
"""simple docstring"""


import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


lowerCAmelCase								=		logging.get_logger(__name__)

class        A_      (	A__  ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_       =  """AutoTokenizer"""
							SCREAMING_SNAKE_CASE_       =  ["""tokenizer"""]

							SCREAMING_SNAKE_CASE_       =  {
							    """semantic_prompt""": 1,
							    """coarse_prompt""": 2,
							    """fine_prompt""": 2,
							}





							def __init__(    self    :Dict	,				lowerCamelCase_    :List[str]	,				lowerCamelCase_    :Optional[int]=None			):





										"""simple docstring"""

										super().__init__(a_			)

										lowerCamelCase__      :	str						=speaker_embeddings





							@classmethod
							def  UpperCAmelCase__				(    cls    :Any	,				lowerCamelCase_    :List[Any]	,				lowerCamelCase_    :List[Any]="speaker_embeddings_path.json"	,				**lowerCamelCase_    :List[str]			):





										"""simple docstring"""

										if speaker_embeddings_dict_path is not None:
													lowerCamelCase__      :	Optional[int]						=get_file_from_repo(
													    a_	,				a_	,				subfolder=kwargs.pop('subfolder'	,				a_			)	,				cache_dir=kwargs.pop('cache_dir'	,				a_			)	,				force_download=kwargs.pop('force_download'	,				a_			)	,				proxies=kwargs.pop('proxies'	,				a_			)	,				resume_download=kwargs.pop('resume_download'	,				a_			)	,				local_files_only=kwargs.pop('local_files_only'	,				a_			)	,				use_auth_token=kwargs.pop('use_auth_token'	,				a_			)	,				revision=kwargs.pop('revision'	,				a_			)	,				)
													if speaker_embeddings_path is None:
																logger.warning(
																    f"""`{os.path.join(a_	,				a_			)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""			)
																lowerCamelCase__      :	Dict						=None
													else:
																with open(a_			) as speaker_embeddings_json:
																			lowerCamelCase__      :	Optional[int]						=json.load(a_			)
										else:
													lowerCamelCase__      :	List[str]						=None

										lowerCamelCase__      :	Any						=AutoTokenizer.from_pretrained(a_	,				**a_			)

										return cls(tokenizer=a_	,				speaker_embeddings=a_			)





							def  UpperCAmelCase__				(    self    :int	,				lowerCamelCase_    :Union[str, Any]	,				lowerCamelCase_    :List[str]="speaker_embeddings_path.json"	,				lowerCamelCase_    :List[str]="speaker_embeddings"	,				lowerCamelCase_    :Union[str, Any] = False	,				**lowerCamelCase_    :Optional[int]	,				):





										"""simple docstring"""

										if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(a_	,				a_	,				'v2'			)	,				exist_ok=a_			)

													lowerCamelCase__      :	Tuple						={}

													lowerCamelCase__      :	int						=save_directory

													for prompt_key in self.speaker_embeddings:
																if prompt_key != "repo_or_path":
																			lowerCamelCase__      :	int						=self._load_voice_preset(a_			)

																			lowerCamelCase__      :	Union[str, Any]						={}
																			for key in self.speaker_embeddings[prompt_key]:
																						np.save(
																						    os.path.join(
																						        embeddings_dict['repo_or_path']	,				a_	,				f"""{prompt_key}_{key}"""			)	,				voice_preset[key]	,				allow_pickle=a_	,				)
																						lowerCamelCase__      :	str						=os.path.join(a_	,				f"""{prompt_key}_{key}.npy"""			)

																			lowerCamelCase__      :	Union[str, Any]						=tmp_dict

													with open(os.path.join(a_	,				a_			)	,				'w'			) as fp:
																json.dump(a_	,				a_			)

										super().save_pretrained(a_	,				a_	,				**a_			)





							def  UpperCAmelCase__				(    self    :Dict	,				lowerCamelCase_    :Union[str, Any] = None	,				**lowerCamelCase_    :int			):





										"""simple docstring"""

										lowerCamelCase__      :	Dict						=self.speaker_embeddings[voice_preset]

										lowerCamelCase__      :	List[str]						={}
										for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset_paths:
																raise ValueError(
																    f"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""			)

													lowerCamelCase__      :	Optional[Any]						=get_file_from_repo(
													    self.speaker_embeddings.get('repo_or_path'	,				'/'			)	,				voice_preset_paths[key]	,				subfolder=kwargs.pop('subfolder'	,				a_			)	,				cache_dir=kwargs.pop('cache_dir'	,				a_			)	,				force_download=kwargs.pop('force_download'	,				a_			)	,				proxies=kwargs.pop('proxies'	,				a_			)	,				resume_download=kwargs.pop('resume_download'	,				a_			)	,				local_files_only=kwargs.pop('local_files_only'	,				a_			)	,				use_auth_token=kwargs.pop('use_auth_token'	,				a_			)	,				revision=kwargs.pop('revision'	,				a_			)	,				)
													if path is None:
																raise ValueError(
																    f"""`{os.path.join(self.speaker_embeddings.get('repo_or_path'	,				'/'			)	,				voice_preset_paths[key]			)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""			)

													lowerCamelCase__      :	int						=np.load(a_			)

										return voice_preset_dict





							def  UpperCAmelCase__				(    self    :int	,				lowerCamelCase_    :Optional[Any] = None			):





										"""simple docstring"""

										for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset:
																raise ValueError(f"""Voice preset unrecognized, missing {key} as a key."""			)

													if not isinstance(voice_preset[key]	,				np.ndarray			):
																raise ValueError(f"""{key} voice preset must be a {str(self.preset_shape[key]			)}D ndarray."""			)

													if len(voice_preset[key].shape			) != self.preset_shape[key]:
																raise ValueError(f"""{key} voice preset must be a {str(self.preset_shape[key]			)}D ndarray."""			)





							def __call__(    self    :Tuple	,				lowerCamelCase_    :Dict=None	,				lowerCamelCase_    :Optional[int]=None	,				lowerCamelCase_    :Optional[int]="pt"	,				lowerCamelCase_    :List[Any]=256	,				lowerCamelCase_    :Optional[Any]=False	,				lowerCamelCase_    :int=True	,				lowerCamelCase_    :Dict=False	,				**lowerCamelCase_    :Dict	,				):





										"""simple docstring"""

										if voice_preset is not None and not isinstance(a_	,				a_			):
													if (
													    isinstance(a_	,				a_			)
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																lowerCamelCase__      :	List[Any]						=self._load_voice_preset(a_			)

													else:
																if isinstance(a_	,				a_			) and not voice_preset.endswith('.npz'			):
																			lowerCamelCase__      :	List[str]						=voice_preset + ".npz"

																lowerCamelCase__      :	Tuple						=np.load(a_			)

										if voice_preset is not None:
													self._validate_voice_preset_dict(a_	,				**a_			)
													lowerCamelCase__      :	str						=BatchFeature(data=a_	,				tensor_type=a_			)

										lowerCamelCase__      :	int						=self.tokenizer(
										    a_	,				return_tensors=a_	,				padding='max_length'	,				max_length=a_	,				return_attention_mask=a_	,				return_token_type_ids=a_	,				add_special_tokens=a_	,				**a_	,				)

										if voice_preset is not None:
													lowerCamelCase__      :	str						=voice_preset

										return encoded_text



'''simple docstring'''
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def 	_A  (       A							)  ->				int:
	# 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 >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
	    or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f)  #
	    or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f)  #
	    or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f)  #
	    or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f)  #
	    or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f)  #
	    or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
	    or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f)  #
	):  #
		return True

	return False




def 	_A  (       A							)  ->				List[Any]:
	# word like '180' or '身高' or '神'
	for char in word:
		lowercase						:		int   					=		ord(A							)
		if not _is_chinese_char(A							):
			return 0
	return 1




def 	_A  (       A							)  ->				Dict:
	lowercase						:		Any   					=		set()

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




def 	_A  (       A							,A							)  ->				Tuple:
	if not chinese_word_set:
		return bert_tokens
	lowercase						:		Optional[Any]   					=		max([len(A							) for w in chinese_word_set]							)

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




def 	_A  (       A							,A							,A							)  ->				List[Any]:
	lowercase						:		List[Any]   					=		[]

	for i in range(0							,len(A							)							,1_0_0							):
		lowercase						:		int   					=		ltp_tokenizer.seg(lines[i : i + 1_0_0]							)[0]
		lowercase						:		Dict   					=		[get_chinese_word(A							) for r in res]
		ltp_res.extend(A							)
	assert len(A							) == len(A							)

	lowercase						:		List[str]   					=		[]
	for i in range(0							,len(A							)							,1_0_0							):
		lowercase						:		int   					=		bert_tokenizer(lines[i : i + 1_0_0]							,add_special_tokens=A							,truncation=A							,max_length=5_1_2							)
		bert_res.extend(res["input_ids"]							)
	assert len(A							) == len(A							)

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

	assert len(A							) == len(A							)

	return ref_ids




def 	_A  (       A							)  ->				Union[str, Any]:
	# 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[str]   					=		f.readlines()
	lowercase						:		Tuple   					=		[line.strip() for line in data if len(A							) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
	lowercase						:		Any   					=		LTP(args.ltp							)  # faster in GPU device
	lowercase						:		int   					=		BertTokenizer.from_pretrained(args.bert							)

	lowercase						:		List[str]   					=		prepare_ref(A							,A							,A							)

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


if __name__ == "__main__":
					lowerCAmelCase   : List[Any]             =				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""")

					lowerCAmelCase   : Union[str, Any]             =				parser.parse_args()
					main(args)


