



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict	)->  Optional[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__    (					UpperCamelCase_				:       str	)->  Optional[Any]:
    # word like '180' or '身高' or '神'
    for char in word:
        A__		 =					ord(UpperCamelCase_	)
        if not _is_chinese_char(UpperCamelCase_	):
            return 0
    return 1



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str]	)->  List[Any]:
    A__		 =					set()

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       set()	)->  List[Any]:
    if not chinese_word_set:
        return bert_tokens
    A__		 =					max([len(UpperCamelCase_	) for w in chinese_word_set]	)

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       LTP ,       UpperCamelCase_				:       BertTokenizer	)->  int:
    A__		 =					[]

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

    A__		 =					[]
    for i in range(0 ,       len(UpperCamelCase_	) ,       1_0_0	):
        A__		 =					bert_tokenizer(lines[i : i + 1_0_0] ,       add_special_tokens=UpperCamelCase_ ,       truncation=UpperCamelCase_ ,       max_length=5_1_2	)
        bert_res.extend(res['''input_ids''']	)
    assert len(UpperCamelCase_	) == len(UpperCamelCase_	)

    A__		 =					[]
    for input_ids, chinese_word in zip(UpperCamelCase_ ,       UpperCamelCase_	):
        A__		 =					[]
        for id in input_ids:
            A__		 =					bert_tokenizer._convert_id_to_token(UpperCamelCase_	)
            input_tokens.append(UpperCamelCase_	)
        A__		 =					add_sub_symbol(UpperCamelCase_ ,       UpperCamelCase_	)
        A__		 =					[]
        # 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] == "##":
                A__		 =					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 		lowerCAmelCase__    (					UpperCamelCase_				:       int	)->  List[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:
        A__		 =					f.readlines()
    A__		 =					[line.strip() for line in data if len(UpperCamelCase_	) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    A__		 =					LTP(args.ltp	)  # faster in GPU device
    A__		 =					BertTokenizer.from_pretrained(args.bert	)

    A__		 =					prepare_ref(UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)

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


if __name__ == "__main__":
   _lowercase						  =		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")

   _lowercase						  =		parser.parse_args()
   main(args)





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				(DDPMScheduler,)




     def 		snake_case_		(				self ,						**a__):
         A__		 =					{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**a__)
         return config




     def 		snake_case_		(				self):
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=a__)




     def 		snake_case_		(				self):
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,						[0.0_0_2, 0.0_2, 0.2, 2]):
             self.check_over_configs(beta_start=a__ ,						beta_end=a__)




     def 		snake_case_		(				self):
         for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=a__)




     def 		snake_case_		(				self):
         for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=a__)




     def 		snake_case_		(				self):
         for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=a__)




     def 		snake_case_		(				self):
         self.check_over_configs(thresholding=a__)
         for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=a__ ,						prediction_type=a__ ,						sample_max_value=a__ ,						)




     def 		snake_case_		(				self):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=a__)




     def 		snake_case_		(				self):
         for t in [0, 5_0_0, 9_9_9]:
             self.check_over_forward(time_step=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.0_0_9_7_9)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.0_2)) < 1e-5




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter
         A__		 =					torch.manual_seed(0)

         for t in reversed(range(a__)):
             # 1. predict noise residual
             A__		 =					model(a__ ,						a__)

             # 2. predict previous mean of sample x_t-1
             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             A__		 =					pred_prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1e-2
         assert abs(result_mean.item() - 0.3_3_7_2) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config(prediction_type='''v_prediction''')
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter
         A__		 =					torch.manual_seed(0)

         for t in reversed(range(a__)):
             # 1. predict noise residual
             A__		 =					model(a__ ,						a__)

             # 2. predict previous mean of sample x_t-1
             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             A__		 =					pred_prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1e-2
         assert abs(result_mean.item() - 0.2_6_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=a__)

         A__		 =					scheduler.timesteps

         for i, timestep in enumerate(a__):
             if i == len(a__) - 1:
                 A__		 =					-1
             else:
                 A__		 =					timesteps[i + 1]

             A__		 =					scheduler.previous_timestep(a__)
             A__		 =					prev_t.item()

             self.assertEqual(a__ ,						a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 5_1, 0]

         with self.assertRaises(a__ ,						msg='''`custom_timesteps` must be in descending order.'''):
             scheduler.set_timesteps(timesteps=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]
         A__		 =					len(a__)

         with self.assertRaises(a__ ,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
             scheduler.set_timesteps(num_inference_steps=a__ ,						timesteps=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             a__ ,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,						):
             scheduler.set_timesteps(timesteps=a__)
