
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
				from ...onnx.config import PatchingSpec
				from ...tokenization_utils_base import PreTrainedTokenizerBase


lowercase__							:							str   		=			logging.get_logger(__name__)

lowercase__							:							Union[str, Any]   		=			{
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}






class        SCREAMING_SNAKE_CASE__    (							__UpperCAmelCase    ):

							"""simple docstring"""






							_snake_case     		=  "longformer"



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_ = 512     ,	SCREAMING_SNAKE_CASE_ = 2     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = 0     ,	SCREAMING_SNAKE_CASE_ = 2     ,	SCREAMING_SNAKE_CASE_ = 30522     ,	SCREAMING_SNAKE_CASE_ = 768     ,	SCREAMING_SNAKE_CASE_ = 12     ,	SCREAMING_SNAKE_CASE_ = 12     ,	SCREAMING_SNAKE_CASE_ = 3072     ,	SCREAMING_SNAKE_CASE_ = "gelu"     ,	SCREAMING_SNAKE_CASE_ = 0.1     ,	SCREAMING_SNAKE_CASE_ = 0.1     ,	SCREAMING_SNAKE_CASE_ = 512     ,	SCREAMING_SNAKE_CASE_ = 2     ,	SCREAMING_SNAKE_CASE_ = 0.0_2     ,	SCREAMING_SNAKE_CASE_ = 1E-12     ,	SCREAMING_SNAKE_CASE_ = False     ,	**SCREAMING_SNAKE_CASE_     ,	)->   List[Any]:





													'''simple docstring'''






													super().__init__(pad_token_id=_lowerCamelCase     ,	**_lowerCamelCase			)

													__UpperCamelCase       =			attention_window
													__UpperCamelCase       =			sep_token_id
													__UpperCamelCase       =			bos_token_id
													__UpperCamelCase       =			eos_token_id
													__UpperCamelCase       =			vocab_size
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			max_position_embeddings
													__UpperCamelCase       =			type_vocab_size
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			layer_norm_eps
													__UpperCamelCase       =			onnx_export






class        SCREAMING_SNAKE_CASE__    (							__UpperCAmelCase    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = "default"     ,	SCREAMING_SNAKE_CASE_ = None			)->   List[Any]:





													'''simple docstring'''






													super().__init__(_lowerCamelCase     ,	_lowerCamelCase     ,	_lowerCamelCase			)
													__UpperCamelCase       =			True



							@property
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													if self.task == "multiple-choice":
																			__UpperCamelCase       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
													else:
																			__UpperCamelCase       =			{0: '''batch''', 1: '''sequence'''}
													return OrderedDict(
													    [
													        ('''input_ids''', dynamic_axis),
													        ('''attention_mask''', dynamic_axis),
													        ('''global_attention_mask''', dynamic_axis),
													    ]			)



							@property
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			super().outputs
													if self.task == "default":
																			__UpperCamelCase       =			{0: '''batch'''}
													return outputs



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return 1E-4



							@property
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													return max(super().default_onnx_opset     ,	14			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			super().generate_dummy_inputs(
													    preprocessor=_lowerCamelCase     ,	batch_size=_lowerCamelCase     ,	seq_length=_lowerCamelCase     ,	is_pair=_lowerCamelCase     ,	framework=_lowerCamelCase			)
													import torch

													# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
													# makes the export fail randomly
													__UpperCamelCase       =			torch.zeros_like(inputs['''input_ids''']			)
													# make every second token global
													__UpperCamelCase       =			1

													return inputs






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__							:							Tuple									=			logging.get_logger(__name__)

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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'table-transformer'
							_snake_case     		=  ['past_key_values']
							_snake_case     		=  {
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=100     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=256     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1.0     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_="sine"     ,	SCREAMING_SNAKE_CASE_="resnet50"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.1     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''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(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																									__UpperCamelCase       =			backbone_config.get('''model_type'''			)
																									__UpperCamelCase       =			CONFIG_MAPPING[backbone_model_type]
																									__UpperCamelCase       =			config_class.from_dict(SCREAMING_SNAKE_CASE_			)
																			# 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=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.encoder_attention_heads



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.d_model






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  version.parse('1.11'    )



							@property
							def     A__		(  self			)->   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     A__		(  self			)->   float:





													'''simple docstring'''






													return 1E-5



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return 12




