



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_   :str        =      logging.get_logger(__name__)

a_   :Tuple        =      {
    "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 					snake_case__						(       _SCREAMING_SNAKE_CASE     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"segformer"






     def __init__(       self  :  List[Any], _snake_case  :  List[Any]=3, _snake_case  :  Dict=4, _snake_case  :  Union[str, Any]=[2, 2, 2, 2], _snake_case  :  Union[str, Any]=[8, 4, 2, 1], _snake_case  :  List[str]=[3_2, 6_4, 1_6_0, 2_5_6], _snake_case  :  Union[str, Any]=[7, 3, 3, 3], _snake_case  :  List[str]=[4, 2, 2, 2], _snake_case  :  Optional[int]=[1, 2, 5, 8], _snake_case  :  str=[4, 4, 4, 4], _snake_case  :  Union[str, Any]="gelu", _snake_case  :  Dict=0.0, _snake_case  :  Any=0.0, _snake_case  :  Optional[int]=0.1, _snake_case  :  Tuple=0.0_2, _snake_case  :  Dict=0.1, _snake_case  :  Dict=1e-6, _snake_case  :  List[Any]=2_5_6, _snake_case  :  Dict=2_5_5, **_snake_case  :  Union[str, Any], )   ->Any:
            super().__init__(**A_					)

            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.', A_, )

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




class 					snake_case__						(       _SCREAMING_SNAKE_CASE     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	version.parse("""1.11"""     )






     @property
     def    lowercase_   (       self  :  List[Any]					)   ->Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ]					)






     @property
     def    lowercase_   (       self  :  str					)   ->float:
            return 1e-4






     @property
     def    lowercase_   (       self  :  Optional[Any]					)   ->int:
            return 1_2








import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )

				UpperCamelCase		=     {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'mask_image': mask_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 __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# 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 __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
