


'''simple docstring'''


import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard



@slow
@require_flax
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       int ):

        '''simple docstring'''




        super().tearDown()
        gc.collect()






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_   ,					A_						:  Tuple          =				FlaxControlNetModel.from_pretrained(
            """lllyasviel/sd-controlnet-canny"""	,from_pt=_a	,dtype=jnp.bfloataa )
        A_   ,					A_						:  Dict          =				FlaxStableDiffusionControlNetPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""	,controlnet=_a	,from_pt=_a	,dtype=jnp.bfloataa )
        A_						:  List[str]          =				controlnet_params

        A_						:  Any          =				"""bird"""
        A_						:  str          =				jax.device_count()
        A_						:  Tuple          =				pipe.prepare_text_inputs([prompts] * num_samples )

        A_						:  Any          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
        A_						:  Union[str, Any]          =				pipe.prepare_image_inputs([canny_image] * num_samples )

        A_						:  Optional[Any]          =				jax.random.PRNGKey(0 )
        A_						:  Union[str, Any]          =				jax.random.split(_a	,jax.device_count() )

        A_						:  int          =				replicate(_a )
        A_						:  List[str]          =				shard(_a )
        A_						:  str          =				shard(_a )

        A_						:  Optional[Any]          =				pipe(
            prompt_ids=_a	,image=_a	,params=_a	,prng_seed=_a	,num_inference_steps=50	,jit=_a	,).images
        assert images.shape == (jax.device_count(), 1, 768, 512, 3)

        A_						:  str          =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        A_						:  Union[str, Any]          =				images[0, 253:256, 253:256, -1]

        A_						:  Optional[Any]          =				jnp.asarray(jax.device_get(image_slice.flatten() ) )
        A_						:  Optional[int]          =				jnp.array(
            [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] )
        print(f'output_slice: {output_slice}' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        A_   ,					A_						:  Dict          =				FlaxControlNetModel.from_pretrained(
            """lllyasviel/sd-controlnet-openpose"""	,from_pt=_a	,dtype=jnp.bfloataa )
        A_   ,					A_						:  int          =				FlaxStableDiffusionControlNetPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""	,controlnet=_a	,from_pt=_a	,dtype=jnp.bfloataa )
        A_						:  Tuple          =				controlnet_params

        A_						:  int          =				"""Chef in the kitchen"""
        A_						:  Any          =				jax.device_count()
        A_						:  Any          =				pipe.prepare_text_inputs([prompts] * num_samples )

        A_						:  Union[str, Any]          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
        A_						:  str          =				pipe.prepare_image_inputs([pose_image] * num_samples )

        A_						:  Optional[Any]          =				jax.random.PRNGKey(0 )
        A_						:  Dict          =				jax.random.split(_a	,jax.device_count() )

        A_						:  Any          =				replicate(_a )
        A_						:  Union[str, Any]          =				shard(_a )
        A_						:  int          =				shard(_a )

        A_						:  str          =				pipe(
            prompt_ids=_a	,image=_a	,params=_a	,prng_seed=_a	,num_inference_steps=50	,jit=_a	,).images
        assert images.shape == (jax.device_count(), 1, 768, 512, 3)

        A_						:  Optional[int]          =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        A_						:  Union[str, Any]          =				images[0, 253:256, 253:256, -1]

        A_						:  List[Any]          =				jnp.asarray(jax.device_get(image_slice.flatten() ) )
        A_						:  Optional[Any]          =				jnp.array(
            [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] )
        print(f'output_slice: {output_slice}' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2




'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




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




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      A_						:  Any          =				set()

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




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      set()):
      if not chinese_word_set:
            return bert_tokens
      A_						:  Any          =				max([len(lowerCamelCase) for w in chinese_word_set])

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




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      LTP							,						lowerCamelCase  :      BertTokenizer):
      A_						:  Union[str, Any]          =				[]

      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  List[Any]          =				ltp_tokenizer.pipeline(lines[i : i + 100]							,						tasks=["""cws"""]).cws
            A_						:  int          =				[get_chinese_word(lowerCamelCase) for r in res]
            ltp_res.extend(lowerCamelCase)
      assert len(lowerCamelCase) == len(lowerCamelCase)

      A_						:  List[Any]          =				[]
      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  Dict          =				bert_tokenizer(lines[i : i + 100]							,						add_special_tokens=lowerCamelCase							,						truncation=lowerCamelCase							,						max_length=512)
            bert_res.extend(res["""input_ids"""])
      assert len(lowerCamelCase) == len(lowerCamelCase)

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

      A_						:  str          =				prepare_ref(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

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


if __name__ == "__main__":
     __magic_name__			=      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',
     )

     __magic_name__			=      parser.parse_args()
     main(args)
