import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			if (
			    (cp >= 0x4_E00 and cp <= 0x9_FFF)
			    or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
			    or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
			    or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
			    or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
			    or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
			    or (cp >= 0xF_900 and cp <= 0xF_AFF)
			    or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
			):  #
						return True

			return False






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Any:



			"""simple docstring"""







			for char in word:
						lowercase							=		ord(UpperCAmelCase       )
						if not _is_chinese_char(UpperCAmelCase       ):
									return 0
			return 1






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			lowercase							=		set()

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







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

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		[]

			for i in range(0,		len(UpperCAmelCase       ),		100       ):
						lowercase							=		ltp_tokenizer.pipeline(lines[i : i + 100],		tasks=['''cws''']       ).cws
						lowercase							=		[get_chinese_word(UpperCAmelCase       ) for r in res]
						ltp_res.extend(UpperCAmelCase       )
			assert len(UpperCAmelCase       ) == len(UpperCAmelCase       )

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

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

			assert len(UpperCAmelCase       ) == len(UpperCAmelCase       )

			return ref_ids






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







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

			lowercase							=		prepare_ref(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )

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


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)






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class      __lowercase					:
						# setable values
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None  # sigma(t_i)

						@classmethod
						def        __a		(	cls				:      List[str]   )							->		Dict:


									'''simple docstring'''

									return cls()






@dataclass
class      __lowercase					(    _A    ):
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									return True

						@register_to_config
						def __init__(	self				:      int							,							__lowerCamelCase				:      float = 0.02							,							__lowerCamelCase				:      float = 1_00							,							__lowerCamelCase				:      float = 1.007							,							__lowerCamelCase				:      float = 80							,							__lowerCamelCase				:      float = 0.05							,							__lowerCamelCase				:      float = 50							,							)							->		List[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									return KarrasVeSchedulerState.create()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Tuple = ()   )							->		KarrasVeSchedulerState:


									'''simple docstring'''

									lowercase							=		jnp.arange(0							,							__lowerCamelCase   )[::-1].copy()
									lowercase							=		[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=__lowerCamelCase							,							schedule=jnp.array(__lowerCamelCase							,							dtype=jnp.floataa   )							,							timesteps=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      random.KeyArray							,							)							->		Tuple[jnp.ndarray, float]:


									'''simple docstring'''

									if self.config.s_min <= sigma <= self.config.s_max:
												lowercase							=		min(self.config.s_churn / state.num_inference_steps							,							2**0.5 - 1   )
									else:
												lowercase							=		0

									# sample eps ~ N(0, S_noise^2 * I)
									lowercase							=		random.split(__lowerCamelCase							,							num=1   )
									lowercase							=		self.config.s_noise * random.normal(key=__lowerCamelCase							,							shape=sample.shape   )
									lowercase							=		sigma + gamma * sigma
									lowercase							=		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_hat + sigma_hat * model_output
									lowercase							=		(sample_hat - pred_original_sample) / sigma_hat
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_prev + sigma_prev * model_output
									lowercase							=		(sample_prev - pred_original_sample) / sigma_prev
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )



						def        __a		(	self				:      int							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Dict   )							->		Tuple:


									'''simple docstring'''

									raise NotImplementedError()





