



"""simple docstring"""

import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

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


if is_torch_available():
							import torch

							from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
							from transformers.models.esm.modeling_esm import (
							    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    EsmEmbeddings,
							    create_position_ids_from_input_ids,
							)
class     lowerCAmelCase__						:


	'''simple docstring'''







	def __init__(      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=13     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=7     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=False     ,       _SCREAMING_SNAKE_CASE				:       Tuple=True     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=False     ,       _SCREAMING_SNAKE_CASE				:       List[str]=True     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=33     ,       _SCREAMING_SNAKE_CASE				:       int=32     ,       _SCREAMING_SNAKE_CASE				:       Any=5     ,       _SCREAMING_SNAKE_CASE				:       Dict=4     ,       _SCREAMING_SNAKE_CASE				:       Any=37     ,       _SCREAMING_SNAKE_CASE				:       List[Any]="gelu"     ,       _SCREAMING_SNAKE_CASE				:       Tuple=0.1     ,       _SCREAMING_SNAKE_CASE				:       List[str]=0.1     ,       _SCREAMING_SNAKE_CASE				:       int=512     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=16     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=2     ,       _SCREAMING_SNAKE_CASE				:       int=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       int=3     ,       _SCREAMING_SNAKE_CASE				:       Dict=4     ,       _SCREAMING_SNAKE_CASE				:       List[str]=None     ,       )						->	Any:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       int    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     None
					if self.use_input_mask:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     random_attention_mask([self.batch_size, self.seq_length]    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     None
					SCREAMING_SNAKE_CASE						:							Any							=     None
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     None
					if self.use_labels:
									SCREAMING_SNAKE_CASE						:							Tuple							=     ids_tensor([self.batch_size]     ,       self.type_sequence_label_size    )
									SCREAMING_SNAKE_CASE						:							str							=     ids_tensor([self.batch_size, self.seq_length]     ,       self.num_labels    )
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     ids_tensor([self.batch_size]     ,       self.num_choices    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.get_config()

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





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





					"""simple docstring"""


					return EsmConfig(
					    vocab_size=self.vocab_size     ,       hidden_size=self.hidden_size     ,       pad_token_id=1     ,       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 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     EsmModel(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     model(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     model(_SCREAMING_SNAKE_CASE    )

					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 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     EsmForMaskedLM(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE    )
					self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)    )





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Any    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.num_labels
					SCREAMING_SNAKE_CASE						:							Any							=     EsmForTokenClassification(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							List[str]							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE    )
					self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.num_labels)    )







	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.prepare_config_and_inputs()
					(
					    (
					    SCREAMING_SNAKE_CASE
					)				,						(
					    SCREAMING_SNAKE_CASE
					)				,						(
					    SCREAMING_SNAKE_CASE
					)				,						(
					    SCREAMING_SNAKE_CASE
					)				,						(
					    SCREAMING_SNAKE_CASE
					)				,						(
					    SCREAMING_SNAKE_CASE
					)				,						
					)						:							Optional[Any]							=     config_and_inputs
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     {'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict




@require_torch
class     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= False

	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= (
	    (
	        EsmForMaskedLM,
	        EsmModel,
	        EsmForSequenceClassification,
	        EsmForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= ()
	_SCREAMING_SNAKE_CASE	:		Any  						= (
	    {
	        '''feature-extraction''': EsmModel,
	        '''fill-mask''': EsmForMaskedLM,
	        '''text-classification''': EsmForSequenceClassification,
	        '''token-classification''': EsmForTokenClassification,
	        '''zero-shot''': EsmForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_SCREAMING_SNAKE_CASE	:		List[str]  						= True





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     EsmModelTester(self    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       hidden_size=37    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Any:





					"""simple docstring"""


					self.config_tester.run_common_tests()





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									SCREAMING_SNAKE_CASE						:							Any							=     type
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						:							Any							=     EsmModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.model_tester.prepare_config_and_inputs()[0]
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     EsmEmbeddings(config=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.as_tensor([[12, 31, 13, model.padding_idx]]    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.as_tensor(
					    [
					        [
					            0 + model.padding_idx + 1,
					            1 + model.padding_idx + 1,
					            2 + model.padding_idx + 1,
					            model.padding_idx,
					        ]
					    ]    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     create_position_ids_from_input_ids(_SCREAMING_SNAKE_CASE     ,       model.padding_idx    )
					self.assertEqual(position_ids.shape     ,       expected_positions.shape    )
					self.assertTrue(torch.all(torch.eq(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )    )





	def 			_lowerCAmelCase     (      self				:       int    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.model_tester.prepare_config_and_inputs()[0]
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     EsmEmbeddings(config=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							str							=     torch.empty(2     ,       4     ,       30    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [
					    0 + embeddings.padding_idx + 1,
					    1 + embeddings.padding_idx + 1,
					    2 + embeddings.padding_idx + 1,
					    3 + embeddings.padding_idx + 1,
					]
					SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.as_tensor([expected_single_positions, expected_single_positions]    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     embeddings.create_position_ids_from_inputs_embeds(_SCREAMING_SNAKE_CASE    )
					self.assertEqual(position_ids.shape     ,       expected_positions.shape    )
					self.assertTrue(torch.all(torch.eq(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )    )





	@unittest.skip('Esm does not support embedding resizing'    )
	def 			_lowerCAmelCase     (      self				:       str    )						->	Optional[Any]:





					"""simple docstring"""


					pass





	@unittest.skip('Esm does not support embedding resizing'    )
	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Optional[int]:





					"""simple docstring"""


					pass







	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Dict:





					"""simple docstring"""


					pass




@require_torch
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@slow
	def 			_lowerCAmelCase     (      self				:       Dict    )						->	List[str]:





					"""simple docstring"""


					with torch.no_grad():
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'    )
									model.eval()
									SCREAMING_SNAKE_CASE						:							int							=     torch.tensor([[0, 1, 2, 3, 4, 5]]    )
									SCREAMING_SNAKE_CASE						:							Tuple							=     model(_SCREAMING_SNAKE_CASE    )[0]

									SCREAMING_SNAKE_CASE						:							Any							=     33

									SCREAMING_SNAKE_CASE						:							Tuple							=     torch.Size((1, 6, vocab_size)    )
									self.assertEqual(output.shape     ,       _SCREAMING_SNAKE_CASE    )

									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.tensor(
									    [[[8.9_2_1_5, -1_0.5_8_9_8, -6.4_6_7_1], [-6.3_9_6_7, -1_3.9_1_1_4, -1.1_2_1_2], [-7.7_8_1_2, -1_3.9_5_1_6, -3.7_4_0_6]]]    )
									self.assertTrue(torch.allclose(output[:, :3, :3]     ,       _SCREAMING_SNAKE_CASE     ,       atol=1E-4    )    )







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





					"""simple docstring"""


					with torch.no_grad():
									SCREAMING_SNAKE_CASE						:							str							=     EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'    )
									model.eval()

									SCREAMING_SNAKE_CASE						:							Any							=     torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]    )
									SCREAMING_SNAKE_CASE						:							str							=     model(_SCREAMING_SNAKE_CASE    )[0]
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE						:							Any							=     torch.tensor(
									    [[[0.1_4_4_4, 0.5_4_1_3, 0.3_2_4_8], [0.3_0_3_4, 0.0_0_5_3, 0.3_1_0_8], [0.3_2_2_8, -0.2_4_9_9, 0.3_4_1_5]]]    )
									self.assertTrue(torch.allclose(output[:, :3, :3]     ,       _SCREAMING_SNAKE_CASE     ,       atol=1E-4    )    )










"""simple docstring"""

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_						:    Optional[int]				=      {
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= '''xmod'''





	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple=30_522     ,       _SCREAMING_SNAKE_CASE				:       Tuple=768     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=12     ,       _SCREAMING_SNAKE_CASE				:       List[str]=12     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=3_072     ,       _SCREAMING_SNAKE_CASE				:       Any="gelu"     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=0.1     ,       _SCREAMING_SNAKE_CASE				:       Dict=0.1     ,       _SCREAMING_SNAKE_CASE				:       str=512     ,       _SCREAMING_SNAKE_CASE				:       Tuple=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=1E-1_2     ,       _SCREAMING_SNAKE_CASE				:       List[str]=1     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]="absolute"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       Dict=2     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       int=True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Tuple=("en_XX",)     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]     ,       )						->	List[str]:





					"""simple docstring"""


					super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,       bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     vocab_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     num_hidden_layers
					SCREAMING_SNAKE_CASE						:							str							=     num_attention_heads
					SCREAMING_SNAKE_CASE						:							List[Any]							=     hidden_act
					SCREAMING_SNAKE_CASE						:							Any							=     intermediate_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							str							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							str							=     type_vocab_size
					SCREAMING_SNAKE_CASE						:							List[str]							=     initializer_range
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     layer_norm_eps
					SCREAMING_SNAKE_CASE						:							Tuple							=     position_embedding_type
					SCREAMING_SNAKE_CASE						:							int							=     use_cache
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     classifier_dropout
					SCREAMING_SNAKE_CASE						:							List[Any]							=     pre_norm
					SCREAMING_SNAKE_CASE						:							int							=     adapter_reduction_factor
					SCREAMING_SNAKE_CASE						:							List[Any]							=     adapter_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     adapter_reuse_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     ln_before_adapter
					SCREAMING_SNAKE_CASE						:							Tuple							=     list(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     default_language




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@property
	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Mapping[str, Mapping[int, str]]:





					"""simple docstring"""


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





