

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


a_     =			logging.get_logger(__name__)

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






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							='conditional_detr'
			lowerCamelCase__							=['past_key_values']
			lowerCamelCase__							={
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'encoder_attention_heads',
			}
			def __init__(	self							:       List[Any]					,					a							:       List[str]=True					,					a							:       int=None					,					a							:       Optional[int]=3					,					a							:       List[str]=300					,					a							:       Optional[Any]=6					,					a							:       Tuple=2048					,					a							:       Dict=8					,					a							:       Optional[Any]=6					,					a							:       Tuple=2048					,					a							:       List[Any]=8					,					a							:       Tuple=0.0					,					a							:       Optional[Any]=0.0					,					a							:       Tuple=True					,					a							:       List[str]="relu"					,					a							:       List[Any]=256					,					a							:       str=0.1					,					a							:       Optional[Any]=0.0					,					a							:       Optional[int]=0.0					,					a							:       Union[str, Any]=0.02					,					a							:       Tuple=1.0					,					a							:       Dict=False					,					a							:       Optional[Any]="sine"					,					a							:       Optional[int]="resnet50"					,					a							:       Dict=True					,					a							:       str=False					,					a							:       int=2					,					a							:       List[str]=5					,					a							:       str=2					,					a							:       Optional[int]=1					,					a							:       Tuple=1					,					a							:       Optional[Any]=2					,					a							:       Optional[int]=5					,					a							:       List[str]=2					,					a							:       Any=0.25					,					**a							:       Tuple					,					)							->				Dict:

				"""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."		)
						SCREAMING_SNAKE_CASE      :					List[str]    =				CONFIG_MAPPING["resnet"](out_features=["stage4"]		)
					elif isinstance(a					,					a		):
						SCREAMING_SNAKE_CASE      :					List[Any]    =				backbone_config.get("model_type"		)
						SCREAMING_SNAKE_CASE      :					List[str]    =				CONFIG_MAPPING[backbone_model_type]
						SCREAMING_SNAKE_CASE      :					List[Any]    =				config_class.from_dict(a		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				use_timm_backbone
				SCREAMING_SNAKE_CASE      :					Dict    =				backbone_config
				SCREAMING_SNAKE_CASE      :					Tuple    =				num_channels
				SCREAMING_SNAKE_CASE      :					str    =				num_queries
				SCREAMING_SNAKE_CASE      :					List[Any]    =				d_model
				SCREAMING_SNAKE_CASE      :					Tuple    =				encoder_ffn_dim
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				encoder_layers
				SCREAMING_SNAKE_CASE      :					Tuple    =				encoder_attention_heads
				SCREAMING_SNAKE_CASE      :					int    =				decoder_ffn_dim
				SCREAMING_SNAKE_CASE      :					Dict    =				decoder_layers
				SCREAMING_SNAKE_CASE      :					Dict    =				decoder_attention_heads
				SCREAMING_SNAKE_CASE      :					str    =				dropout
				SCREAMING_SNAKE_CASE      :					Any    =				attention_dropout
				SCREAMING_SNAKE_CASE      :					List[Any]    =				activation_dropout
				SCREAMING_SNAKE_CASE      :					List[Any]    =				activation_function
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				init_std
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				init_xavier_std
				SCREAMING_SNAKE_CASE      :					Dict    =				encoder_layerdrop
				SCREAMING_SNAKE_CASE      :					str    =				decoder_layerdrop
				SCREAMING_SNAKE_CASE      :					Any    =				encoder_layers
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				auxiliary_loss
				SCREAMING_SNAKE_CASE      :					Dict    =				position_embedding_type
				SCREAMING_SNAKE_CASE      :					Dict    =				backbone
				SCREAMING_SNAKE_CASE      :					Any    =				use_pretrained_backbone
				SCREAMING_SNAKE_CASE      :					str    =				dilation
				# Hungarian matcher
				SCREAMING_SNAKE_CASE      :					int    =				class_cost
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				bbox_cost
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				giou_cost
				# Loss coefficients
				SCREAMING_SNAKE_CASE      :					List[Any]    =				mask_loss_coefficient
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				dice_loss_coefficient
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				cls_loss_coefficient
				SCREAMING_SNAKE_CASE      :					Dict    =				bbox_loss_coefficient
				SCREAMING_SNAKE_CASE      :					Tuple    =				giou_loss_coefficient
				SCREAMING_SNAKE_CASE      :					List[Any]    =				focal_alpha
				super().__init__(is_encoder_decoder=a					,					**a		)
			@property
			def 							__UpperCamelCase       (	self							:       Dict		)							->				int:

				"""simple docstring"""

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

				"""simple docstring"""

				return self.d_model






			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				copy.deepcopy(self.__dict__		)
				if self.backbone_config is not None:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.backbone_config.to_dict()
				SCREAMING_SNAKE_CASE      :					str    =				self.__class__.model_type
				return output






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							=version.parse('1.11'      )
			@property
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				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							:       List[Any]		)							->				float:

				"""simple docstring"""

				return 1e-5






			@property
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				int:

				"""simple docstring"""

				return 12




from typing import TYPE_CHECKING

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


__UpperCAmelCase          =       {
    '''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
    '''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       [
        '''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AdaptiveEmbedding''',
        '''TransfoXLForSequenceClassification''',
        '''TransfoXLLMHeadModel''',
        '''TransfoXLModel''',
        '''TransfoXLPreTrainedModel''',
        '''load_tf_weights_in_transfo_xl''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       [
        '''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFAdaptiveEmbedding''',
        '''TFTransfoXLForSequenceClassification''',
        '''TFTransfoXLLMHeadModel''',
        '''TFTransfoXLMainLayer''',
        '''TFTransfoXLModel''',
        '''TFTransfoXLPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

    __UpperCAmelCase          =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



