


import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	__lowercase					(	__snake_case			,       __snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							AutoencoderKL
      UpperCamelCase										=							'''sample'''
      UpperCamelCase										=							1E-2





      @property
      def 							_lowercase					(		self    :       Dict							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  4
         UpperCAmelCase      					=  3
         UpperCAmelCase      					=  (3_2, 3_2)

         UpperCAmelCase      					=  floats_tensor((batch_size, num_channels) + sizes							).to(__lowerCamelCase							)

         return {"sample": image}





      @property
      def 							_lowercase					(		self    :       str							)     -> Tuple:






         """simple docstring"""







         return (3, 3_2, 3_2)





      @property
      def 							_lowercase					(		self    :       List[str]							)     -> Optional[int]:






         """simple docstring"""







         return (3, 3_2, 3_2)





      def 							_lowercase					(		self    :       Any							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  {
             """block_out_channels""": [3_2, 6_4],
             """in_channels""": 3,
             """out_channels""": 3,
             """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
             """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
             """latent_channels""": 4,
         }
         UpperCAmelCase      					=  self.dummy_input
         return init_dict, inputs_dict





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Tuple:






         """simple docstring"""







         pass





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






         """simple docstring"""







         pass





      @unittest.skipIf(torch_device == """mps"""       ,							"""Gradient checkpointing skipped on MPS"""							)
      def 							_lowercase					(		self    :       List[str]							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase      					=  self.model_class(**__lowerCamelCase							)
         model.to(__lowerCamelCase							)

         assert not model.is_gradient_checkpointing and model.training

         UpperCAmelCase      					=  model(**__lowerCamelCase							).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model.zero_grad()

         UpperCAmelCase      					=  torch.randn_like(__lowerCamelCase							)
         UpperCAmelCase      					=  (out - labels).mean()
         loss.backward()

         # re-instantiate the model now enabling gradient checkpointing
         UpperCAmelCase      					=  self.model_class(**__lowerCamelCase							)
         # clone model
         model_a.load_state_dict(model.state_dict()							)
         model_a.to(__lowerCamelCase							)
         model_a.enable_gradient_checkpointing()

         assert model_a.is_gradient_checkpointing and model_a.training

         UpperCAmelCase      					=  model_a(**__lowerCamelCase							).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model_a.zero_grad()
         UpperCAmelCase      					=  (out_a - labels).mean()
         loss_a.backward()

         # compare the output and parameters gradients
         self.assertTrue((loss - loss_a).abs() < 1e-5							)
         UpperCAmelCase      					=  dict(model.named_parameters()							)
         UpperCAmelCase      					=  dict(model_a.named_parameters()							)
         for name, param in named_params.items():
            self.assertTrue(torch_all_close(param.grad.data       ,							named_params_a[name].grad.data       ,							atol=5e-5							)							)





      def 							_lowercase					(		self    :       int							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""       ,							output_loading_info=__lowerCamelCase							)
         self.assertIsNotNone(__lowerCamelCase							)
         self.assertEqual(len(loading_info["""missing_keys"""]							)       ,							0							)

         model.to(__lowerCamelCase							)
         UpperCAmelCase      					=  model(**self.dummy_input							)

         assert image is not None, "Make sure output is not None"





      def 							_lowercase					(		self    :       Optional[Any]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""							)
         UpperCAmelCase      					=  model.to(__lowerCamelCase							)
         model.eval()

         if torch_device == "mps":
            UpperCAmelCase      					=  torch.manual_seed(0							)
         else:
            UpperCAmelCase      					=  torch.Generator(device=__lowerCamelCase							).manual_seed(0							)

         UpperCAmelCase      					=  torch.randn(
             1       ,							model.config.in_channels       ,							model.config.sample_size       ,							model.config.sample_size       ,							generator=torch.manual_seed(0							)       ,							)
         UpperCAmelCase      					=  image.to(__lowerCamelCase							)
         with torch.no_grad():
            UpperCAmelCase      					=  model(__lowerCamelCase       ,							sample_posterior=__lowerCamelCase       ,							generator=__lowerCamelCase							).sample

         UpperCAmelCase      					=  output[0, -1, -3:, -3:].flatten().cpu()

         # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
         # the expected output slices are not the same for CPU and GPU.
         if torch_device == "mps":
            UpperCAmelCase      					=  torch.tensor(
                [
                    -4.0_0_7_8e-0_1,
                    -3.8_3_2_3e-0_4,
                    -1.2_6_8_1e-0_1,
                    -1.1_4_6_2e-0_1,
                    2.0_0_9_5e-0_1,
                    1.0_8_9_3e-0_1,
                    -8.8_2_4_7e-0_2,
                    -3.0_3_6_1e-0_1,
                    -9.8_6_4_4e-0_3,
                ]							)
         elif torch_device == "cpu":
            UpperCAmelCase      					=  torch.tensor(
                [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]							)
         else:
            UpperCAmelCase      					=  torch.tensor(
                [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]							)

         self.assertTrue(torch_all_close(__lowerCamelCase       ,							__lowerCamelCase       ,							rtol=1e-2							)							)






@slow
class 	__lowercase					(	unittest.TestCase	):





      def 							_lowercase					(		self    :       int       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       int							)     -> Optional[Any]:






         """simple docstring"""







         return F"""gaussian_noise_s={seed}_shape={'_'.join([str(__lowerCamelCase							) for s in shape]							)}.npy"""





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Dict:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Optional[int]       ,							__lowerCamelCase    :       Tuple=0       ,							__lowerCamelCase    :       List[Any]=(4, 3, 5_1_2, 5_1_2)       ,							__lowerCamelCase    :       List[str]=False							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  torch.floataa if fpaa else torch.floataa
         UpperCAmelCase      					=  torch.from_numpy(load_hf_numpy(self.get_file_format(__lowerCamelCase       ,							__lowerCamelCase							)							)							).to(__lowerCamelCase							).to(__lowerCamelCase							)
         return image





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Optional[int]="CompVis/stable-diffusion-v1-4"       ,							__lowerCamelCase    :       Any=False							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  """fp16""" if fpaa else None
         UpperCAmelCase      					=  torch.floataa if fpaa else torch.floataa

         UpperCAmelCase      					=  AutoencoderKL.from_pretrained(
             __lowerCamelCase       ,							subfolder="""vae"""       ,							torch_dtype=__lowerCamelCase       ,							revision=__lowerCamelCase       ,							)
         model.to(__lowerCamelCase							).eval()

         return model





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       Union[str, Any]=0							)     -> Any:






         """simple docstring"""







         if torch_device == "mps":
            return torch.manual_seed(__lowerCamelCase							)
         return torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)





      @parameterized.expand(
          [
              # fmt: off
              [3_3, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
              [4_7, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
              # fmt: on
          ]							)
      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       Optional[Any]							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_sd_vae_model()
         UpperCAmelCase      					=  self.get_sd_image(__lowerCamelCase							)
         UpperCAmelCase      					=  self.get_generator(__lowerCamelCase							)

         with torch.no_grad():
            UpperCAmelCase      					=  model(__lowerCamelCase       ,							generator=__lowerCamelCase       ,							sample_posterior=__lowerCamelCase							).sample

         assert sample.shape == image.shape

         UpperCAmelCase      					=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
         UpperCAmelCase      					=  torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice							)

         assert torch_all_close(__lowerCamelCase       ,							__lowerCamelCase       ,							atol=3e-3							)





      @parameterized.expand(
          [
              # fmt: off
              [3_3, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
              [4_7, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
              # fmt: on
          ]							)
      @require_torch_gpu
      def 							_lowercase					(		self    :       int       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       Tuple							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_sd_vae_model(fpaa=__lowerCamelCase							)
         UpperCAmelCase      					=  self.get_sd_image(__lowerCamelCase       ,							fpaa=__lowerCamelCase							)
         UpperCAmelCase      					=  self.get_generator(__lowerCamelCase							)

         with torch.no_grad():
            UpperCAmelCase      					=  model(__lowerCamelCase       ,							generator=__lowerCamelCase       ,							sample_posterior=__lowerCamelCase							).sample

         assert sample.shape == image.shape

         UpperCAmelCase      					=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
         UpperCAmelCase      					=  torch.tensor(__lowerCamelCase							)

         assert torch_all_close(__lowerCamelCase       ,							__lowerCamelCase       ,							atol=1e-2							)





      @parameterized.expand(
          [
              # fmt: off
              [3_3, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
              [4_7, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
              # fmt: on
          ]							)
      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       List[Any]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_sd_vae_model()
         UpperCAmelCase      					=  self.get_sd_image(__lowerCamelCase							)

         with torch.no_grad():
            UpperCAmelCase      					=  model(__lowerCamelCase							).sample

         assert sample.shape == image.shape

         UpperCAmelCase      					=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
         UpperCAmelCase      					=  torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice							)

         assert torch_all_close(__lowerCamelCase       ,							__lowerCamelCase       ,							atol=3e-3							)





      @parameterized.expand(
          [
              # fmt: off
              [1_3, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
              [3_7, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
              # fmt: on
          ]							)
      @require_torch_gpu
      def 							_lowercase					(		self    :       List[str]       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Any							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_sd_vae_model()
         UpperCAmelCase      					=  self.get_sd_image(__lowerCamelCase       ,							shape=(3, 4, 6_4, 6_4)							)

         with torch.no_grad():
            UpperCAmelCase      					=  model.decode(__lowerCamelCase							).sample

         assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

         UpperCAmelCase      					=  sample[-1, -2:, :2, -2:].flatten().cpu()
         UpperCAmelCase      					=  torch.tensor(__lowerCamelCase							)

         assert torch_all_close(__lowerCamelCase       ,							__lowerCamelCase       ,							atol=1e-3							)





      @parameterized.expand(
          [
              # fmt: off
              [2_7, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
              [1_6, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
              # fmt: on
          ]							)
      @require_torch_gpu
      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       str							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_sd_vae_model(fpaa=__lowerCamelCase							)
         UpperCAmelCase      					=  self.get_sd_image(__lowerCamelCase       ,							shape=(3, 4, 6_4, 6_4)       ,							fpaa=__lowerCamelCase							)

         with torch.no_grad():
            UpperCAmelCase      					=  model.decode(__lowerCamelCase							).sample

         assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

         UpperCAmelCase      					=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
         UpperCAmelCase      					=  torch.tensor(__lowerCamelCase							)

         assert torch_all_close(__lowerCamelCase       ,							__lowerCamelCase       ,							atol=5e-3							)





      @parameterized.expand([(1_3,), (1_6,), (2_7,)]							)
      @require_torch_gpu
      @unittest.skipIf(not is_xformers_available()       ,							reason="""xformers is not required when using PyTorch 2.0."""							)
      def 							_lowercase					(		self    :       str       ,							__lowerCamelCase    :       List[Any]							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_sd_vae_model(fpaa=__lowerCamelCase							)
         UpperCAmelCase      					=  self.get_sd_image(__lowerCamelCase       ,							shape=(3, 4, 6_4, 6_4)       ,							fpaa=__lowerCamelCase							)

         with torch.no_grad():
            UpperCAmelCase      					=  model.decode(__lowerCamelCase							).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
            UpperCAmelCase      					=  model.decode(__lowerCamelCase							).sample

         assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

         assert torch_all_close(__lowerCamelCase       ,							__lowerCamelCase       ,							atol=1e-1							)





      @parameterized.expand([(1_3,), (1_6,), (3_7,)]							)
      @require_torch_gpu
      @unittest.skipIf(not is_xformers_available()       ,							reason="""xformers is not required when using PyTorch 2.0."""							)
      def 							_lowercase					(		self    :       Optional[int]       ,							__lowerCamelCase    :       Any							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_sd_vae_model()
         UpperCAmelCase      					=  self.get_sd_image(__lowerCamelCase       ,							shape=(3, 4, 6_4, 6_4)							)

         with torch.no_grad():
            UpperCAmelCase      					=  model.decode(__lowerCamelCase							).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
            UpperCAmelCase      					=  model.decode(__lowerCamelCase							).sample

         assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

         assert torch_all_close(__lowerCamelCase       ,							__lowerCamelCase       ,							atol=1e-2							)





      @parameterized.expand(
          [
              # fmt: off
              [3_3, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
              [4_7, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
              # fmt: on
          ]							)
      def 							_lowercase					(		self    :       Dict       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       List[Any]							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_sd_vae_model()
         UpperCAmelCase      					=  self.get_sd_image(__lowerCamelCase							)
         UpperCAmelCase      					=  self.get_generator(__lowerCamelCase							)

         with torch.no_grad():
            UpperCAmelCase      					=  model.encode(__lowerCamelCase							).latent_dist
            UpperCAmelCase      					=  dist.sample(generator=__lowerCamelCase							)

         assert list(sample.shape							) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

         UpperCAmelCase      					=  sample[0, -1, -3:, -3:].flatten().cpu()
         UpperCAmelCase      					=  torch.tensor(__lowerCamelCase							)

         UpperCAmelCase      					=  3e-3 if torch_device != """mps""" else 1e-2
         assert torch_all_close(__lowerCamelCase       ,							__lowerCamelCase       ,							atol=__lowerCamelCase							)







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->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 >= 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 	_UpperCamelCase					(    lowerCAmelCase_ )    ->List[str]:
   # word like '180' or '身高' or '神'
   for char in word:
      UpperCAmelCase      					=  ord(lowerCAmelCase_ )
      if not _is_chinese_char(lowerCAmelCase_ ):
         return 0
   return 1
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[int]:
   UpperCAmelCase      					=  set()

   for token in tokens:
      UpperCAmelCase      					=  len(lowerCAmelCase_ ) > 1 and is_chinese(lowerCAmelCase_ )
      if chinese_word:
         word_set.add(lowerCAmelCase_ )
   UpperCAmelCase      					=  list(lowerCAmelCase_ )
   return word_list
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Union[str, Any]:
   if not chinese_word_set:
      return bert_tokens
   UpperCAmelCase      					=  max([len(lowerCAmelCase_ ) for w in chinese_word_set] )

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

   for i in range(0	,    len(lowerCAmelCase_ )	,    1_0_0 ):
      UpperCAmelCase      					=  ltp_tokenizer.pipeline(lines[i : i + 1_0_0]	,    tasks=["""cws"""] ).cws
      UpperCAmelCase      					=  [get_chinese_word(lowerCAmelCase_ ) for r in res]
      ltp_res.extend(lowerCAmelCase_ )
   assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )

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

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

   assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )

   return ref_ids
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   # 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      					=  f.readlines()
   UpperCAmelCase      					=  [line.strip() for line in data if len(lowerCAmelCase_ ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   UpperCAmelCase      					=  LTP(args.ltp )  # faster in GPU device
   UpperCAmelCase      					=  BertTokenizer.from_pretrained(args.bert )

   UpperCAmelCase      					=  prepare_ref(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )

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


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)



