
import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 				_lowerCamelCase (      lowerCAmelCase__	,							unittest.TestCase ):
     SCREAMING_SNAKE_CASE_		   =  ConsistencyModelPipeline
     SCREAMING_SNAKE_CASE_		   =  UNCONDITIONAL_IMAGE_GENERATION_PARAMS
     SCREAMING_SNAKE_CASE_		   =  UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

     # Override required_optional_params to remove num_images_per_prompt
     SCREAMING_SNAKE_CASE_		   =  frozenset(
         [
             '''num_inference_steps''',
             '''generator''',
             '''latents''',
             '''output_type''',
             '''return_dict''',
             '''callback''',
             '''callback_steps''',
         ] )






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   int					=				UNetaDModel.from_pretrained(
               '''diffusers/consistency-models-test'''						,						subfolder='''test_unet'''						,						)
           return unet






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				UNetaDModel.from_pretrained(
               '''diffusers/consistency-models-test'''						,						subfolder='''test_unet_class_cond'''						,						)
           return unet






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=False  )  ->   Union[str, Any]:




           """simple docstring"""







           if class_cond:
                 UpperCamelCase__    :   List[str]					=				self.dummy_cond_unet
           else:
                 UpperCamelCase__    :   Optional[Any]					=				self.dummy_uncond_unet

           # Default to CM multistep sampler
           UpperCamelCase__    :   Union[str, Any]					=				CMStochasticIterativeScheduler(
               num_train_timesteps=4_0						,						sigma_min=0.002						,						sigma_max=8_0.0						,						)

           UpperCamelCase__    :   Union[str, Any]					=				{
               '''unet''': unet,
               '''scheduler''': scheduler,
           }

           return components






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=0  )  ->   int:




           """simple docstring"""







           if str(_SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
                 UpperCamelCase__    :   Union[str, Any]					=				torch.manual_seed(_SCREAMING_SNAKE_CASE  )
           else:
                 UpperCamelCase__    :   Dict					=				torch.Generator(device=_SCREAMING_SNAKE_CASE  ).manual_seed(_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   int					=				{
               '''batch_size''': 1,
               '''num_inference_steps''': None,
               '''timesteps''': [2_2, 0],
               '''generator''': generator,
               '''output_type''': '''np''',
           }

           return inputs






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase__    :   int					=				self.get_dummy_components()
           UpperCamelCase__    :   List[str]					=				ConsistencyModelPipeline(**_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   str					=				pipe.to(_SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Tuple					=				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				pipe(**_SCREAMING_SNAKE_CASE  ).images
           assert image.shape == (1, 3_2, 3_2, 3)

           UpperCamelCase__    :   Dict					=				image[0, -3:, -3:, -1]
           UpperCamelCase__    :   int					=				np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase__    :   Union[str, Any]					=				self.get_dummy_components(class_cond=_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				ConsistencyModelPipeline(**_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Any					=				pipe.to(_SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   str					=				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				0
           UpperCamelCase__    :   Optional[int]					=				pipe(**_SCREAMING_SNAKE_CASE  ).images
           assert image.shape == (1, 3_2, 3_2, 3)

           UpperCamelCase__    :   Dict					=				image[0, -3:, -3:, -1]
           UpperCamelCase__    :   int					=				np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase__    :   str					=				self.get_dummy_components()
           UpperCamelCase__    :   Optional[int]					=				ConsistencyModelPipeline(**_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   int					=				pipe.to(_SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   str					=				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				1
           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Union[str, Any]					=				pipe(**_SCREAMING_SNAKE_CASE  ).images
           assert image.shape == (1, 3_2, 3_2, 3)

           UpperCamelCase__    :   str					=				image[0, -3:, -3:, -1]
           UpperCamelCase__    :   Dict					=				np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase__    :   str					=				self.get_dummy_components(class_cond=_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   int					=				ConsistencyModelPipeline(**_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				pipe.to(_SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[str]					=				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   int					=				1
           UpperCamelCase__    :   Union[str, Any]					=				None
           UpperCamelCase__    :   Dict					=				0
           UpperCamelCase__    :   List[str]					=				pipe(**_SCREAMING_SNAKE_CASE  ).images
           assert image.shape == (1, 3_2, 3_2, 3)

           UpperCamelCase__    :   Dict					=				image[0, -3:, -3:, -1]
           UpperCamelCase__    :   Dict					=				np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3




@slow
@require_torch_gpu
class 				_lowerCamelCase (      unittest.TestCase ):






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=0						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE="cpu"						,						__SCREAMING_SNAKE_CASE=torch.floataa						,						__SCREAMING_SNAKE_CASE=(1, 3, 6_4, 6_4)  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				torch.manual_seed(_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[Any]					=				{
               '''num_inference_steps''': None,
               '''timesteps''': [2_2, 0],
               '''class_labels''': 0,
               '''generator''': generator,
               '''output_type''': '''np''',
           }

           if get_fixed_latents:
                 UpperCamelCase__    :   str					=				self.get_fixed_latents(seed=_SCREAMING_SNAKE_CASE						,						device=_SCREAMING_SNAKE_CASE						,						dtype=_SCREAMING_SNAKE_CASE						,						shape=_SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   str					=				latents

           return inputs






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=0						,						__SCREAMING_SNAKE_CASE="cpu"						,						__SCREAMING_SNAKE_CASE=torch.floataa						,						__SCREAMING_SNAKE_CASE=(1, 3, 6_4, 6_4)  )  ->   Optional[int]:




           """simple docstring"""







           if type(_SCREAMING_SNAKE_CASE  ) == str:
                 UpperCamelCase__    :   List[Any]					=				torch.device(_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				torch.Generator(device=_SCREAMING_SNAKE_CASE  ).manual_seed(_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				randn_tensor(_SCREAMING_SNAKE_CASE						,						generator=_SCREAMING_SNAKE_CASE						,						device=_SCREAMING_SNAKE_CASE						,						dtype=_SCREAMING_SNAKE_CASE  )
           return latents






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   str					=				UNetaDModel.from_pretrained('''diffusers/consistency_models'''						,						subfolder='''diffusers_cd_imagenet64_l2'''  )
           UpperCamelCase__    :   Union[str, Any]					=				CMStochasticIterativeScheduler(
               num_train_timesteps=4_0						,						sigma_min=0.002						,						sigma_max=8_0.0						,						)
           UpperCamelCase__    :   Tuple					=				ConsistencyModelPipeline(unet=_SCREAMING_SNAKE_CASE						,						scheduler=_SCREAMING_SNAKE_CASE  )
           pipe.to(torch_device=_SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[int]					=				self.get_inputs()
           UpperCamelCase__    :   Optional[int]					=				pipe(**_SCREAMING_SNAKE_CASE  ).images
           assert image.shape == (1, 6_4, 6_4, 3)

           UpperCamelCase__    :   str					=				image[0, -3:, -3:, -1]

           UpperCamelCase__    :   Union[str, Any]					=				np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 2e-2






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   int					=				UNetaDModel.from_pretrained('''diffusers/consistency_models'''						,						subfolder='''diffusers_cd_imagenet64_l2'''  )
           UpperCamelCase__    :   Tuple					=				CMStochasticIterativeScheduler(
               num_train_timesteps=4_0						,						sigma_min=0.002						,						sigma_max=8_0.0						,						)
           UpperCamelCase__    :   Any					=				ConsistencyModelPipeline(unet=_SCREAMING_SNAKE_CASE						,						scheduler=_SCREAMING_SNAKE_CASE  )
           pipe.to(torch_device=_SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[int]					=				self.get_inputs()
           UpperCamelCase__    :   Tuple					=				1
           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Dict					=				pipe(**_SCREAMING_SNAKE_CASE  ).images
           assert image.shape == (1, 6_4, 6_4, 3)

           UpperCamelCase__    :   Union[str, Any]					=				image[0, -3:, -3:, -1]

           UpperCamelCase__    :   str					=				np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 2e-2






     @require_torch_a
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				UNetaDModel.from_pretrained('''diffusers/consistency_models'''						,						subfolder='''diffusers_cd_imagenet64_l2'''  )
           UpperCamelCase__    :   Any					=				CMStochasticIterativeScheduler(
               num_train_timesteps=4_0						,						sigma_min=0.002						,						sigma_max=8_0.0						,						)
           UpperCamelCase__    :   List[str]					=				ConsistencyModelPipeline(unet=_SCREAMING_SNAKE_CASE						,						scheduler=_SCREAMING_SNAKE_CASE  )
           pipe.to(torch_device=_SCREAMING_SNAKE_CASE						,						torch_dtype=torch.floataa  )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Tuple					=				self.get_inputs(get_fixed_latents=_SCREAMING_SNAKE_CASE						,						device=_SCREAMING_SNAKE_CASE  )
           # Ensure usage of flash attention in torch 2.0
           with sdp_kernel(enable_flash=_SCREAMING_SNAKE_CASE						,						enable_math=_SCREAMING_SNAKE_CASE						,						enable_mem_efficient=_SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Union[str, Any]					=				pipe(**_SCREAMING_SNAKE_CASE  ).images
           assert image.shape == (1, 6_4, 6_4, 3)

           UpperCamelCase__    :   int					=				image[0, -3:, -3:, -1]

           UpperCamelCase__    :   Dict					=				np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3






     @require_torch_a
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				UNetaDModel.from_pretrained('''diffusers/consistency_models'''						,						subfolder='''diffusers_cd_imagenet64_l2'''  )
           UpperCamelCase__    :   List[Any]					=				CMStochasticIterativeScheduler(
               num_train_timesteps=4_0						,						sigma_min=0.002						,						sigma_max=8_0.0						,						)
           UpperCamelCase__    :   List[Any]					=				ConsistencyModelPipeline(unet=_SCREAMING_SNAKE_CASE						,						scheduler=_SCREAMING_SNAKE_CASE  )
           pipe.to(torch_device=_SCREAMING_SNAKE_CASE						,						torch_dtype=torch.floataa  )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[str]					=				self.get_inputs(get_fixed_latents=_SCREAMING_SNAKE_CASE						,						device=_SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				1
           UpperCamelCase__    :   Union[str, Any]					=				None
           # Ensure usage of flash attention in torch 2.0
           with sdp_kernel(enable_flash=_SCREAMING_SNAKE_CASE						,						enable_math=_SCREAMING_SNAKE_CASE						,						enable_mem_efficient=_SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Any					=				pipe(**_SCREAMING_SNAKE_CASE  ).images
           assert image.shape == (1, 6_4, 6_4, 3)

           UpperCamelCase__    :   Tuple					=				image[0, -3:, -3:, -1]

           UpperCamelCase__    :   List[str]					=				np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # 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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # word like '180' or '身高' or '神'
      for char in word:
            UpperCamelCase__    :   Optional[Any]					=				ord(UpperCamelCase__		)
            if not _is_chinese_char(UpperCamelCase__		):
                  return 0
      return 1


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Dict					=				set()

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      if not chinese_word_set:
            return bert_tokens
      UpperCamelCase__    :   str					=				max([len(UpperCamelCase__		) for w in chinese_word_set]		)

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				[]

      for i in range(0 ,		len(UpperCamelCase__		) ,		1_0_0		):
            UpperCamelCase__    :   Optional[Any]					=				ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,		tasks=['''cws''']		).cws
            UpperCamelCase__    :   List[str]					=				[get_chinese_word(UpperCamelCase__		) for r in res]
            ltp_res.extend(UpperCamelCase__		)
      assert len(UpperCamelCase__		) == len(UpperCamelCase__		)

      UpperCamelCase__    :   str					=				[]
      for i in range(0 ,		len(UpperCamelCase__		) ,		1_0_0		):
            UpperCamelCase__    :   Dict					=				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__		)

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

      UpperCamelCase__    :   Dict					=				prepare_ref(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

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


if __name__ == "__main__":
    lowerCamelCase      =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",
    )

    lowerCamelCase      =parser.parse_args()
    main(args)


