

'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]						)				->  Union[str, Any]:
						# This defines a "chinese character" as anything in the CJK Unicode block:
						#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
						#
						# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
						# despite its name. The modern Korean Hangul alphabet is a different block,
						# as is Japanese Hiragana and Katakana. Those alphabets are used to write
						# space-separated words, so they are not treated specially and handled
						# like the all of the other languages.
						if (
						    (cp >= 0x4e_00 and cp <= 0x9f_ff)
						    or (cp >= 0x34_00 and cp <= 0x4d_bf)  #
						    or (cp >= 0x2_00_00 and cp <= 0x2_a6_df)  #
						    or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f)  #
						    or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f)  #
						    or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af)  #
						    or (cp >= 0xf9_00 and cp <= 0xfa_ff)
						    or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f)  #
						):  #
												return True

						return False






def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  Optional[int]:
						# word like '180' or '身高' or '神'
						for char in word:
												lowercase :							Optional[int]    		=ord(__magic_name__						)
												if not _is_chinese_char(__magic_name__						):
																		return 0
						return 1






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]						)				->  List[str]:
						lowercase :							str    		=set()

						for token in tokens:
												lowercase :							Optional[int]    		=len(__magic_name__						) > 1 and is_chinese(__magic_name__						)
												if chinese_word:
																		word_set.add(__magic_name__						)
						lowercase :							str    		=list(__magic_name__						)
						return word_list






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     set()						)				->  Optional[int]:
						if not chinese_word_set:
												return bert_tokens
						lowercase :							Optional[Any]    		=max([len(__magic_name__						) for w in chinese_word_set]						)

						lowercase :							Optional[int]    		=bert_tokens
						lowercase		,	lowercase :							Dict    		=0, len(__magic_name__						)
						while start < end:
												lowercase :							List[Any]    		=True
												if is_chinese(bert_word[start]						):
																		lowercase :							Dict    		=min(end - start   , __magic_name__						)
																		for i in range(__magic_name__   , 1   , -1						):
																								lowercase :							int    		=''''''.join(bert_word[start : start + i]						)
																								if whole_word in chinese_word_set:
																														for j in range(start + 1   , start + i						):
																																				lowercase :							Optional[Any]    		='''##''' + bert_word[j]
																														lowercase :							List[str]    		=start + i
																														lowercase :							Optional[Any]    		=False
																														break
												if single_word:
																		start += 1
						return bert_word






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     LTP   , __magic_name__						:     BertTokenizer						)				->  Dict:
						lowercase :							List[Any]    		=[]

						for i in range(0   , len(__magic_name__						)   , 100						):
												lowercase :							Optional[Any]    		=ltp_tokenizer.seg(lines[i : i + 100]						)[0]
												lowercase :							Any    		=[get_chinese_word(__magic_name__						) for r in res]
												ltp_res.extend(__magic_name__						)
						assert len(__magic_name__						) == len(__magic_name__						)

						lowercase :							Union[str, Any]    		=[]
						for i in range(0   , len(__magic_name__						)   , 100						):
												lowercase :							Union[str, Any]    		=bert_tokenizer(lines[i : i + 100]   , add_special_tokens=__magic_name__   , truncation=__magic_name__   , max_length=512						)
												bert_res.extend(res['''input_ids''']						)
						assert len(__magic_name__						) == len(__magic_name__						)

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

						assert len(__magic_name__						) == len(__magic_name__						)

						return ref_ids






def 				_lowerCAmelCase			(   __magic_name__						:     Tuple						)				->  Dict:
						# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
						# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
						with open(args.file_name   , '''r'''   , encoding='''utf-8'''						) as f:
												lowercase :							List[Any]    		=f.readlines()
						lowercase :							int    		=[line.strip() for line in data if len(__magic_name__						) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						lowercase :							List[Any]    		=LTP(args.ltp						)  # faster in GPU device
						lowercase :							List[str]    		=BertTokenizer.from_pretrained(args.bert						)

						lowercase :							Tuple    		=prepare_ref(__magic_name__   , __magic_name__   , __magic_name__						)

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


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

							UpperCamelCase_    						=       parser.parse_args()
							main(args)








'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCamelCase_    						=       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 				_lowerCAmelCase			(   __magic_name__						:     Any   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Tuple						)				->  str:
						for attribute in key.split('''.'''						):
												lowercase :							Tuple    		=getattr(__magic_name__   , __magic_name__						)

						if weight_type is not None:
												lowercase :							Optional[int]    		=getattr(__magic_name__   , __magic_name__						).shape
						else:
												lowercase :							List[Any]    		=hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
												    f''' {value.shape} for {full_name}'''						)

						if weight_type == "weight":
												lowercase :							Any    		=value
						elif weight_type == "weight_g":
												lowercase :							List[Any]    		=value
						elif weight_type == "weight_v":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "bias":
												lowercase :							Tuple    		=value
						elif weight_type == "running_mean":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "running_var":
												lowercase :							str    		=value
						elif weight_type == "num_batches_tracked":
												lowercase :							Tuple    		=value
						elif weight_type == "inv_freq":
												lowercase :							Optional[Any]    		=value
						else:
												lowercase :							Tuple    		=value

						logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''						)






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     str						)				->  Union[str, Any]:
						lowercase :							Optional[int]    		=[]
						lowercase :							Tuple    		=fairseq_model.state_dict()

						lowercase :							List[Any]    		=hf_model.wavaveca_conformer.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase :							Tuple    		=False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , hf_model.config.feat_extract_norm == '''group'''   , )
																		lowercase :							List[Any]    		=True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase :							Optional[int]    		='''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																								if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
																														lowercase :							Union[str, Any]    		=True
																														if "*" in mapped_key:
																																				lowercase :							Optional[int]    		=name.split(__magic_name__						)[0].split('''.'''						)[-2]
																																				lowercase :							List[str]    		=mapped_key.replace('''*'''   , __magic_name__						)
																														if "pos_bias_u" in name:
																																				lowercase :							Optional[Any]    		=None
																														elif "pos_bias_v" in name:
																																				lowercase :							Union[str, Any]    		=None
																														elif "weight_g" in name:
																																				lowercase :							Any    		='''weight_g'''
																														elif "weight_v" in name:
																																				lowercase :							Tuple    		='''weight_v'''
																														elif "bias" in name:
																																				lowercase :							Optional[int]    		='''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowercase :							Optional[int]    		='''weight'''
																														elif "running_mean" in name:
																																				lowercase :							Union[str, Any]    		='''running_mean'''
																														elif "inv_freq" in name:
																																				lowercase :							Any    		='''inv_freq'''
																														elif "running_var" in name:
																																				lowercase :							Tuple    		='''running_var'''
																														elif "num_batches_tracked" in name:
																																				lowercase :							Dict    		='''num_batches_tracked'''
																														else:
																																				lowercase :							str    		=None
																														set_recursively(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__						)
																								continue
												if not is_used:
																		unused_weights.append(__magic_name__						)

						logger.warning(f'''Unused weights: {unused_weights}'''						)






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     List[Any]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple						)				->  int:
						lowercase :							Optional[Any]    		=full_name.split('''conv_layers.'''						)[-1]
						lowercase :							Any    		=name.split('''.'''						)
						lowercase :							List[str]    		=int(items[0]						)
						lowercase :							Union[str, Any]    		=int(items[1]						)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''						)
																		lowercase :							Union[str, Any]    		=value
																		logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''						)
																		lowercase :							Optional[Any]    		=value
																		logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
																		lowercase :							Optional[int]    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
																		lowercase :							str    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
						else:
												unused_weights.append(__magic_name__						)






@torch.no_grad()
def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple=None   , __magic_name__						:     Optional[Any]=None   , __magic_name__						:     Union[str, Any]=True						)				->  Union[str, Any]:
						if config_path is not None:
												lowercase :							Optional[Any]    		=WavaVecaConformerConfig.from_pretrained(__magic_name__   , hidden_act='''swish'''						)
						else:
												lowercase :							Optional[int]    		=WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												lowercase :							Dict    		='''rotary'''

						if is_finetuned:
												if dict_path:
																		lowercase :							Optional[Any]    		=Dictionary.load(__magic_name__						)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowercase :							str    		=target_dict.pad_index
																		lowercase :							Union[str, Any]    		=target_dict.bos_index
																		lowercase :							Any    		=target_dict.eos_index
																		lowercase :							Tuple    		=len(target_dict.symbols						)
																		lowercase :							str    		=os.path.join(__magic_name__   , '''vocab.json'''						)
																		if not os.path.isdir(__magic_name__						):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__magic_name__						)						)
																								return
																		os.makedirs(__magic_name__   , exist_ok=__magic_name__						)
																		lowercase :							Dict    		=target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		lowercase :							str    		=0
																		lowercase :							List[Any]    		=1
																		with open(__magic_name__   , '''w'''   , encoding='''utf-8'''						) as vocab_handle:
																								json.dump(__magic_name__   , __magic_name__						)
																		lowercase :							List[str]    		=WavaVecaCTCTokenizer(
																		    __magic_name__   , unk_token=target_dict.unk_word   , pad_token=target_dict.pad_word   , bos_token=target_dict.bos_word   , eos_token=target_dict.eos_word   , word_delimiter_token='''|'''   , do_lower_case=__magic_name__   , )
																		lowercase :							Optional[int]    		=True if config.feat_extract_norm == '''layer''' else False
																		lowercase :							str    		=WavaVecaFeatureExtractor(
																		    feature_size=1   , sampling_rate=16000   , padding_value=0   , do_normalize=__magic_name__   , return_attention_mask=__magic_name__   , )
																		lowercase :							Tuple    		=WavaVecaProcessor(feature_extractor=__magic_name__   , tokenizer=__magic_name__						)
																		processor.save_pretrained(__magic_name__						)

												lowercase :							str    		=WavaVecaConformerForCTC(__magic_name__						)
						else:
												lowercase :							Tuple    		=WavaVecaConformerForPreTraining(__magic_name__						)

						if is_finetuned:
												lowercase		,	lowercase		,	lowercase :							Optional[int]    		=fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]   , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
						else:
												lowercase :							Dict    		=argparse.Namespace(task='''audio_pretraining'''						)
												lowercase :							Optional[int]    		=fairseq.tasks.setup_task(__magic_name__						)

												lowercase		,	lowercase		,	lowercase :							str    		=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   , task=__magic_name__						)

						lowercase :							List[Any]    		=model[0].eval()

						recursively_load_weights(__magic_name__   , __magic_name__   , not is_finetuned						)

						hf_wavavec.save_pretrained(__magic_name__						)


if __name__ == "__main__":
							UpperCamelCase_    						=       argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							UpperCamelCase_    						=       parser.parse_args()
							convert_wavaveca_conformer_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)





