





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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A_        =  logging.get_logger(__name__)

A_        =  {
    "facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}

class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     "convnext"





					def __init__(			self						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=4						,	_lowerCAmelCase=None						,	_lowerCAmelCase=None						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=1E-12						,	_lowerCAmelCase=1E-6						,	_lowerCAmelCase=0.0						,	_lowerCAmelCase=224						,	_lowerCAmelCase=None						,	_lowerCAmelCase=None						,	**_lowerCAmelCase						,	):
										super().__init__(**_lowerCAmelCase						)

										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							patch_size
										lowerCamelCase__      =							num_stages
										lowerCamelCase__      =							[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
										lowerCamelCase__      =							[3, 3, 9, 3] if depths is None else depths
										lowerCamelCase__      =							hidden_act
										lowerCamelCase__      =							initializer_range
										lowerCamelCase__      =							layer_norm_eps
										lowerCamelCase__      =							layer_scale_init_value
										lowerCamelCase__      =							drop_path_rate
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							["stem"] + [F"stage{idx}" for idx in range(1						,	len(self.depths						) + 1						)]
										lowerCamelCase__     ,		lowerCamelCase__      =							get_aligned_output_features_output_indices(
										    out_features=_lowerCAmelCase						,	out_indices=_lowerCAmelCase						,	stage_names=self.stage_names						)

class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     version.parse("1.11" )





					@property
					def        __magic_name__			(			self						):
										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ]						)






					@property
					def        __magic_name__			(			self						):
										return 1E-5








import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@property
					def        __magic_name__			(			self						):
										torch.manual_seed(0						)
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler()

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						,	return_dict=_lowerCAmelCase						)[0]

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array(
										    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]						)

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






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler(prediction_type="sample"						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						)[0]

										lowerCamelCase__      =							image[0, -3:, -3:, -1]
										lowerCamelCase__      =							image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							1E-2 if torch_device != "mps" else 3E-2
										assert np.abs(image_slice.flatten() - image_eps_slice.flatten()						).max() < tolerance

@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"google/ddpm-cifar10-32"

										lowerCamelCase__      =							UNetaDModel.from_pretrained(_lowerCAmelCase						)
										lowerCamelCase__      =							DDPMScheduler.from_pretrained(_lowerCAmelCase						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	output_type="numpy"						).images

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]						)
										assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2

