


'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, 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       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             torch.manual_seed(0 )
             lowercase__      			= 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       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= self.dummy_uncond_unet
             lowercase__      			= ScoreSdeVeScheduler()

             lowercase__      			= ScoreSdeVePipeline(unet=UpperCamelCase   ,						scheduler=UpperCamelCase )
             sde_ve.to(UpperCamelCase )
             sde_ve.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= torch.manual_seed(0 )
             lowercase__      			= sde_ve(num_inference_steps=2   ,						output_type='''numpy'''   ,						generator=UpperCamelCase ).images

             lowercase__      			= torch.manual_seed(0 )
             lowercase__      			= sde_ve(num_inference_steps=2   ,						output_type='''numpy'''   ,						generator=UpperCamelCase   ,						return_dict=UpperCamelCase )[
                 0
             ]

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

             assert image.shape == (1, 32, 32, 3)
             lowercase__      			= 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       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             lowercase__      			= '''google/ncsnpp-church-256'''
             lowercase__      			= UNetaDModel.from_pretrained(UpperCamelCase )

             lowercase__      			= ScoreSdeVeScheduler.from_pretrained(UpperCamelCase )

             lowercase__      			= ScoreSdeVePipeline(unet=UpperCamelCase   ,						scheduler=UpperCamelCase )
             sde_ve.to(UpperCamelCase )
             sde_ve.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= torch.manual_seed(0 )
             lowercase__      			= sde_ve(num_inference_steps=10   ,						output_type='''numpy'''   ,						generator=UpperCamelCase ).images

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

             assert image.shape == (1, 256, 256, 3)

             lowercase__      			= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )

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








'''simple docstring'''

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


lowerCamelCase							:   Tuple  						=	logging.get_logger(__name__)

lowerCamelCase							:   Dict  						=	{
    'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Tuple  					=							"""cvt"""



      def __init__(self    :    int   ,						UpperCamelCase    :    List[Any]=3   ,						UpperCamelCase    :    int=[7, 3, 3]   ,						UpperCamelCase    :    str=[4, 2, 2]   ,						UpperCamelCase    :    Dict=[2, 1, 1]   ,						UpperCamelCase    :    Dict=[64, 192, 384]   ,						UpperCamelCase    :    Dict=[1, 3, 6]   ,						UpperCamelCase    :    Dict=[1, 2, 10]   ,						UpperCamelCase    :    Any=[4.0, 4.0, 4.0]   ,						UpperCamelCase    :    Optional[int]=[0.0, 0.0, 0.0]   ,						UpperCamelCase    :    Optional[int]=[0.0, 0.0, 0.0]   ,						UpperCamelCase    :    int=[0.0, 0.0, 0.1]   ,						UpperCamelCase    :    Any=[True, True, True]   ,						UpperCamelCase    :    int=[False, False, True]   ,						UpperCamelCase    :    Union[str, Any]=["dw_bn", "dw_bn", "dw_bn"]   ,						UpperCamelCase    :    Optional[int]=[3, 3, 3]   ,						UpperCamelCase    :    Tuple=[1, 1, 1]   ,						UpperCamelCase    :    Any=[2, 2, 2]   ,						UpperCamelCase    :    Dict=[1, 1, 1]   ,						UpperCamelCase    :    List[str]=[1, 1, 1]   ,						UpperCamelCase    :    str=0.02   ,						UpperCamelCase    :    int=1E-12   ,						**UpperCamelCase    :    Union[str, Any]   ,						):

             '''simple docstring'''







             super().__init__(**UpperCamelCase )
             lowercase__      			= num_channels
             lowercase__      			= patch_sizes
             lowercase__      			= patch_stride
             lowercase__      			= patch_padding
             lowercase__      			= embed_dim
             lowercase__      			= num_heads
             lowercase__      			= depth
             lowercase__      			= mlp_ratio
             lowercase__      			= attention_drop_rate
             lowercase__      			= drop_rate
             lowercase__      			= drop_path_rate
             lowercase__      			= qkv_bias
             lowercase__      			= cls_token
             lowercase__      			= qkv_projection_method
             lowercase__      			= kernel_qkv
             lowercase__      			= padding_kv
             lowercase__      			= stride_kv
             lowercase__      			= padding_q
             lowercase__      			= stride_q
             lowercase__      			= initializer_range
             lowercase__      			= layer_norm_eps




