





"""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


lowerCAmelCase__     			=			logging.get_logger(__name__)

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





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"table-transformer"
						UpperCAmelCase_ =			["past_key_values"]
						UpperCAmelCase_ =			{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}


						def __init__(self     :   Optional[Any]   ,							__a     :   Union[str, Any]=True   ,							__a     :   List[Any]=None   ,							__a     :   str=3   ,							__a     :   Optional[int]=1_00   ,							__a     :   List[Any]=6   ,							__a     :   int=20_48   ,							__a     :   Union[str, Any]=8   ,							__a     :   int=6   ,							__a     :   Tuple=20_48   ,							__a     :   str=8   ,							__a     :   Union[str, Any]=0.0   ,							__a     :   Dict=0.0   ,							__a     :   int=True   ,							__a     :   int="relu"   ,							__a     :   Optional[int]=2_56   ,							__a     :   Dict=0.1   ,							__a     :   int=0.0   ,							__a     :   List[str]=0.0   ,							__a     :   Dict=0.02   ,							__a     :   List[Any]=1.0   ,							__a     :   Any=False   ,							__a     :   Optional[Any]="sine"   ,							__a     :   Union[str, Any]="resnet50"   ,							__a     :   Dict=True   ,							__a     :   Union[str, Any]=False   ,							__a     :   int=1   ,							__a     :   List[Any]=5   ,							__a     :   Any=2   ,							__a     :   Union[str, Any]=1   ,							__a     :   str=1   ,							__a     :   List[str]=5   ,							__a     :   Optional[Any]=2   ,							__a     :   int=0.1   ,							**__a     :   Dict   ,							) ->   List[Any]:
								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(__a   ,							__a		):
												UpperCamelCase   =	backbone_config.get("model_type"		)
												UpperCamelCase   =	CONFIG_MAPPING[backbone_model_type]
												UpperCamelCase   =	config_class.from_dict(__a		)
										# set timm attributes to None
										UpperCamelCase   ,				UpperCamelCase   ,				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=__a   ,							**__a		)


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




						@property
						def     snake_case_   (self     :   List[str]		) ->   int:
								return self.d_model





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			version.parse("1.11" )


						@property
						def     snake_case_   (self     :   Dict		) ->   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     :   Union[str, Any]		) ->   float:
								return 1e-5




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













"""simple docstring"""





import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows


def       a__					(					_SCREAMING_SNAKE_CASE    ):  # picklable for multiprocessing




		"""simple docstring"""


		return i + 1


@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def       a__					(					):




		"""simple docstring"""


		with parallel_backend("spark"    ):
				assert ParallelBackendConfig.backend_name == "spark"

		UpperCamelCase   =	[1, 2, 3]
		with pytest.raises(_SCREAMING_SNAKE_CASE    ):
				with parallel_backend("unsupported backend"    ):
						map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=2    )

		with pytest.raises(_SCREAMING_SNAKE_CASE    ):
				with parallel_backend("unsupported backend"    ):
						map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=-1    )






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"  ,	[2, -1]    )
def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[1, 2]
		UpperCamelCase   =	{"a": 1, "b": 2}
		UpperCamelCase   =	{"a": [1, 2], "b": [3, 4]}
		UpperCamelCase   =	{"a": {"1": 1}, "b": 2}
		UpperCamelCase   =	{"a": 1, "b": 2, "c": 3, "d": 4}
		UpperCamelCase   =	[2, 3]
		UpperCamelCase   =	{"a": 2, "b": 3}
		UpperCamelCase   =	{"a": [2, 3], "b": [4, 5]}
		UpperCamelCase   =	{"a": {"1": 2}, "b": 3}
		UpperCamelCase   =	{"a": 2, "b": 3, "c": 4, "d": 5}

		with parallel_backend("spark"    ):
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa






