


'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  str          =				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        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Any          =				self.dummy_uncond_unet
        A_						:  List[str]          =				KarrasVeScheduler()

        A_						:  Union[str, Any]          =				KarrasVePipeline(unet=_a	,scheduler=_a )
        pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )

        A_						:  Tuple          =				torch.manual_seed(0 )
        A_						:  Union[str, Any]          =				pipe(num_inference_steps=2	,generator=_a	,output_type="""numpy""" ).images

        A_						:  Union[str, Any]          =				torch.manual_seed(0 )
        A_						:  Optional[int]          =				pipe(num_inference_steps=2	,generator=_a	,output_type="""numpy"""	,return_dict=_a )[0]

        A_						:  Dict          =				image[0, -3:, -3:, -1]
        A_						:  Union[str, Any]          =				image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        A_						:  str          =				np.array([0.0, 1.0, 0.0, 0.0, 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 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        A_						:  Tuple          =				"""google/ncsnpp-celebahq-256"""
        A_						:  Dict          =				UNetaDModel.from_pretrained(_a )
        A_						:  List[str]          =				KarrasVeScheduler()

        A_						:  Any          =				KarrasVePipeline(unet=_a	,scheduler=_a )
        pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )

        A_						:  str          =				torch.manual_seed(0 )
        A_						:  Optional[int]          =				pipe(num_inference_steps=20	,generator=_a	,output_type="""numpy""" ).images

        A_						:  List[str]          =				image[0, -3:, -3:, -1]
        assert image.shape == (1, 256, 256, 3)
        A_						:  Dict          =				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )

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




'''simple docstring'''


from ... import PretrainedConfig


__magic_name__			=      {
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  a_  					=							"""nezha"""






  def __init__(							self :       int	,_a :       Union[str, Any]=21128	,_a :       int=768	,_a :       Any=12	,_a :       List[str]=12	,_a :       str=3072	,_a :       int="gelu"	,_a :       int=0.1	,_a :       str=0.1	,_a :       Tuple=512	,_a :       List[Any]=64	,_a :       Dict=2	,_a :       List[Any]=0.02	,_a :       Optional[Any]=1e-12	,_a :       List[Any]=0.1	,_a :       Union[str, Any]=0	,_a :       Any=2	,_a :       Union[str, Any]=3	,_a :       int=True	,**_a :       int	,):

        '''simple docstring'''




        super().__init__(pad_token_id=_a	,bos_token_id=_a	,eos_token_id=_a	,**_a )

        A_						:  Tuple          =				vocab_size
        A_						:  int          =				hidden_size
        A_						:  Any          =				num_hidden_layers
        A_						:  List[Any]          =				num_attention_heads
        A_						:  Tuple          =				hidden_act
        A_						:  List[Any]          =				intermediate_size
        A_						:  List[str]          =				hidden_dropout_prob
        A_						:  Tuple          =				attention_probs_dropout_prob
        A_						:  Dict          =				max_position_embeddings
        A_						:  Optional[Any]          =				max_relative_position
        A_						:  List[Any]          =				type_vocab_size
        A_						:  int          =				initializer_range
        A_						:  Tuple          =				layer_norm_eps
        A_						:  Dict          =				classifier_dropout
        A_						:  int          =				use_cache
