"""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
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        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase     ):
				A__				:       List[str]  							=	'''convnext'''

				def __init__(				self     ,			_lowerCamelCase=3     ,			_lowerCamelCase=4     ,			_lowerCamelCase=4     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=0.0_2     ,			_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        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       int  							=	version.parse('''1.11'''     )

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






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



"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	IFSuperResolutionPipeline
				A__				:       Tuple  							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				A__				:       Optional[int]  							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				A__				:       List[Any]  							=	PipelineTesterMixin.required_optional_params - {'''latents'''}

				def 		_a     (				self				):
										return self._get_superresolution_dummy_components()

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

										lowerCamelCase__ ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs

				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()     ,			reason="XFormers attention is only available with CUDA and `xformers` installed"     ,			)
				def 		_a     (				self				):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

				def 		_a     (				self				):
										self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
				def 		_a     (				self				):
										# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
										super().test_save_load_floataa(expected_max_diff=1E-1				)

				def 		_a     (				self				):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

				def 		_a     (				self				):
										self._test_save_load_local()






				def 		_a     (				self				):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,			)


