
"""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  lowerCamelCase__						(     __snake_case      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    [
					    '''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(__snake_case,	__snake_case      )



def  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase					,    _UpperCamelCase									=    emb.weight.shape
					_UpperCamelCase									=    nn.Linear(__snake_case,	__snake_case,	bias=__snake_case      )
					_UpperCamelCase									=    emb.weight.data
					return lin_layer



def  lowerCamelCase__						(     __snake_case,	__snake_case=None      )				->						Optional[Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case = WEIGHTS_NAME      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []
					_UpperCamelCase									=    0
					os.makedirs(__snake_case,	exist_ok=__snake_case      )

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

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

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

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

					return metadata, index


if __name__ == "__main__":
				_a								=						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								=						parser.parse_args()
				_a				,				_a								=						shard_on_the_fly(
				    args.nllb_moe_checkpoint_path,
				    args.pytorch_dump_folder_path,
				    128,
				    args.dtype,
				)

				_a								=						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								=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
				print("""Done""")
				model.save_pretrained(args.pytorch_dump_folder_path)








"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					if (
					    (cp >= 0x4E00 and cp <= 0x9FFF)
					    or (cp >= 0x3400 and cp <= 0x4DBF)  #
					    or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
					    or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
					    or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
					    or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
					    or (cp >= 0xF900 and cp <= 0xFAFF)
					    or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
					):  #
										return True

					return False



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					for char in word:
										_UpperCamelCase									=    ord(__snake_case      )
										if not _is_chinese_char(__snake_case      ):
															return 0
					return 1



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    set()

					for token in tokens:
										_UpperCamelCase									=    len(__snake_case      ) > 1 and is_chinese(__snake_case      )
										if chinese_word:
															word_set.add(__snake_case      )
					_UpperCamelCase									=    list(__snake_case      )
					return word_list



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					if not chinese_word_set:
										return bert_tokens
					_UpperCamelCase									=    max([len(__snake_case      ) for w in chinese_word_set]      )

					_UpperCamelCase									=    bert_tokens
					_UpperCamelCase					,    _UpperCamelCase									=    0, len(__snake_case      )
					while start < end:
										_UpperCamelCase									=    True
										if is_chinese(bert_word[start]      ):
															_UpperCamelCase									=    min(end - start,	__snake_case      )
															for i in range(__snake_case,	1,	-1      ):
																				_UpperCamelCase									=    ''''''.join(bert_word[start : start + i]      )
																				if whole_word in chinese_word_set:
																									for j in range(start + 1,	start + i      ):
																														_UpperCamelCase									=    '''##''' + bert_word[j]
																									_UpperCamelCase									=    start + i
																									_UpperCamelCase									=    False
																									break
										if single_word:
															start += 1
					return bert_word



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    ltp_tokenizer.pipeline(lines[i : i + 1_00],	tasks=['''cws''']      ).cws
										_UpperCamelCase									=    [get_chinese_word(__snake_case      ) for r in res]
										ltp_res.extend(__snake_case      )
					assert len(__snake_case      ) == len(__snake_case      )

					_UpperCamelCase									=    []
					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    bert_tokenizer(lines[i : i + 1_00],	add_special_tokens=__snake_case,	truncation=__snake_case,	max_length=5_12      )
										bert_res.extend(res['''input_ids''']      )
					assert len(__snake_case      ) == len(__snake_case      )

					_UpperCamelCase									=    []
					for input_ids, chinese_word in zip(__snake_case,	__snake_case      ):
										_UpperCamelCase									=    []
										for id in input_ids:
															_UpperCamelCase									=    bert_tokenizer._convert_id_to_token(__snake_case      )
															input_tokens.append(__snake_case      )
										_UpperCamelCase									=    add_sub_symbol(__snake_case,	__snake_case      )
										_UpperCamelCase									=    []
										# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
										for i, token in enumerate(__snake_case      ):
															if token[:2] == "##":
																				_UpperCamelCase									=    token[2:]
																				# save chinese tokens' pos
																				if len(__snake_case      ) == 1 and _is_chinese_char(ord(__snake_case      )      ):
																									ref_id.append(__snake_case      )
										ref_ids.append(__snake_case      )

					assert len(__snake_case      ) == len(__snake_case      )

					return ref_ids



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					with open(args.file_name,	'''r''',	encoding='''utf-8'''      ) as f:
										_UpperCamelCase									=    f.readlines()
					_UpperCamelCase									=    [line.strip() for line in data if len(__snake_case      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
					_UpperCamelCase									=    LTP(args.ltp      )  # faster in GPU device
					_UpperCamelCase									=    BertTokenizer.from_pretrained(args.bert      )

					_UpperCamelCase									=    prepare_ref(__snake_case,	__snake_case,	__snake_case      )

					with open(args.save_path,	'''w''',	encoding='''utf-8'''      ) as f:
										_UpperCamelCase									=    [json.dumps(__snake_case      ) + '''\n''' for ref in ref_ids]
										f.writelines(__snake_case      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser(description="""prepare_chinese_ref""")
				parser.add_argument(
				    """--file_name""",
				    required=False,
				    type=str,
				    default="""./resources/chinese-demo.txt""",
				    help="""file need process, same as training data in lm""",
				)
				parser.add_argument(
				    """--ltp""",
				    required=False,
				    type=str,
				    default="""./resources/ltp""",
				    help="""resources for LTP tokenizer, usually a path""",
				)
				parser.add_argument(
				    """--bert""",
				    required=False,
				    type=str,
				    default="""./resources/robert""",
				    help="""resources for Bert tokenizer""",
				)
				parser.add_argument(
				    """--save_path""",
				    required=False,
				    type=str,
				    default="""./resources/ref.txt""",
				    help="""path to save res""",
				)

				_a								=						parser.parse_args()
				main(args)






