"""simple docstring"""







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str   ):
     # 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 >= 0x4E00 and cp <= 0x9FFF)
         or (cp >= 0x3400 and cp <= 0x4DBF)  #
         or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
         or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
         or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
         or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
         or (cp >= 0xF900 and cp <= 0xFAFF)
         or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
     ):  #
          return True

     return False



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str   ):
     # word like '180' or '身高' or '神'
     for char in word:
          lowercase_			:       List[str]									=			ord(__SCREAMING_SNAKE_CASE   )
          if not _is_chinese_char(__SCREAMING_SNAKE_CASE   ):
               return 0
     return 1



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]   ):
     lowercase_			:       Union[str, Any]									=			set()

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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  set()   ):
     if not chinese_word_set:
          return bert_tokens
     lowercase_			:       Dict									=			max([len(__SCREAMING_SNAKE_CASE   ) for w in chinese_word_set]   )

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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  LTP  ,    __SCREAMING_SNAKE_CASE							:  BertTokenizer   ):
     lowercase_			:       List[str]									=			[]

     for i in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    1_00   ):
          lowercase_			:       Union[str, Any]									=			ltp_tokenizer.pipeline(lines[i : i + 1_00]  ,    tasks=['cws']   ).cws
          lowercase_			:       List[str]									=			[get_chinese_word(__SCREAMING_SNAKE_CASE   ) for r in res]
          ltp_res.extend(__SCREAMING_SNAKE_CASE   )
     assert len(__SCREAMING_SNAKE_CASE   ) == len(__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[Any]									=			[]
     for i in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    1_00   ):
          lowercase_			:       Tuple									=			bert_tokenizer(lines[i : i + 1_00]  ,    add_special_tokens=__SCREAMING_SNAKE_CASE  ,    truncation=__SCREAMING_SNAKE_CASE  ,    max_length=5_12   )
          bert_res.extend(res['input_ids']   )
     assert len(__SCREAMING_SNAKE_CASE   ) == len(__SCREAMING_SNAKE_CASE   )

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

     assert len(__SCREAMING_SNAKE_CASE   ) == len(__SCREAMING_SNAKE_CASE   )

     return ref_ids



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict   ):
     # 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:
          lowercase_			:       Optional[Any]									=			f.readlines()
     lowercase_			:       Dict									=			[line.strip() for line in data if len(__SCREAMING_SNAKE_CASE   ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
     lowercase_			:       Tuple									=			LTP(args.ltp   )  # faster in GPU device
     lowercase_			:       List[str]									=			BertTokenizer.from_pretrained(args.bert   )

     lowercase_			:       List[str]									=			prepare_ref(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

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


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

       __SCREAMING_SNAKE_CASE								=parser.parse_args()
       main(args)


"""simple docstring"""







import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


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

       from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline







@require_flax
class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        with tempfile.TemporaryDirectory() as tmpdirname:
             # pipeline has Flax weights
             lowercase_			:       Optional[int]									=			FlaxDiffusionPipeline.from_pretrained(
                 'hf-internal-testing/tiny-stable-diffusion-pipe'  ,safety_checker=__UpperCamelCase  ,cache_dir=__UpperCamelCase    )

             lowercase_			:       Dict									=			[t[-1] for t in os.walk(os.path.join(__UpperCamelCase  ,os.listdir(__UpperCamelCase    )[0]  ,'snapshots'    )    )]
             lowercase_			:       Tuple									=			[item for sublist in all_root_files for item in sublist]

             # None of the downloaded files should be a PyTorch file even if we have some here:
             # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
             assert not any(f.endswith('.bin'    ) for f in files    )







@slow
@require_flax
class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       List[Any]									=			FlaxStableDiffusionPipeline.from_pretrained(
            'hf-internal-testing/tiny-stable-diffusion-pipe'  ,safety_checker=__UpperCamelCase    )

        lowercase_			:       Dict									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Optional[Any]									=			jax.random.PRNGKey(0    )
        lowercase_			:       Optional[int]									=			4

        lowercase_			:       int									=			jax.device_count()
        lowercase_			:       Optional[int]									=			num_samples * [prompt]
        lowercase_			:       List[Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       Dict									=			replicate(__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Dict									=			shard(__UpperCamelCase    )

        lowercase_			:       List[Any]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 64, 64, 3)
        if jax.device_count() == 8:
             assert np.abs(np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 4.151_4745    ) < 1e-3
             assert np.abs(np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 4_9947.875    ) < 5e-1

        lowercase_			:       List[Any]									=			pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]    )    )    )
        assert len(__UpperCamelCase    ) == num_samples
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       Any									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='flax'  ,safety_checker=__UpperCamelCase    )

        lowercase_			:       Any									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       List[str]									=			jax.random.PRNGKey(0    )
        lowercase_			:       Tuple									=			50

        lowercase_			:       Optional[Any]									=			jax.device_count()
        lowercase_			:       List[Any]									=			num_samples * [prompt]
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       List[str]									=			replicate(__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			shard(__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0565_2401)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 238_3808.2)    ) < 5e-1
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       Tuple									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,safety_checker=__UpperCamelCase    )

        lowercase_			:       List[Any]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Dict									=			jax.random.PRNGKey(0    )
        lowercase_			:       Optional[int]									=			50

        lowercase_			:       Tuple									=			jax.device_count()
        lowercase_			:       Dict									=			num_samples * [prompt]
        lowercase_			:       Any									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       int									=			replicate(__UpperCamelCase    )
        lowercase_			:       int									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			shard(__UpperCamelCase    )

        lowercase_			:       List[str]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0400_3906)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 237_3516.75)    ) < 5e-1
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       str									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa    )

        lowercase_			:       Optional[int]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Optional[Any]									=			jax.random.PRNGKey(0    )
        lowercase_			:       Union[str, Any]									=			50

        lowercase_			:       Optional[Any]									=			jax.device_count()
        lowercase_			:       Tuple									=			num_samples * [prompt]
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       List[Any]									=			replicate(__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			shard(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0400_3906)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 237_3516.75)    ) < 5e-1
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Tuple									=			FlaxDDIMScheduler(
            beta_start=0.0_0085  ,beta_end=0.012  ,beta_schedule='scaled_linear'  ,set_alpha_to_one=__UpperCamelCase  ,steps_offset=1  ,)

        lowercase_							,						lowercase_			:       str									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,scheduler=__UpperCamelCase  ,safety_checker=__UpperCamelCase  ,)
        lowercase_			:       Optional[int]									=			scheduler.create_state()

        lowercase_			:       List[Any]									=			scheduler_state

        lowercase_			:       List[Any]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       str									=			jax.random.PRNGKey(0    )
        lowercase_			:       str									=			50

        lowercase_			:       List[Any]									=			jax.device_count()
        lowercase_			:       List[str]									=			num_samples * [prompt]
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       List[Any]									=			replicate(__UpperCamelCase    )
        lowercase_			:       int									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       str									=			shard(__UpperCamelCase    )

        lowercase_			:       str									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0_4504_3945)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 234_7693.5)    ) < 5e-1







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


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Union[str, Any]									=			jax.device_count()
        lowercase_			:       List[str]									=			num_samples * [prompt]
        lowercase_			:       int									=			jax.random.split(jax.random.PRNGKey(0    )  ,__UpperCamelCase    )

        lowercase_							,						lowercase_			:       Dict									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,safety_checker=__UpperCamelCase  ,)

        lowercase_			:       str									=			replicate(__UpperCamelCase    )
        lowercase_			:       int									=			pipeline.prepare_inputs(__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			shard(__UpperCamelCase    )
        lowercase_			:       Tuple									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images
        assert images.shape == (num_samples, 1, 512, 512, 3)
        lowercase_			:       Union[str, Any]									=			images[2, 0, 256, 10:17, 1]

        # With memory efficient attention
        lowercase_							,						lowercase_			:       List[Any]									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,safety_checker=__UpperCamelCase  ,use_memory_efficient_attention=__UpperCamelCase  ,)

        lowercase_			:       List[Any]									=			replicate(__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )
        lowercase_			:       List[Any]									=			shard(__UpperCamelCase    )
        lowercase_			:       Any									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images
        assert images_eff.shape == (num_samples, 1, 512, 512, 3)
        lowercase_			:       Optional[int]									=			images[2, 0, 256, 10:17, 1]

        # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
        # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
        assert abs(slice_eff - slice    ).max() < 1e-2

