



"""simple docstring"""





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


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class 			lowercase__			(      _snake_case    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					"""table-transformer"""
							_UpperCAmelCase      				=					["""past_key_values"""]
							_UpperCAmelCase      				=					{
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}






							def __init__(  self	,       snake_case=True	,       snake_case=None	,       snake_case=3	,       snake_case=100	,       snake_case=6	,       snake_case=2048	,       snake_case=8	,       snake_case=6	,       snake_case=2048	,       snake_case=8	,       snake_case=0.0	,       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=False	,       snake_case="sine"	,       snake_case="resnet50"	,       snake_case=True	,       snake_case=False	,       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	,       )			->							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.'						)
													_UpperCAmelCase							 =		CONFIG_MAPPING["resnet"](out_features=['stage4']						)
											elif isinstance(snake_case_	,       snake_case_						):
													_UpperCAmelCase							 =		backbone_config.get('model_type'						)
													_UpperCAmelCase							 =		CONFIG_MAPPING[backbone_model_type]
													_UpperCAmelCase							 =		config_class.from_dict(snake_case_						)
											# set timm attributes to None
											_UpperCAmelCase							 =		None, None, None

									_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							 =		bbox_loss_coefficient
									_UpperCAmelCase							 =		giou_loss_coefficient
									_UpperCAmelCase							 =		eos_coefficient
									super().__init__(is_encoder_decoder=snake_case_	,       **snake_case_						)






							@property
							def        lowerCamelCase_   (  self						)			->							List[str]:
									return self.encoder_attention_heads






							@property
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									return self.d_model







class 			lowercase__			(      _snake_case    ):
							'''simple docstring'''







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






							@property
							def        lowerCamelCase_   (  self						)			->							Dict:
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									        ('pixel_mask', {0: 'batch'}),
									    ]						)






							@property
							def        lowerCamelCase_   (  self						)			->							int:
									return 1E-5






							@property
							def        lowerCamelCase_   (  self						)			->							Any:
									return 12







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  							=					{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					['''RobertaTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RobertaForCausalLM''',
					    '''RobertaForMaskedLM''',
					    '''RobertaForMultipleChoice''',
					    '''RobertaForQuestionAnswering''',
					    '''RobertaForSequenceClassification''',
					    '''RobertaForTokenClassification''',
					    '''RobertaModel''',
					    '''RobertaPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRobertaForCausalLM''',
					    '''TFRobertaForMaskedLM''',
					    '''TFRobertaForMultipleChoice''',
					    '''TFRobertaForQuestionAnswering''',
					    '''TFRobertaForSequenceClassification''',
					    '''TFRobertaForTokenClassification''',
					    '''TFRobertaMainLayer''',
					    '''TFRobertaModel''',
					    '''TFRobertaPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''FlaxRobertaForCausalLM''',
					    '''FlaxRobertaForMaskedLM''',
					    '''FlaxRobertaForMultipleChoice''',
					    '''FlaxRobertaForQuestionAnswering''',
					    '''FlaxRobertaForSequenceClassification''',
					    '''FlaxRobertaForTokenClassification''',
					    '''FlaxRobertaModel''',
					    '''FlaxRobertaPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
					from .tokenization_roberta import RobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roberta_fast import RobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta import (
										    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaForCausalLM,
										    RobertaForMaskedLM,
										    RobertaForMultipleChoice,
										    RobertaForQuestionAnswering,
										    RobertaForSequenceClassification,
										    RobertaForTokenClassification,
										    RobertaModel,
										    RobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta import (
										    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaForCausalLM,
										    TFRobertaForMaskedLM,
										    TFRobertaForMultipleChoice,
										    TFRobertaForQuestionAnswering,
										    TFRobertaForSequenceClassification,
										    TFRobertaForTokenClassification,
										    TFRobertaMainLayer,
										    TFRobertaModel,
										    TFRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta import (
										    FlaxRobertaForCausalLM,
										    FlaxRobertaForMaskedLM,
										    FlaxRobertaForMultipleChoice,
										    FlaxRobertaForQuestionAnswering,
										    FlaxRobertaForSequenceClassification,
										    FlaxRobertaForTokenClassification,
										    FlaxRobertaModel,
										    FlaxRobertaPreTrainedModel,
										)

else:
					import sys

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


