






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase	:							Optional[Any]				=logging.get_logger(__name__)

lowerCamelCase	:							Optional[Any]				={
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Dict        = '''roc_bert'''







 def __init__(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=3_05_22							,   SCREAMING_SNAKE_CASE					:   Dict=7_68							,   SCREAMING_SNAKE_CASE					:   int=12							,   SCREAMING_SNAKE_CASE					:   Dict=12							,   SCREAMING_SNAKE_CASE					:   Any=30_72							,   SCREAMING_SNAKE_CASE					:   Dict="gelu"							,   SCREAMING_SNAKE_CASE					:   int=0.1							,   SCREAMING_SNAKE_CASE					:   Tuple=0.1							,   SCREAMING_SNAKE_CASE					:   int=5_12							,   SCREAMING_SNAKE_CASE					:   Any=2							,   SCREAMING_SNAKE_CASE					:   List[str]=0.0_2							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=1e-1_2							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   SCREAMING_SNAKE_CASE					:   Tuple=0							,   SCREAMING_SNAKE_CASE					:   List[str]="absolute"							,   SCREAMING_SNAKE_CASE					:   int=None							,   SCREAMING_SNAKE_CASE					:   str=True							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=True							,   SCREAMING_SNAKE_CASE					:   List[Any]=7_68							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=9_10							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=5_12							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=2_48_58							,   SCREAMING_SNAKE_CASE					:   Any=True							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  vocab_size
      UpperCamelCase__			:   List[str]       				=  max_position_embeddings
      UpperCamelCase__			:   Optional[Any]       				=  hidden_size
      UpperCamelCase__			:   Optional[int]       				=  num_hidden_layers
      UpperCamelCase__			:   int       				=  num_attention_heads
      UpperCamelCase__			:   Optional[int]       				=  intermediate_size
      UpperCamelCase__			:   Dict       				=  hidden_act
      UpperCamelCase__			:   Dict       				=  hidden_dropout_prob
      UpperCamelCase__			:   Optional[Any]       				=  attention_probs_dropout_prob
      UpperCamelCase__			:   List[Any]       				=  initializer_range
      UpperCamelCase__			:   int       				=  type_vocab_size
      UpperCamelCase__			:   List[str]       				=  layer_norm_eps
      UpperCamelCase__			:   str       				=  use_cache
      UpperCamelCase__			:   Dict       				=  enable_pronunciation
      UpperCamelCase__			:   str       				=  enable_shape
      UpperCamelCase__			:   Any       				=  pronunciation_embed_dim
      UpperCamelCase__			:   str       				=  pronunciation_vocab_size
      UpperCamelCase__			:   List[str]       				=  shape_embed_dim
      UpperCamelCase__			:   List[Any]       				=  shape_vocab_size
      UpperCamelCase__			:   Tuple       				=  concat_input
      UpperCamelCase__			:   Any       				=  position_embedding_type
      UpperCamelCase__			:   Union[str, Any]       				=  classifier_dropout
      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase	:							Tuple				=False





class 			__a  (    unittest.TestCase       ):
 pass







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







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__			:   List[Any]       				=  torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  pipe(
          image=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   guidance_scale=7.5							,   num_inference_steps=50							,   output_type="numpy"							,   ).images

      UpperCamelCase__			:   Union[str, Any]       				=  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__			:   Optional[Any]       				=  np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2