
'''simple docstring'''


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


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

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


class     lowercase__						(	UpperCamelCase__ ):






   '''simple docstring'''







   _snake_case		 =				'''conditional_detr'''
   _snake_case		 =				['''past_key_values''']
   _snake_case		 =				{
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }







   def __init__(      self			, lowerCamelCase__=True			, lowerCamelCase__=None			, lowerCamelCase__=3			, lowerCamelCase__=3_0_0			, lowerCamelCase__=6			, lowerCamelCase__=2_0_4_8			, lowerCamelCase__=8			, lowerCamelCase__=6			, lowerCamelCase__=2_0_4_8			, lowerCamelCase__=8			, lowerCamelCase__=0.0			, lowerCamelCase__=0.0			, lowerCamelCase__=True			, lowerCamelCase__="relu"			, lowerCamelCase__=2_5_6			, 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__			, ):

      '''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(__lowerCamelCase			, __lowerCamelCase ):
            UpperCamelCase	  =	backbone_config.get('''model_type''' )
            UpperCamelCase	  =	CONFIG_MAPPING[backbone_model_type]
            UpperCamelCase	  =	config_class.from_dict(__lowerCamelCase )

      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	  =	cls_loss_coefficient
      UpperCamelCase	  =	bbox_loss_coefficient
      UpperCamelCase	  =	giou_loss_coefficient
      UpperCamelCase	  =	focal_alpha
      super().__init__(is_encoder_decoder=__lowerCamelCase			, **__lowerCamelCase )







   @property
   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      return self.encoder_attention_heads







   @property
   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      return self.d_model







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







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




class     lowercase__						(	UpperCamelCase__ ):






   '''simple docstring'''







   _snake_case		 =				version.parse('''1.11''' )







   @property
   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







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







   @property
   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      return 1e-5







   @property
   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      return 1_2












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__: int							   =						{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: List[Any]							   =						[
           'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
           'M2M100ForConditionalGeneration',
           'M2M100Model',
           'M2M100PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
       from .tokenization_mam_aaa import MaMaaaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mam_aaa import (
                  M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaMaaaForConditionalGeneration,
                  MaMaaaModel,
                  MaMaaaPreTrainedModel,
              )


else:
       import sys

       a__: List[str]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





