


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_   (   _snake_case       ):

   '''simple docstring'''




   __A					: Any							=						"roc_bert"






   def __init__(						self		,							__A=3_0522		,							__A=768		,							__A=12		,							__A=12		,							__A=3072		,							__A="gelu"		,							__A=0.1		,							__A=0.1		,							__A=512		,							__A=2		,							__A=0.02		,							__A=1e-12		,							__A=True		,							__A=0		,							__A="absolute"		,							__A=None		,							__A=True		,							__A=True		,							__A=768		,							__A=910		,							__A=512		,							__A=2_4858		,							__A=True		,							**__A		,							):






     """simple docstring"""





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






'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        A__   (   unittest.TestCase      ):



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           torch.manual_seed(0							)
           A_						       =   UNetaDModel(
               block_out_channels=(32, 64)		,					layers_per_block=2		,					sample_size=32		,					in_channels=3		,					out_channels=3		,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")		,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")		,					)
           return model







    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.dummy_uncond_unet
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""							).images

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""		,					return_dict=UpperCamelCase__							)[0]

           A_						       =   image[0, -3:, -3:, -1]
           A_						       =   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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


@slow
@require_torch
class        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   """google/ddpm-cifar10-32"""

           A_						       =   UNetaDModel.from_pretrained(UpperCamelCase__							)
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)
           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					output_type="""numpy"""							).images

           A_						       =   image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]							)

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