





'''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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''detr'''
    UpperCamelCase     = ['''past_key_values''']
    UpperCamelCase     = {
        '''hidden_size''': '''d_model''',
        '''num_attention_heads''': '''encoder_attention_heads''',
    }


    def __init__(		self ,		A=True ,		A=None ,		A=3 ,		A=100 ,		A=6 ,		A=2048 ,		A=8 ,		A=6 ,		A=2048 ,		A=8 ,		A=0.0 ,		A=0.0 ,		A=True ,		A="relu" ,		A=256 ,		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 ,		)		->   List[str]:
        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."""       )
                _SCREAMING_SNAKE_CASE									=			CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]       )
            elif isinstance(A ,		A       ):
                _SCREAMING_SNAKE_CASE									=			backbone_config.get("""model_type"""       )
                _SCREAMING_SNAKE_CASE									=			CONFIG_MAPPING[backbone_model_type]
                _SCREAMING_SNAKE_CASE									=			config_class.from_dict(A       )
            # set timm attributes to None
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			None, None, None

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


    @property
    def 			snake_case_(		self       )		->   int:
        return self.encoder_attention_heads


    @property
    def 			snake_case_(		self       )		->   int:
        return self.d_model


    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   int:
        return cls(backbone_config=A ,		**A       )





    def 			snake_case_(		self       )		->   Dict[str, any]:
        _SCREAMING_SNAKE_CASE									=			copy.deepcopy(self.__dict__       )
        if output["backbone_config"] is not None:
            _SCREAMING_SNAKE_CASE									=			self.backbone_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.__class__.model_type
        return output




class 					a_     (				snake_case_       ):

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


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


    @property
    def 			snake_case_(		self       )		->   float:
        return 1e-5





    @property
    def 			snake_case_(		self       )		->   int:
        return 12










'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline



