


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


_SCREAMING_SNAKE_CASE      :			Any      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'conditional_detr'
    __magic_name__        =		['past_key_values']
    __magic_name__        =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(	self      , __snake_case=True      , __snake_case=None      , __snake_case=3      , __snake_case=3_0_0      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=True      , __snake_case="relu"      , __snake_case=2_5_6      , __snake_case=0.1      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.02      , __snake_case=1.0      , __snake_case=False      , __snake_case="sine"      , __snake_case="resnet50"      , __snake_case=True      , __snake_case=False      , __snake_case=2      , __snake_case=5      , __snake_case=2      , __snake_case=1      , __snake_case=1      , __snake_case=2      , __snake_case=5      , __snake_case=2      , __snake_case=0.25      , **__snake_case      , ):
         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.'''   )
                   snake_case					=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
              elif isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    backbone_config.get('''model_type'''   )
                   snake_case					=    CONFIG_MAPPING[backbone_model_type]
                   snake_case					=    config_class.from_dict(__snake_case   )

         snake_case					=    use_timm_backbone
         snake_case					=    backbone_config
         snake_case					=    num_channels
         snake_case					=    num_queries
         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					=    decoder_layerdrop
         snake_case					=    encoder_layers
         snake_case					=    auxiliary_loss
         snake_case					=    position_embedding_type
         snake_case					=    backbone
         snake_case					=    use_pretrained_backbone
         snake_case					=    dilation
         # 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					=    cls_loss_coefficient
         snake_case					=    bbox_loss_coefficient
         snake_case					=    giou_loss_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__   )
         if self.backbone_config is not None:
              snake_case					=    self.backbone_config.to_dict()
         snake_case					=    self.__class__.model_type
         return output






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]   )
    @property
    def 		a_      (	self   ):
         return 1E-5




    @property
    def 		a_      (	self   ):
         return 1_2








from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


_SCREAMING_SNAKE_CASE      :			List[str]      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'efficientnet'
    def __init__(	self      , __snake_case = 3      , __snake_case = 6_0_0      , __snake_case = 2.0      , __snake_case = 3.1      , __snake_case = 8      , __snake_case = [3, 3, 5, 3, 5, 5, 3]      , __snake_case = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]      , __snake_case = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]      , __snake_case = []      , __snake_case = [1, 2, 2, 2, 1, 2, 1]      , __snake_case = [1, 2, 2, 3, 3, 4, 1]      , __snake_case = [1, 6, 6, 6, 6, 6, 6]      , __snake_case = 0.25      , __snake_case = "swish"      , __snake_case = 2_5_6_0      , __snake_case = "mean"      , __snake_case = 0.02      , __snake_case = 0.001      , __snake_case = 0.99      , __snake_case = 0.5      , __snake_case = 0.2      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    num_channels
         snake_case					=    image_size
         snake_case					=    width_coefficient
         snake_case					=    depth_coefficient
         snake_case					=    depth_divisor
         snake_case					=    kernel_sizes
         snake_case					=    in_channels
         snake_case					=    out_channels
         snake_case					=    depthwise_padding
         snake_case					=    strides
         snake_case					=    num_block_repeats
         snake_case					=    expand_ratios
         snake_case					=    squeeze_expansion_ratio
         snake_case					=    hidden_act
         snake_case					=    hidden_dim
         snake_case					=    pooling_type
         snake_case					=    initializer_range
         snake_case					=    batch_norm_eps
         snake_case					=    batch_norm_momentum
         snake_case					=    dropout_rate
         snake_case					=    drop_connect_rate
         snake_case					=    sum(__snake_case   ) * 4






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]   )




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




