






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


__snake_case												=  logging.get_logger(__name__)

__snake_case												=  {
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}







class 			UpperCAmelCase_						(					UpperCamelCase_ ):



      """simple docstring"""
      UpperCamelCase_				:					Optional[Any]           ='roc_bert'
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_=3_0522    ,				SCREAMING_SNAKE_CASE_=768    ,				SCREAMING_SNAKE_CASE_=12    ,				SCREAMING_SNAKE_CASE_=12    ,				SCREAMING_SNAKE_CASE_=3072    ,				SCREAMING_SNAKE_CASE_="gelu"    ,				SCREAMING_SNAKE_CASE_=0.1    ,				SCREAMING_SNAKE_CASE_=0.1    ,				SCREAMING_SNAKE_CASE_=512    ,				SCREAMING_SNAKE_CASE_=2    ,				SCREAMING_SNAKE_CASE_=0.02    ,				SCREAMING_SNAKE_CASE_=1e-12    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=0    ,				SCREAMING_SNAKE_CASE_="absolute"    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=768    ,				SCREAMING_SNAKE_CASE_=910    ,				SCREAMING_SNAKE_CASE_=512    ,				SCREAMING_SNAKE_CASE_=2_4858    ,				SCREAMING_SNAKE_CASE_=True    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      Optional[int]:
          UpperCamelCase       :List[str]								=   vocab_size
          UpperCamelCase       :Any								=   max_position_embeddings
          UpperCamelCase       :Dict								=   hidden_size
          UpperCamelCase       :Union[str, Any]								=   num_hidden_layers
          UpperCamelCase       :Tuple								=   num_attention_heads
          UpperCamelCase       :int								=   intermediate_size
          UpperCamelCase       :Tuple								=   hidden_act
          UpperCamelCase       :List[str]								=   hidden_dropout_prob
          UpperCamelCase       :Tuple								=   attention_probs_dropout_prob
          UpperCamelCase       :Any								=   initializer_range
          UpperCamelCase       :Union[str, Any]								=   type_vocab_size
          UpperCamelCase       :Union[str, Any]								=   layer_norm_eps
          UpperCamelCase       :Optional[int]								=   use_cache
          UpperCamelCase       :Tuple								=   enable_pronunciation
          UpperCamelCase       :Optional[Any]								=   enable_shape
          UpperCamelCase       :Union[str, Any]								=   pronunciation_embed_dim
          UpperCamelCase       :List[Any]								=   pronunciation_vocab_size
          UpperCamelCase       :Tuple								=   shape_embed_dim
          UpperCamelCase       :str								=   shape_vocab_size
          UpperCamelCase       :Optional[int]								=   concat_input
          UpperCamelCase       :Optional[Any]								=   position_embedding_type
          UpperCamelCase       :Optional[Any]								=   classifier_dropout
          super().__init__(pad_token_id=A_    ,				**A_ )










import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
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 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       Tuple):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Any									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png''')
									lowerCAmelCase_			:     str									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

									lowerCAmelCase_			:     List[Any]									= '''xvjiarui/stable-diffusion-2-inpainting'''
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= FlaxStableDiffusionInpaintPipeline.from_pretrained(A_				,				safety_checker=A_)

									lowerCAmelCase_			:     List[str]									= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowerCAmelCase_			:     List[Any]									= jax.random.PRNGKey(0)
									lowerCAmelCase_			:     str									= 5_0

									lowerCAmelCase_			:     List[Any]									= jax.device_count()
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [prompt]
									lowerCAmelCase_			:     str									= num_samples * [init_image]
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [mask_image]
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= pipeline.prepare_inputs(A_				,				A_				,				A_)

									# shard inputs and rng
									lowerCAmelCase_			:     str									= replicate(A_)
									lowerCAmelCase_			:     str									= jax.random.split(A_				,				jax.device_count())
									lowerCAmelCase_			:     List[Any]									= shard(A_)
									lowerCAmelCase_			:     str									= shard(A_)
									lowerCAmelCase_			:     Tuple									= shard(A_)

									lowerCAmelCase_			:     int									= pipeline(
									    A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				jit=A_)

									lowerCAmelCase_			:     Optional[int]									= output.images.reshape(A_				,				5_1_2				,				5_1_2				,				3)

									lowerCAmelCase_			:     List[str]									= images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									lowerCAmelCase_			:     int									= jnp.asarray(jax.device_get(image_slice.flatten()))
									lowerCAmelCase_			:     str									= jnp.array(
									    [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084])
									print(F"""output_slice: {output_slice}""")
									assert jnp.abs(output_slice - expected_slice).max() < 1e-2





