

from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available


@dataclass
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
    __lowerCamelCase		: Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
       from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
       from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline




from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
         return True





    def    snake_case_       (   self):
         lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
         lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
         lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
         lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

         lowercase__					:     Any   = tokenizer.tokenize(a)
         self.assertListEqual(a		,     a)

         lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]

         lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)

         lowercase__					:     Optional[Any]   = tokenizer.decode(a)
         self.assertEqual(a		,     a)

