





import warnings
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									=				logging.get_logger(__name__)

__A									=				{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			"segformer"

 def __init__(self				:   str       , UpperCAmelCase_				:   Union[str, Any]=3       , UpperCAmelCase_				:   List[str]=4       , UpperCAmelCase_				:   Any=[2, 2, 2, 2]       , UpperCAmelCase_				:   Dict=[8, 4, 2, 1]       , UpperCAmelCase_				:   Any=[32, 64, 160, 256]       , UpperCAmelCase_				:   Dict=[7, 3, 3, 3]       , UpperCAmelCase_				:   Any=[4, 2, 2, 2]       , UpperCAmelCase_				:   str=[1, 2, 5, 8]       , UpperCAmelCase_				:   Optional[Any]=[4, 4, 4, 4]       , UpperCAmelCase_				:   Optional[Any]="gelu"       , UpperCAmelCase_				:   Any=0.0       , UpperCAmelCase_				:   Any=0.0       , UpperCAmelCase_				:   List[str]=0.1       , UpperCAmelCase_				:   Any=0.02       , UpperCAmelCase_				:   int=0.1       , UpperCAmelCase_				:   Union[str, Any]=1E-6       , UpperCAmelCase_				:   List[str]=256       , UpperCAmelCase_				:   Union[str, Any]=255       , **UpperCAmelCase_				:   Any       , )       ->List[str]:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)

   if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
     warnings.warn(
         "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
         " removed, as the behaviour will default to that of reshape_last_stage = True."       , UpperCAmelCase_       , )

   lowerCamelCase__:      List[str]			  =num_channels
   lowerCamelCase__:      int			  =num_encoder_blocks
   lowerCamelCase__:      Union[str, Any]			  =depths
   lowerCamelCase__:      List[Any]			  =sr_ratios
   lowerCamelCase__:      Union[str, Any]			  =hidden_sizes
   lowerCamelCase__:      Optional[int]			  =patch_sizes
   lowerCamelCase__:      str			  =strides
   lowerCamelCase__:      List[str]			  =mlp_ratios
   lowerCamelCase__:      Tuple			  =num_attention_heads
   lowerCamelCase__:      Optional[Any]			  =hidden_act
   lowerCamelCase__:      Dict			  =hidden_dropout_prob
   lowerCamelCase__:      str			  =attention_probs_dropout_prob
   lowerCamelCase__:      List[str]			  =classifier_dropout_prob
   lowerCamelCase__:      Tuple			  =initializer_range
   lowerCamelCase__:      List[str]			  =drop_path_rate
   lowerCamelCase__:      Optional[int]			  =layer_norm_eps
   lowerCamelCase__:      Optional[int]			  =decoder_hidden_size
   lowerCamelCase__:      List[Any]			  =kwargs.get("reshape_last_stage"       , UpperCAmelCase_)
   lowerCamelCase__:      List[Any]			  =semantic_loss_ignore_index



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			version.parse("1.11"    )

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



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

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->float:



   '''simple docstring'''



   return 1E-4







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



   '''simple docstring'''



   return 12





'''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 							__UpperCamelCase				(		unittest.TestCase					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       Dict					    =  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
						__a			:       str					    =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,				revision='onnx'						,				safety_checker=__a						,				feature_extractor=__a						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 1E-2



