




import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    LayoutLMForMaskedLM,
						    LayoutLMForQuestionAnswering,
						    LayoutLMForSequenceClassification,
						    LayoutLMForTokenClassification,
						    LayoutLMModel,
						)







class 				UpperCAmelCase:



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase	,				lowerCamelCase=13	,				lowerCamelCase=7	,				lowerCamelCase=True	,				lowerCamelCase=True	,				lowerCamelCase=True	,				lowerCamelCase=True	,				lowerCamelCase=99	,				lowerCamelCase=32	,				lowerCamelCase=5	,				lowerCamelCase=4	,				lowerCamelCase=37	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=16	,				lowerCamelCase=2	,				lowerCamelCase=0.02	,				lowerCamelCase=3	,				lowerCamelCase=4	,				lowerCamelCase=None	,				lowerCamelCase=1000	,				) ->      Union[str, Any]:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							parent
								lowercase__  :	Dict        =							batch_size
								lowercase__  :	List[str]        =							seq_length
								lowercase__  :	List[str]        =							is_training
								lowercase__  :	Optional[Any]        =							use_input_mask
								lowercase__  :	Optional[Any]        =							use_token_type_ids
								lowercase__  :	Optional[int]        =							use_labels
								lowercase__  :	Tuple        =							vocab_size
								lowercase__  :	Optional[int]        =							hidden_size
								lowercase__  :	Union[str, Any]        =							num_hidden_layers
								lowercase__  :	List[str]        =							num_attention_heads
								lowercase__  :	Union[str, Any]        =							intermediate_size
								lowercase__  :	Tuple        =							hidden_act
								lowercase__  :	Optional[int]        =							hidden_dropout_prob
								lowercase__  :	Any        =							attention_probs_dropout_prob
								lowercase__  :	List[Any]        =							max_position_embeddings
								lowercase__  :	List[str]        =							type_vocab_size
								lowercase__  :	str        =							type_sequence_label_size
								lowercase__  :	int        =							initializer_range
								lowercase__  :	int        =							num_labels
								lowercase__  :	Any        =							num_choices
								lowercase__  :	Dict        =							scope
								lowercase__  :	str        =							range_bbox

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	List[Any]        =							ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size    )

								lowercase__  :	Optional[Any]        =							ids_tensor([self.batch_size, self.seq_length, 4]	,				self.range_bbox    )
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]    ):
															for j in range(bbox.shape[1]    ):
																						if bbox[i, j, 3] < bbox[i, j, 1]:
																													lowercase__  :	str        =							bbox[i, j, 3]
																													lowercase__  :	Optional[Any]        =							bbox[i, j, 1]
																													lowercase__  :	Optional[Any]        =							t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																													lowercase__  :	List[Any]        =							bbox[i, j, 2]
																													lowercase__  :	List[str]        =							bbox[i, j, 0]
																													lowercase__  :	List[Any]        =							t

								lowercase__  :	Union[str, Any]        =							None
								if self.use_input_mask:
															lowercase__  :	Dict        =							ids_tensor([self.batch_size, self.seq_length]	,				vocab_size=2    )

								lowercase__  :	Union[str, Any]        =							None
								if self.use_token_type_ids:
															lowercase__  :	List[Any]        =							ids_tensor([self.batch_size, self.seq_length]	,				self.type_vocab_size    )

								lowercase__  :	List[str]        =							None
								lowercase__  :	Dict        =							None
								lowercase__  :	int        =							None
								if self.use_labels:
															lowercase__  :	Union[str, Any]        =							ids_tensor([self.batch_size]	,				self.type_sequence_label_size    )
															lowercase__  :	List[str]        =							ids_tensor([self.batch_size, self.seq_length]	,				self.num_labels    )
															lowercase__  :	List[str]        =							ids_tensor([self.batch_size]	,				self.num_choices    )

								lowercase__  :	str        =							self.get_config()

								return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

	def    __a     (					self    ) ->      Union[str, Any]:





								"""simple docstring"""


								return LayoutLMConfig(
								    vocab_size=self.vocab_size	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				num_attention_heads=self.num_attention_heads	,				intermediate_size=self.intermediate_size	,				hidden_act=self.hidden_act	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				max_position_embeddings=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				initializer_range=self.initializer_range	,				)

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      str:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							LayoutLMModel(config=lowerCamelCase    )
								model.to(lowerCamelCase    )
								model.eval()
								lowercase__  :	int        =							model(lowerCamelCase	,				lowerCamelCase	,				attention_mask=lowerCamelCase	,				token_type_ids=lowerCamelCase    )
								lowercase__  :	Any        =							model(lowerCamelCase	,				lowerCamelCase	,				token_type_ids=lowerCamelCase    )
								lowercase__  :	Tuple        =							model(lowerCamelCase	,				lowerCamelCase    )
								self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)    )
								self.parent.assertEqual(result.pooler_output.shape	,				(self.batch_size, self.hidden_size)    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      List[str]:





								"""simple docstring"""


								lowercase__  :	Any        =							LayoutLMForMaskedLM(config=lowerCamelCase    )
								model.to(lowerCamelCase    )
								model.eval()
								lowercase__  :	Any        =							model(lowerCamelCase	,				lowerCamelCase	,				attention_mask=lowerCamelCase	,				token_type_ids=lowerCamelCase	,				labels=lowerCamelCase    )
								self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      Union[str, Any]:





								"""simple docstring"""


								lowercase__  :	Dict        =							self.num_labels
								lowercase__  :	List[Any]        =							LayoutLMForSequenceClassification(lowerCamelCase    )
								model.to(lowerCamelCase    )
								model.eval()
								lowercase__  :	Dict        =							model(
								    lowerCamelCase	,				lowerCamelCase	,				attention_mask=lowerCamelCase	,				token_type_ids=lowerCamelCase	,				labels=lowerCamelCase    )
								self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      Any:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							self.num_labels
								lowercase__  :	str        =							LayoutLMForTokenClassification(config=lowerCamelCase    )
								model.to(lowerCamelCase    )
								model.eval()
								lowercase__  :	Optional[int]        =							model(lowerCamelCase	,				lowerCamelCase	,				attention_mask=lowerCamelCase	,				token_type_ids=lowerCamelCase	,				labels=lowerCamelCase    )
								self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      Any:





								"""simple docstring"""


								lowercase__  :	Tuple        =							LayoutLMForQuestionAnswering(config=lowerCamelCase    )
								model.to(lowerCamelCase    )
								model.eval()
								lowercase__  :	List[str]        =							model(
								    lowerCamelCase	,				bbox=lowerCamelCase	,				attention_mask=lowerCamelCase	,				token_type_ids=lowerCamelCase	,				start_positions=lowerCamelCase	,				end_positions=lowerCamelCase	,				)
								self.parent.assertEqual(result.start_logits.shape	,				(self.batch_size, self.seq_length)    )
								self.parent.assertEqual(result.end_logits.shape	,				(self.batch_size, self.seq_length)    )







	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								lowercase__  :	List[str]        =							self.prepare_config_and_inputs()
								(
								    (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       
								)  :	Dict        =							config_and_inputs
								lowercase__  :	List[Any]        =							{
								    "input_ids": input_ids,
								    "bbox": bbox,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict







@require_torch
class 				UpperCAmelCase(					snake_case_   ,     snake_case_   ,     unittest.TestCase					):



	"""simple docstring"""






	a     : List[str]	  =					(
	    (
	        LayoutLMModel,
	        LayoutLMForMaskedLM,
	        LayoutLMForSequenceClassification,
	        LayoutLMForTokenClassification,
	        LayoutLMForQuestionAnswering,
	    )
	    if is_torch_available()
	    else None
	)
	a     : Dict	  =					(
	    {
	        """document-question-answering""": LayoutLMForQuestionAnswering,
	        """feature-extraction""": LayoutLMModel,
	        """fill-mask""": LayoutLMForMaskedLM,
	        """text-classification""": LayoutLMForSequenceClassification,
	        """token-classification""": LayoutLMForTokenClassification,
	        """zero-shot""": LayoutLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	a     : str	  =					True

	def    __a     (					self    ) ->      Optional[Any]:





								"""simple docstring"""


								lowercase__  :	Any        =							LayoutLMModelTester(self    )
								lowercase__  :	Optional[Any]        =							ConfigTester(self	,				config_class=lowerCamelCase	,				hidden_size=37    )

	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


								self.config_tester.run_common_tests()

	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase    )

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
															lowercase__  :	List[Any]        =							type
															self.model_tester.create_and_check_model(*lowerCamelCase    )

	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase    )

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase    )

	def    __a     (					self    ) ->      Optional[Any]:





								"""simple docstring"""


								lowercase__  :	int        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase    )

	def    __a     (					self    ) ->      List[str]:





								"""simple docstring"""


								lowercase__  :	Dict        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase    )







	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								pass

def  snake_case_					(		)      ->			int:
							# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
							# fmt: off
							lowercase__  :	Optional[int]        =							torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]    ,device=SCREAMING_SNAKE_CASE_    )  # noqa: E231
							lowercase__  :	int        =							torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]    ,device=SCREAMING_SNAKE_CASE_    )  # noqa: E231
							lowercase__  :	Any        =							torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]    ,device=SCREAMING_SNAKE_CASE_    )  # noqa: E231
							lowercase__  :	List[Any]        =							torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]    ,device=SCREAMING_SNAKE_CASE_    )  # noqa: E231
							# these are sequence labels (i.e. at the token level)
							lowercase__  :	Dict        =							torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]    ,device=SCREAMING_SNAKE_CASE_    )  # noqa: E231
							# fmt: on

							return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 				UpperCAmelCase(					unittest.TestCase					):



	"""simple docstring"""






	@slow
	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								lowercase__  :	Dict        =							LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"    ).to(lowerCamelCase    )

								lowercase__ ,       lowercase__ ,       lowercase__ ,       lowercase__ ,       lowercase__  :	str        =							prepare_layoutlm_batch_inputs()

								# forward pass
								lowercase__  :	int        =							model(input_ids=lowerCamelCase	,				bbox=lowerCamelCase	,				attention_mask=lowerCamelCase	,				token_type_ids=lowerCamelCase    )

								# test the sequence output on [0, :3, :3]
								lowercase__  :	Dict        =							torch.tensor(
								    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]	,				device=lowerCamelCase	,				)

								self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,				lowerCamelCase	,				atol=1E-3    )    )

								# test the pooled output on [1, :3]
								lowercase__  :	List[Any]        =							torch.tensor([-0.65_80, -0.02_14, 0.85_52]	,				device=lowerCamelCase    )

								self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]	,				lowerCamelCase	,				atol=1E-3    )    )

	@slow
	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"	,				num_labels=2    ).to(
								    lowerCamelCase    )

								lowercase__ ,       lowercase__ ,       lowercase__ ,       lowercase__ ,       lowercase__  :	List[str]        =							prepare_layoutlm_batch_inputs()

								# forward pass
								lowercase__  :	List[str]        =							model(
								    input_ids=lowerCamelCase	,				bbox=lowerCamelCase	,				attention_mask=lowerCamelCase	,				token_type_ids=lowerCamelCase	,				labels=torch.tensor([1, 1]	,				device=lowerCamelCase    )	,				)

								# test whether we get a loss as a scalar
								lowercase__  :	Union[str, Any]        =							outputs.loss
								lowercase__  :	str        =							torch.Size([]    )
								self.assertEqual(loss.shape	,				lowerCamelCase    )

								# test the shape of the logits
								lowercase__  :	List[Any]        =							outputs.logits
								lowercase__  :	Dict        =							torch.Size((2, 2)    )
								self.assertEqual(logits.shape	,				lowerCamelCase    )

	@slow
	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"	,				num_labels=13    ).to(
								    lowerCamelCase    )

								lowercase__ ,       lowercase__ ,       lowercase__ ,       lowercase__ ,       lowercase__  :	Tuple        =							prepare_layoutlm_batch_inputs()

								# forward pass
								lowercase__  :	List[Any]        =							model(
								    input_ids=lowerCamelCase	,				bbox=lowerCamelCase	,				attention_mask=lowerCamelCase	,				token_type_ids=lowerCamelCase	,				labels=lowerCamelCase    )

								# test the loss calculation to be around 2.65
								# expected_loss = torch.tensor(2.65, device=torch_device)

								# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
								# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

								# test the shape of the logits
								lowercase__  :	Dict        =							outputs.logits
								lowercase__  :	Optional[int]        =							torch.Size((2, 25, 13)    )
								self.assertEqual(logits.shape	,				lowerCamelCase    )







	@slow
	def    __a     (					self    ) ->      Optional[Any]:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"    ).to(lowerCamelCase    )

								lowercase__ ,       lowercase__ ,       lowercase__ ,       lowercase__ ,       lowercase__  :	Any        =							prepare_layoutlm_batch_inputs()

								# forward pass
								lowercase__  :	List[Any]        =							model(input_ids=lowerCamelCase	,				bbox=lowerCamelCase	,				attention_mask=lowerCamelCase	,				token_type_ids=lowerCamelCase    )

								# test the shape of the logits
								lowercase__  :	Optional[Any]        =							torch.Size((2, 25)    )
								self.assertEqual(outputs.start_logits.shape	,				lowerCamelCase    )
								self.assertEqual(outputs.end_logits.shape	,				lowerCamelCase    )





from collections import OrderedDict
from typing import Mapping

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


__a  :		Any		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : str	  =					SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	a     : int	  =					"""squeezebert"""

	def __init__(					self	,				lowerCamelCase=30522	,				lowerCamelCase=768	,				lowerCamelCase=12	,				lowerCamelCase=12	,				lowerCamelCase=3072	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=2	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-12	,				lowerCamelCase=0	,				lowerCamelCase=768	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=1	,				lowerCamelCase=4	,				lowerCamelCase=4	,				**lowerCamelCase	,				) ->      str:





								"""simple docstring"""


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

								lowercase__  :	Any        =							vocab_size
								lowercase__  :	Tuple        =							hidden_size
								lowercase__  :	str        =							num_hidden_layers
								lowercase__  :	Optional[int]        =							num_attention_heads
								lowercase__  :	List[Any]        =							hidden_act
								lowercase__  :	Union[str, Any]        =							intermediate_size
								lowercase__  :	int        =							hidden_dropout_prob
								lowercase__  :	List[Any]        =							attention_probs_dropout_prob
								lowercase__  :	Any        =							max_position_embeddings
								lowercase__  :	List[Any]        =							type_vocab_size
								lowercase__  :	Any        =							initializer_range
								lowercase__  :	Optional[Any]        =							layer_norm_eps
								lowercase__  :	Any        =							embedding_size
								lowercase__  :	Optional[Any]        =							q_groups
								lowercase__  :	List[str]        =							k_groups
								lowercase__  :	List[Any]        =							v_groups
								lowercase__  :	str        =							post_attention_groups
								lowercase__  :	Optional[int]        =							intermediate_groups
								lowercase__  :	Optional[int]        =							output_groups







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	@property
	def    __a     (					self    ) ->      Mapping[str, Mapping[int, str]]:





								"""simple docstring"""


								if self.task == "multiple-choice":
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "choice", 2: "sequence"}
								else:
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]    )