
"""simple docstring"""





from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)






@dataclass
class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				42
		lowercase__      =				42
		lowercase__      =				42


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .pipeline_if import IFPipeline
				from .pipeline_if_imgaimg import IFImgaImgPipeline
				from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
				from .pipeline_if_inpainting import IFInpaintingPipeline
				from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
				from .pipeline_if_superresolution import IFSuperResolutionPipeline
				from .safety_checker import IFSafetyChecker
				from .watermark import IFWatermarker








"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_a														=		logging.get_logger(__name__)

_a														=		{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'detr'
		lowercase__      =				['past_key_values']
		lowercase__      =				{
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}



		def __init__( self    ,		__a=True    ,		__a=None    ,		__a=3    ,		__a=1_00    ,		__a=6    ,		__a=20_48    ,		__a=8    ,		__a=6    ,		__a=20_48    ,		__a=8    ,		__a=0.0    ,		__a=0.0    ,		__a=True    ,		__a="relu"    ,		__a=2_56    ,		__a=0.1    ,		__a=0.0    ,		__a=0.0    ,		__a=0.02    ,		__a=1.0    ,		__a=False    ,		__a="sine"    ,		__a="resnet50"    ,		__a=True    ,		__a=False    ,		__a=1    ,		__a=5    ,		__a=2    ,		__a=1    ,		__a=1    ,		__a=5    ,		__a=2    ,		__a=0.1    ,		**__a    ,		)	->	Tuple:






							'''simple docstring'''
							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.''')
																	_UpperCamelCase										=  CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
												elif isinstance(__a    ,		__a):
																	_UpperCamelCase										=  backbone_config.get('''model_type''')
																	_UpperCamelCase										=  CONFIG_MAPPING[backbone_model_type]
																	_UpperCamelCase										=  config_class.from_dict(__a)
												# set timm attributes to None
												_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  None, None, None

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



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.encoder_attention_heads



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.d_model



		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	Any:






							'''simple docstring'''
							return cls(backbone_config=__a    ,		**__a)




		def 						UpperCAmelCase							( self)	->	Dict[str, any]:






							'''simple docstring'''
							_UpperCamelCase										=  copy.deepcopy(self.__dict__)
							if output["backbone_config"] is not None:
												_UpperCamelCase										=  self.backbone_config.to_dict()
							_UpperCamelCase										=  self.__class__.model_type
							return output






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				version.parse('1.11'     )



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






							'''simple docstring'''
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							        ('''pixel_mask''', {0: '''batch'''}),
							    ])



		@property
		def 						UpperCAmelCase							( self)	->	float:






							'''simple docstring'''
							return 1e-5




		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return 12






