
'''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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  IFInpaintingSuperResolutionPipeline
						_lowercase						: str										=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
						_lowercase						: List[str]										=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
						_lowercase						: List[str]										=  PipelineTesterMixin.required_optional_params - {'''latents'''}




						def    _lowercase      (   self ):

												"""simple docstring"""



												return self._get_superresolution_dummy_components()




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=0 ):

												"""simple docstring"""



												if str(_lowercase ).startswith("""mps""" ):
																		_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												else:
																		_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(_lowercase )

												_lowerCAmelCase    		=					floats_tensor((1, 3, 16, 16)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )

												_lowerCAmelCase    		=					{
												    """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    _lowercase      (   self ):

												"""simple docstring"""



												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )




						def    _lowercase      (   self ):

												"""simple docstring"""



												self._test_save_load_optional_components()




						@unittest.skipIf(torch_device != """cuda"""       ,  reason="""float16 requires CUDA""" )
						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )




						def    _lowercase      (   self ):

												"""simple docstring"""



												self._test_save_load_local()




						def    _lowercase      (   self ):

												"""simple docstring"""



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







import copy
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 ..auto import CONFIG_MAPPING


a__: Optional[Any]							   =						logging.get_logger(__name__)

a__: Any							   =						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      '''conditional_detr'''
 __SCREAMING_SNAKE_CASE							=      ['''past_key_values''']
 __SCREAMING_SNAKE_CASE							=      {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }



 def __init__(				self,__lowerCamelCase=True,__lowerCamelCase=None,__lowerCamelCase=3,__lowerCamelCase=300,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=256,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.02,__lowerCamelCase=1.0,__lowerCamelCase=False,__lowerCamelCase="sine",__lowerCamelCase="resnet50",__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=1,__lowerCamelCase=1,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=0.25,**__lowerCamelCase,):
        if backbone_config is not None and use_timm_backbone:
               raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''					)

        if not use_timm_backbone:
               if backbone_config is None:
                      logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''					)
                      A__		   =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
               elif isinstance(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		backbone_config.get('''model_type'''					)
                      A__		   =		CONFIG_MAPPING[backbone_model_type]
                      A__		   =		config_class.from_dict(__lowerCamelCase					)

        A__		   =		use_timm_backbone
        A__		   =		backbone_config
        A__		   =		num_channels
        A__		   =		num_queries
        A__		   =		d_model
        A__		   =		encoder_ffn_dim
        A__		   =		encoder_layers
        A__		   =		encoder_attention_heads
        A__		   =		decoder_ffn_dim
        A__		   =		decoder_layers
        A__		   =		decoder_attention_heads
        A__		   =		dropout
        A__		   =		attention_dropout
        A__		   =		activation_dropout
        A__		   =		activation_function
        A__		   =		init_std
        A__		   =		init_xavier_std
        A__		   =		encoder_layerdrop
        A__		   =		decoder_layerdrop
        A__		   =		encoder_layers
        A__		   =		auxiliary_loss
        A__		   =		position_embedding_type
        A__		   =		backbone
        A__		   =		use_pretrained_backbone
        A__		   =		dilation
        # Hungarian matcher
        A__		   =		class_cost
        A__		   =		bbox_cost
        A__		   =		giou_cost
        # Loss coefficients
        A__		   =		mask_loss_coefficient
        A__		   =		dice_loss_coefficient
        A__		   =		cls_loss_coefficient
        A__		   =		bbox_loss_coefficient
        A__		   =		giou_loss_coefficient
        A__		   =		focal_alpha
        super().__init__(is_encoder_decoder=__lowerCamelCase,**__lowerCamelCase					)



 @property
 def 							UpperCamelCase     (				self					):
        return self.encoder_attention_heads



 @property
 def 							UpperCamelCase     (				self					):
        return self.d_model




 def 							UpperCamelCase     (				self					):
        A__		   =		copy.deepcopy(self.__dict__					)
        if self.backbone_config is not None:
               A__		   =		self.backbone_config.to_dict()
        A__		   =		self.__class__.model_type
        return output




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      version.parse('''1.11''' )



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



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




 @property
 def 							UpperCamelCase     (				self					):
        return 12





