
import copy

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


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

_UpperCAmelCase	:     str		       = {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}







class 				lowercase       ( _snake_case    ):
				__SCREAMING_SNAKE_CASE      :				Dict     =    'deta'
				__SCREAMING_SNAKE_CASE      :				List[str]     =    {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}




				def __init__(      self   ,     snake_case=None   ,     snake_case=900   ,     snake_case=2048   ,     snake_case=6   ,     snake_case=2048   ,     snake_case=8   ,     snake_case=6   ,     snake_case=1024   ,     snake_case=8   ,     snake_case=0.0   ,     snake_case=True   ,     snake_case="relu"   ,     snake_case=256   ,     snake_case=0.1   ,     snake_case=0.0   ,     snake_case=0.0   ,     snake_case=0.02   ,     snake_case=1.0   ,     snake_case=True   ,     snake_case=False   ,     snake_case="sine"   ,     snake_case=5   ,     snake_case=4   ,     snake_case=4   ,     snake_case=True   ,     snake_case=300   ,     snake_case=True   ,     snake_case=True   ,     snake_case=1   ,     snake_case=5   ,     snake_case=2   ,     snake_case=1   ,     snake_case=1   ,     snake_case=5   ,     snake_case=2   ,     snake_case=0.1   ,     snake_case=0.25   ,     **snake_case   ,     ):

											if backbone_config is None:
																		logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
																		snake_case_           =					CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
											else:
																		if isinstance(snake_case   ,     snake_case ):
																									snake_case_           =					backbone_config.pop('model_type' )
																									snake_case_           =					CONFIG_MAPPING[backbone_model_type]
																									snake_case_           =					config_class.from_dict(snake_case )

											snake_case_           =					backbone_config
											snake_case_           =					num_queries
											snake_case_           =					max_position_embeddings
											snake_case_           =					d_model
											snake_case_           =					encoder_ffn_dim
											snake_case_           =					encoder_layers
											snake_case_           =					encoder_attention_heads
											snake_case_           =					decoder_ffn_dim
											snake_case_           =					decoder_layers
											snake_case_           =					decoder_attention_heads
											snake_case_           =					dropout
											snake_case_           =					attention_dropout
											snake_case_           =					activation_dropout
											snake_case_           =					activation_function
											snake_case_           =					init_std
											snake_case_           =					init_xavier_std
											snake_case_           =					encoder_layerdrop
											snake_case_           =					auxiliary_loss
											snake_case_           =					position_embedding_type
											# deformable attributes
											snake_case_           =					num_feature_levels
											snake_case_           =					encoder_n_points
											snake_case_           =					decoder_n_points
											snake_case_           =					two_stage
											snake_case_           =					two_stage_num_proposals
											snake_case_           =					with_box_refine
											snake_case_           =					assign_first_stage
											if two_stage is True and with_box_refine is False:
																		raise ValueError('If two_stage is True, with_box_refine must be True.' )
											# Hungarian matcher
											snake_case_           =					class_cost
											snake_case_           =					bbox_cost
											snake_case_           =					giou_cost
											# Loss coefficients
											snake_case_           =					mask_loss_coefficient
											snake_case_           =					dice_loss_coefficient
											snake_case_           =					bbox_loss_coefficient
											snake_case_           =					giou_loss_coefficient
											snake_case_           =					eos_coefficient
											snake_case_           =					focal_alpha
											super().__init__(is_encoder_decoder=snake_case   ,     **snake_case )




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




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




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					self.backbone_config.to_dict()
											snake_case_           =					self.__class__.model_type
											return output







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 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Union[List[PIL.Image.Image], np.ndarray]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]


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




