






'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCamelCase      					=logging.get_logger(__name__)

UpperCamelCase      					={
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Tuple			  =    '''levit'''
 def __init__(				self      ,  __lowerCAmelCase=2_24      ,  __lowerCAmelCase=3      ,  __lowerCAmelCase=3      ,  __lowerCAmelCase=2      ,  __lowerCAmelCase=1      ,  __lowerCAmelCase=16      ,  __lowerCAmelCase=[1_28, 2_56, 3_84]      ,  __lowerCAmelCase=[4, 8, 12]      ,  __lowerCAmelCase=[4, 4, 4]      ,  __lowerCAmelCase=[16, 16, 16]      ,  __lowerCAmelCase=0      ,  __lowerCAmelCase=[2, 2, 2]      ,  __lowerCAmelCase=[2, 2, 2]      ,  __lowerCAmelCase=0.02      ,  **__lowerCAmelCase      ,  ):
       super().__init__(**__lowerCAmelCase			)
       UpperCamelCase_					: Union[str, Any]            =				image_size
       UpperCamelCase_					: str            =				num_channels
       UpperCamelCase_					: Tuple            =				kernel_size
       UpperCamelCase_					: Dict            =				stride
       UpperCamelCase_					: int            =				padding
       UpperCamelCase_					: Optional[int]            =				hidden_sizes
       UpperCamelCase_					: Optional[Any]            =				num_attention_heads
       UpperCamelCase_					: List[Any]            =				depths
       UpperCamelCase_					: Dict            =				key_dim
       UpperCamelCase_					: Optional[Any]            =				drop_path_rate
       UpperCamelCase_					: List[str]            =				patch_size
       UpperCamelCase_					: Optional[Any]            =				attention_ratio
       UpperCamelCase_					: Any            =				mlp_ratio
       UpperCamelCase_					: Optional[Any]            =				initializer_range
       UpperCamelCase_					: Dict            =				[
           ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    str			  =    version.parse('''1.11'''    )
 @property
 def 	_UpperCAmelCase							(				self			):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]			)





 @property
 def 	_UpperCAmelCase							(				self			):
       return 1E-4














'''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    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: 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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_uncond_unet
       UpperCamelCase_					: List[str]            =				PNDMScheduler()

       UpperCamelCase_					: Dict            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: int            =				image_from_tuple[0, -3:, -3:, -1]

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






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				"""google/ddpm-cifar10-32"""

       UpperCamelCase_					: List[str]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: int            =				PNDMScheduler()

       UpperCamelCase_					: Any            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Tuple            =				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Union[str, Any]            =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

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






