

'''simple docstring'''


from ...configuration_utils import PretrainedConfig


lowerCAmelCase__										= {
    "google/tapas-base-finetuned-sqa": (
        "https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
    ),
    "google/tapas-base-finetuned-wtq": (
        "https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
    ),
    "google/tapas-base-finetuned-wikisql-supervised": (
        "https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
    ),
    "google/tapas-base-finetuned-tabfact": (
        "https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
    ),
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      List[str]=   "tapas"


							def __init__(			self    ,			_snake_case=3_0522    ,			_snake_case=768    ,			_snake_case=12    ,			_snake_case=12    ,			_snake_case=3072    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=1024    ,			_snake_case=[3, 256, 256, 2, 256, 256, 10]    ,			_snake_case=0.02    ,			_snake_case=1e-12    ,			_snake_case=0    ,			_snake_case=10.0    ,			_snake_case=0    ,			_snake_case=1.0    ,			_snake_case=None    ,			_snake_case=1.0    ,			_snake_case=False    ,			_snake_case=None    ,			_snake_case=1.0    ,			_snake_case=1.0    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case="ratio"    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=64    ,			_snake_case=32    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Union[str, Any]:
								super().__init__(pad_token_id=_snake_case    ,			**_snake_case)

								# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
								UpperCAmelCase_		:      Optional[Any]       	=					vocab_size
								UpperCAmelCase_		:      Tuple       	=					hidden_size
								UpperCAmelCase_		:      List[str]       	=					num_hidden_layers
								UpperCAmelCase_		:      List[str]       	=					num_attention_heads
								UpperCAmelCase_		:      Dict       	=					hidden_act
								UpperCAmelCase_		:      Optional[Any]       	=					intermediate_size
								UpperCAmelCase_		:      int       	=					hidden_dropout_prob
								UpperCAmelCase_		:      str       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      str       	=					max_position_embeddings
								UpperCAmelCase_		:      Dict       	=					type_vocab_sizes
								UpperCAmelCase_		:      Optional[Any]       	=					initializer_range
								UpperCAmelCase_		:      List[str]       	=					layer_norm_eps

								# Fine-tuning task hyperparameters
								UpperCAmelCase_		:      Dict       	=					positive_label_weight
								UpperCAmelCase_		:      List[str]       	=					num_aggregation_labels
								UpperCAmelCase_		:      Tuple       	=					aggregation_loss_weight
								UpperCAmelCase_		:      Tuple       	=					use_answer_as_supervision
								UpperCAmelCase_		:      List[Any]       	=					answer_loss_importance
								UpperCAmelCase_		:      int       	=					use_normalized_answer_loss
								UpperCAmelCase_		:      str       	=					huber_loss_delta
								UpperCAmelCase_		:      Optional[int]       	=					temperature
								UpperCAmelCase_		:      Optional[int]       	=					aggregation_temperature
								UpperCAmelCase_		:      str       	=					use_gumbel_for_cells
								UpperCAmelCase_		:      Any       	=					use_gumbel_for_aggregation
								UpperCAmelCase_		:      Any       	=					average_approximation_function
								UpperCAmelCase_		:      str       	=					cell_selection_preference
								UpperCAmelCase_		:      str       	=					answer_loss_cutoff
								UpperCAmelCase_		:      Optional[int]       	=					max_num_rows
								UpperCAmelCase_		:      Optional[Any]       	=					max_num_columns
								UpperCAmelCase_		:      Tuple       	=					average_logits_per_cell
								UpperCAmelCase_		:      int       	=					select_one_column
								UpperCAmelCase_		:      str       	=					allow_empty_column_selection
								UpperCAmelCase_		:      Tuple       	=					init_cell_selection_weights_to_zero
								UpperCAmelCase_		:      int       	=					reset_position_index_per_cell
								UpperCAmelCase_		:      Optional[Any]       	=					disable_per_token_loss

								# Aggregation hyperparameters
								UpperCAmelCase_		:      List[Any]       	=					aggregation_labels
								UpperCAmelCase_		:      Optional[int]       	=					no_aggregation_label_index

								if isinstance(self.aggregation_labels    ,			_snake_case):
									UpperCAmelCase_		:      Any       	=					{int(_snake_case): v for k, v in aggregation_labels.items()}









'''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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class 					lowercase						(  a_,					a_ ):
							_lowerCamelCase		:      Optional[int]=   "swin"

							_lowerCamelCase		:      Dict=   {
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self    ,			_snake_case=224    ,			_snake_case=4    ,			_snake_case=3    ,			_snake_case=96    ,			_snake_case=[2, 2, 6, 2]    ,			_snake_case=[3, 6, 12, 24]    ,			_snake_case=7    ,			_snake_case=4.0    ,			_snake_case=True    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=0.1    ,			_snake_case="gelu"    ,			_snake_case=False    ,			_snake_case=0.02    ,			_snake_case=1e-5    ,			_snake_case=32    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Tuple:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      Dict       	=					patch_size
								UpperCAmelCase_		:      int       	=					num_channels
								UpperCAmelCase_		:      Tuple       	=					embed_dim
								UpperCAmelCase_		:      Dict       	=					depths
								UpperCAmelCase_		:      Dict       	=					len(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					num_heads
								UpperCAmelCase_		:      Optional[int]       	=					window_size
								UpperCAmelCase_		:      Any       	=					mlp_ratio
								UpperCAmelCase_		:      Any       	=					qkv_bias
								UpperCAmelCase_		:      str       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Optional[int]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					drop_path_rate
								UpperCAmelCase_		:      int       	=					hidden_act
								UpperCAmelCase_		:      Tuple       	=					use_absolute_embeddings
								UpperCAmelCase_		:      Optional[int]       	=					layer_norm_eps
								UpperCAmelCase_		:      Optional[int]       	=					initializer_range
								UpperCAmelCase_		:      Optional[Any]       	=					encoder_stride
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								UpperCAmelCase_		:      Union[str, Any]       	=					int(embed_dim * 2 ** (len(_snake_case) - 1))
								UpperCAmelCase_		:      Tuple       	=					['stem'] + [F"""stage{idx}""" for idx in range(1    ,			len(_snake_case) + 1)]
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					get_aligned_output_features_output_indices(
								    out_features=_snake_case    ,			out_indices=_snake_case    ,			stage_names=self.stage_names)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Tuple=   version.parse("1.11" )


							@property
							def 						_snake_case							(			self)							->				Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ])





							@property
							def 						_snake_case							(			self)							->				float:
								return 1e-4






