'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							IFInpaintingSuperResolutionPipeline
				UpperCAmelCase__							=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
				UpperCAmelCase__							=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}	)
				UpperCAmelCase__							=							PipelineTesterMixin.required_optional_params - {'''latents'''}





				def  snake_case__				(						self					:						Optional[int]					)							->Union[str, Any]:


										'''simple docstring'''




										return self._get_superresolution_dummy_components()





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						List[str]       ,		lowercase__					:						str=0					)							->Tuple:


										'''simple docstring'''




										if str(lowercase__					).startswith("mps"					):
																_UpperCamelCase			:			Any   			=						torch.manual_seed(lowercase__					)
										else:
																_UpperCamelCase			:			List[Any]   			=						torch.Generator(device=lowercase__					).manual_seed(lowercase__					)

										_UpperCamelCase			:			List[str]   			=						floats_tensor((1, 3, 16, 16)       ,		rng=random.Random(lowercase__					)					).to(lowercase__					)
										_UpperCamelCase			:			Optional[Any]   			=						floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(lowercase__					)					).to(lowercase__					)
										_UpperCamelCase			:			Optional[int]   			=						floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(lowercase__					)					).to(lowercase__					)

										_UpperCamelCase			:			int   			=						{
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "original_image": original_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  snake_case__				(						self					:						List[str]					)							->List[str]:


										'''simple docstring'''




										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)





				def  snake_case__				(						self					:						Tuple					)							->Tuple:


										'''simple docstring'''




										self._test_save_load_optional_components()





				@unittest.skipIf(torch_device != "cuda"       ,		reason="float16 requires CUDA"					)
				def  snake_case__				(						self					:						Dict					)							->str:


										'''simple docstring'''




										super().test_save_load_floataa(expected_max_diff=1e-1					)





				def  snake_case__				(						self					:						Any					)							->List[Any]:


										'''simple docstring'''




										self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)





				def  snake_case__				(						self					:						Dict					)							->List[Any]:


										'''simple docstring'''




										self._test_save_load_local()







				def  snake_case__				(						self					:						Optional[int]					)							->Optional[int]:


										'''simple docstring'''




										self._test_inference_batch_single_identical(
										    expected_max_diff=1e-2       ,		)



'''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


lowerCAmelCase_	:		Union[str, Any]			=     logging.get_logger(__name__)

lowerCAmelCase_	:		List[str]			=     {
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''yolos'''





				def __init__(						self					:						int       ,		lowercase__					:						List[str]=768       ,		lowercase__					:						Optional[Any]=12       ,		lowercase__					:						Union[str, Any]=12       ,		lowercase__					:						Any=3_072       ,		lowercase__					:						List[Any]="gelu"       ,		lowercase__					:						Dict=0.0       ,		lowercase__					:						Any=0.0       ,		lowercase__					:						Dict=0.0_2       ,		lowercase__					:						Tuple=1e-12       ,		lowercase__					:						str=[512, 864]       ,		lowercase__					:						Dict=16       ,		lowercase__					:						int=3       ,		lowercase__					:						Optional[Any]=True       ,		lowercase__					:						List[Any]=100       ,		lowercase__					:						str=True       ,		lowercase__					:						str=False       ,		lowercase__					:						List[str]=1       ,		lowercase__					:						Dict=5       ,		lowercase__					:						str=2       ,		lowercase__					:						Optional[int]=5       ,		lowercase__					:						Optional[int]=2       ,		lowercase__					:						Optional[Any]=0.1       ,		**lowercase__					:						Union[str, Any]       ,		)							->Tuple:


										'''simple docstring'''




										super().__init__(**lowercase__					)

										_UpperCamelCase			:			Optional[int]   			=						hidden_size
										_UpperCamelCase			:			str   			=						num_hidden_layers
										_UpperCamelCase			:			Optional[Any]   			=						num_attention_heads
										_UpperCamelCase			:			Optional[int]   			=						intermediate_size
										_UpperCamelCase			:			Union[str, Any]   			=						hidden_act
										_UpperCamelCase			:			Tuple   			=						hidden_dropout_prob
										_UpperCamelCase			:			Any   			=						attention_probs_dropout_prob
										_UpperCamelCase			:			Optional[int]   			=						initializer_range
										_UpperCamelCase			:			str   			=						layer_norm_eps
										_UpperCamelCase			:			Optional[int]   			=						image_size
										_UpperCamelCase			:			int   			=						patch_size
										_UpperCamelCase			:			Dict   			=						num_channels
										_UpperCamelCase			:			List[str]   			=						qkv_bias
										_UpperCamelCase			:			Dict   			=						num_detection_tokens
										_UpperCamelCase			:			int   			=						use_mid_position_embeddings
										_UpperCamelCase			:			int   			=						auxiliary_loss
										# Hungarian matcher
										_UpperCamelCase			:			Tuple   			=						class_cost
										_UpperCamelCase			:			str   			=						bbox_cost
										_UpperCamelCase			:			str   			=						giou_cost
										# Loss coefficients
										_UpperCamelCase			:			List[str]   			=						bbox_loss_coefficient
										_UpperCamelCase			:			str   			=						giou_loss_coefficient
										_UpperCamelCase			:			str   			=						eos_coefficient





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							version.parse('''1.11'''	)





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


										'''simple docstring'''




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





				@property
				def  snake_case__				(						self					:						Any					)							->float:


										'''simple docstring'''




										return 1e-4







				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return 12


