake_case_		:							Any			       =						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_		:							str			       =						key.replace(""".encoder_attn."""				,      """.cross_attention.""" )
				if "encoder_attn_layer_norm" in key:
						snake_case_		:							List[str]			       =						key.replace("""encoder_attn_layer_norm"""				,      """cross_attention_layer_norm""" )
				if "final_layer_norm" in key:
						snake_case_		:							Dict			       =						key.replace("""final_layer_norm"""				,      """ff_layer_norm""" )
				snake_case_		:							Dict			       =						state_dict[old_key]
		return new_dict





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :str = WEIGHTS_NAME ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[]
		snake_case_		:							Dict			       =						0
		os.makedirs(lowerCamelCase_				,      exist_ok=lowerCamelCase_ )

		for expert in range(lowerCamelCase_ ):
				snake_case_		:							Optional[Any]			       =						switch_checkpoint_path + F'''-rank-{expert}.pt'''
				if os.path.isfile(lowerCamelCase_ ):
						snake_case_		:							List[Any]			       =						torch.load(lowerCamelCase_ )["""model"""]
						remove_ignore_keys_(lowerCamelCase_ )
						snake_case_		:							List[str]			       =						rename_fairseq_keys(lowerCamelCase_				,      lowerCamelCase_ )
						snake_case_		:							List[str]			       =						os.path.join(
						    lowerCamelCase_				,      weights_name.replace(""".bin"""				,      F'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
						torch.save(lowerCamelCase_				,      lowerCamelCase_ )
						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(lowerCamelCase_ )[0]].dtype )

    # Add the last block
		snake_case_		:							Tuple			       =						os.path.join(lowerCamelCase_				,      weights_name.replace(""".bin"""				,      F'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
		snake_case_		:							Tuple			       =						torch.load(switch_checkpoint_path + """-shared.pt""" )["""model"""]
		remove_ignore_keys_(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						rename_fairseq_keys(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							List[str]			       =						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(lowerCamelCase_ ) == 1:
				snake_case_		:							Union[str, Any]			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
				torch.save(lowerCamelCase_				,      lowerCamelCase_ )
				return {weights_name: sharded_state_dicts[0]}, None
		else:
				torch.save(lowerCamelCase_				,      lowerCamelCase_ )
		# Otherwise, let's build the index
		snake_case_		:							str			       =						{}
		for idx, shard in enumerate(lowerCamelCase_ ):
				snake_case_		:							List[str]			       =						weights_name.replace(""".bin"""				,      F'''-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin''' )
				snake_case_		:							Optional[int]			       =						os.path.join(lowerCamelCase_				,      weights_name.replace(""".bin"""				,      F'''-{idx+1:05d}-of-???.bin''' ) )
				os.rename(lowerCamelCase_				,      os.path.join(lowerCamelCase_				,      lowerCamelCase_ ) )
				for key in shard:
						snake_case_		:							Optional[int]			       =						shard_file

    # Add the metadata
		snake_case_		:							Any			       =						{"""total_size""": total_size}
		snake_case_		:							int			       =						{"""metadata""": metadata, """weight_map""": weight_map}

		with open(os.path.join(lowerCamelCase_				,      lowerCamelCase_ )				,      """w"""				,      encoding="""utf-8""" ) as f:
				snake_case_		:							List[str]			       =						json.dumps(lowerCamelCase_				,      indent=2				,      sort_keys=lowerCamelCase_ ) + """\n"""
				f.write(lowerCamelCase_ )

		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	:	List[str]     =     parser.parse_args()
							__A,   __A	:	List[Any]     =     shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    128,
							    args.dtype,
							)

							__A	:	Tuple     =     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	:	List[str]     =     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('Done')
							model.save_pretrained(args.pytorch_dump_folder_path)