

"""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


A_							:			Dict											= logging.get_logger(__name__)

A_							:			Optional[Any]											= {
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Any							=							'mobilenet_v2'

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str=3    ,				__UpperCAmelCase				:						str=2_2_4    ,				__UpperCAmelCase				:						Tuple=1.0    ,				__UpperCAmelCase				:						Optional[Any]=8    ,				__UpperCAmelCase				:						Union[str, Any]=8    ,				__UpperCAmelCase				:						str=6    ,				__UpperCAmelCase				:						List[Any]=3_2    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						str=True    ,				__UpperCAmelCase				:						Union[str, Any]="relu6"    ,				__UpperCAmelCase				:						Optional[Any]=True    ,				__UpperCAmelCase				:						List[Any]=0.8    ,				__UpperCAmelCase				:						Optional[Any]=0.02    ,				__UpperCAmelCase				:						int=0.001    ,				__UpperCAmelCase				:						Any=2_5_5    ,				**__UpperCAmelCase				:						List[Any]    ,				)   ->    List[Any]:
						super().__init__(**__UpperCAmelCase       )

						if depth_multiplier <= 0:
									raise ValueError("""depth_multiplier must be greater than zero."""       )

						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      depth_multiplier
						SCREAMING_SNAKE_CASE__								=      depth_divisible_by
						SCREAMING_SNAKE_CASE__								=      min_depth
						SCREAMING_SNAKE_CASE__								=      expand_ratio
						SCREAMING_SNAKE_CASE__								=      output_stride
						SCREAMING_SNAKE_CASE__								=      first_layer_is_expansion
						SCREAMING_SNAKE_CASE__								=      finegrained_output
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      tf_padding
						SCREAMING_SNAKE_CASE__								=      classifier_dropout_prob
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      layer_norm_eps
						SCREAMING_SNAKE_CASE__								=      semantic_loss_ignore_index





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Union[str, Any]							=							version.parse('1.11'			)

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Mapping[str, Mapping[int, str]]:
						return OrderedDict([("""pixel_values""", {0: """batch"""})]       )

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Mapping[str, Mapping[int, str]]:
						if self.task == "image-classification":
									return OrderedDict([("""logits""", {0: """batch"""})]       )
						else:
									return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]       )





			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    float:
						return 1e-4





"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
						import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      ort.SessionOptions()
						SCREAMING_SNAKE_CASE__								=      False
						return options





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""       )

						# using the PNDM scheduler by default
						SCREAMING_SNAKE_CASE__								=      OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""    ,				revision="""onnx"""    ,				safety_checker=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				provider=self.gpu_provider    ,				sess_options=self.gpu_options    ,				)
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      """A red cat sitting on a park bench"""

						SCREAMING_SNAKE_CASE__								=      np.random.RandomState(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(
						    prompt=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				mask_image=__UpperCAmelCase    ,				strength=0.75    ,				guidance_scale=7.5    ,				num_inference_steps=1_5    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image       ).max() < 1e-2


