



'''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  snake_case_     (					_lowerCAmelCase  :						List[Any]  )      ->   Union[str, Any]:
							UpperCAmelCase :       int 							=		[
							    '''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  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   Tuple:
							UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		emb.weight.shape
							UpperCAmelCase :       Union[str, Any] 							=		nn.Linear(_lowerCAmelCase   ,      _lowerCAmelCase   ,      bias=_lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		emb.weight.data
							return lin_layer


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


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						str = WEIGHTS_NAME  )      ->   Optional[int]:
							UpperCAmelCase :       Optional[int] 							=		[]
							UpperCAmelCase :       Any 							=		0
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )

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

    # Add the metadata
							UpperCAmelCase :       List[str] 							=		{'''total_size''': total_size}
							UpperCAmelCase :       List[str] 							=		{'''metadata''': metadata, '''weight_map''': weight_map}

							with open(os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )   ,      '''w'''   ,      encoding='''utf-8'''  ) as f:
														UpperCAmelCase :       List[Any] 							=		json.dumps(_lowerCAmelCase   ,      indent=2   ,      sort_keys=_lowerCAmelCase  ) + '''\n'''
														f.write(_lowerCAmelCase  )

							return metadata, index


if __name__ == "__main__":
						UpperCamelCase__:   Tuple								=			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.",
						)
						UpperCamelCase__:   List[Any]								=			parser.parse_args()
						UpperCamelCase__	,					UpperCamelCase__:   Optional[Any]								=			shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						UpperCamelCase__:   Optional[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)
						UpperCamelCase__:   str								=			NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)







"""simple docstring"""



# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

UpperCAmelCase__          = 'tiny-wmt19-en-ru'

# Build

# borrowed from a test
UpperCAmelCase__          = [
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
UpperCAmelCase__          = dict(zip(vocab, range(len(vocab))))
UpperCAmelCase__          = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
 UpperCAmelCase__          = Path(tmpdirname)
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['merges_file']
 with open(src_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, 'w') as fp:
  fp.write('\n'.join(merges))

 UpperCAmelCase__          = FSMTTokenizer(
     langs=['en', 'ru'],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

UpperCAmelCase__          = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

UpperCAmelCase__          = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
UpperCAmelCase__          = tokenizer(['Making tiny model'], return_tensors='pt')
UpperCAmelCase__          = tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru



