

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}



class  __lowerCAmelCase	(      UpperCamelCase__					):
			lowerCamelCase_					:					str	      =				"""efficientnet"""






			def __init__(self ,							__magic_name__ = 3 ,							__magic_name__ = 600 ,							__magic_name__ = 2.0 ,							__magic_name__ = 3.1 ,							__magic_name__ = 8 ,							__magic_name__ = [3, 3, 5, 3, 5, 5, 3] ,							__magic_name__ = [32, 16, 24, 40, 80, 112, 192] ,							__magic_name__ = [16, 24, 40, 80, 112, 192, 320] ,							__magic_name__ = [] ,							__magic_name__ = [1, 2, 2, 2, 1, 2, 1] ,							__magic_name__ = [1, 2, 2, 3, 3, 4, 1] ,							__magic_name__ = [1, 6, 6, 6, 6, 6, 6] ,							__magic_name__ = 0.25 ,							__magic_name__ = "swish" ,							__magic_name__ = 2560 ,							__magic_name__ = "mean" ,							__magic_name__ = 0.02 ,							__magic_name__ = 0.001 ,							__magic_name__ = 0.99 ,							__magic_name__ = 0.5 ,							__magic_name__ = 0.2 ,							**__magic_name__ ,							)					->       Union[str, Any]:


							'''simple docstring'''



							super().__init__(**__a   )

							snake_case_						:   Any           =  num_channels
							snake_case_						:   Any           =  image_size
							snake_case_						:   Union[str, Any]           =  width_coefficient
							snake_case_						:   List[Any]           =  depth_coefficient
							snake_case_						:   Union[str, Any]           =  depth_divisor
							snake_case_						:   Dict           =  kernel_sizes
							snake_case_						:   Optional[int]           =  in_channels
							snake_case_						:   Any           =  out_channels
							snake_case_						:   List[str]           =  depthwise_padding
							snake_case_						:   Any           =  strides
							snake_case_						:   str           =  num_block_repeats
							snake_case_						:   Union[str, Any]           =  expand_ratios
							snake_case_						:   List[str]           =  squeeze_expansion_ratio
							snake_case_						:   List[str]           =  hidden_act
							snake_case_						:   Dict           =  hidden_dim
							snake_case_						:   List[str]           =  pooling_type
							snake_case_						:   str           =  initializer_range
							snake_case_						:   List[str]           =  batch_norm_eps
							snake_case_						:   Any           =  batch_norm_momentum
							snake_case_						:   Dict           =  dropout_rate
							snake_case_						:   Union[str, Any]           =  drop_connect_rate
							snake_case_						:   Dict           =  sum(__a   ) * 4




class  __lowerCAmelCase	(      UpperCamelCase__					):
			lowerCamelCase_					:					Any	      =				version.parse('''1.11'''					)






			@property
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]   )






			@property
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							return 1e-5







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  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   int           =  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       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.dummy_uncond_unet
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Optional[Any]           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   Dict           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy'''   ).images

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy''' ,							return_dict=__magic_name__   )[0]

							snake_case_						:   Any           =  image[0, -3:, -3:, -1]
							snake_case_						:   str           =  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  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  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''google/ddpm-cifar10-32'''

							snake_case_						:   Tuple           =  UNetaDModel.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Any           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )
							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Tuple           =  pndm(generator=__magic_name__ ,							output_type='''numpy'''   ).images

							snake_case_						:   List[str]           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

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