






'''simple docstring'''







import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
			import torch
			from torch import nn

			from transformers import YolosForObjectDetection, YolosModel
			from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class _lowercase	:




							def __init__(							self:				Union[str, Any]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				Tuple=13			,							UpperCamelCase__:				Optional[int]=[30, 30]			,							UpperCamelCase__:				Tuple=2			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				Tuple=True			,							UpperCamelCase__:				str=True			,							UpperCamelCase__:				int=32			,							UpperCamelCase__:				List[str]=5			,							UpperCamelCase__:				str=4			,							UpperCamelCase__:				Dict=37			,							UpperCamelCase__:				int="gelu"			,							UpperCamelCase__:				List[str]=0.1			,							UpperCamelCase__:				int=0.1			,							UpperCamelCase__:				List[Any]=10			,							UpperCamelCase__:				Union[str, Any]=0.02			,							UpperCamelCase__:				Optional[int]=3			,							UpperCamelCase__:				Tuple=None			,							UpperCamelCase__:				int=8			,							UpperCamelCase__:				List[str]=10			,							):
														lowerCamelCase__					:	Tuple         =       parent
														lowerCamelCase__					:	List[Any]         =       batch_size
														lowerCamelCase__					:	int         =       image_size
														lowerCamelCase__					:	int         =       patch_size
														lowerCamelCase__					:	int         =       num_channels
														lowerCamelCase__					:	Optional[Any]         =       is_training
														lowerCamelCase__					:	List[str]         =       use_labels
														lowerCamelCase__					:	List[Any]         =       hidden_size
														lowerCamelCase__					:	str         =       num_hidden_layers
														lowerCamelCase__					:	Tuple         =       num_attention_heads
														lowerCamelCase__					:	List[str]         =       intermediate_size
														lowerCamelCase__					:	Optional[Any]         =       hidden_act
														lowerCamelCase__					:	int         =       hidden_dropout_prob
														lowerCamelCase__					:	Optional[Any]         =       attention_probs_dropout_prob
														lowerCamelCase__					:	str         =       type_sequence_label_size
														lowerCamelCase__					:	Dict         =       initializer_range
														lowerCamelCase__					:	Tuple         =       num_labels
														lowerCamelCase__					:	Dict         =       scope
														lowerCamelCase__					:	Dict         =       n_targets
														lowerCamelCase__					:	Optional[Any]         =       num_detection_tokens
														# we set the expected sequence length (which is used in several tests)
														# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
														lowerCamelCase__					:	str         =       (image_size[1] // patch_size) * (image_size[0] // patch_size)
														lowerCamelCase__					:	Dict         =       num_patches + 1 + self.num_detection_tokens




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	str         =       floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]     )

														lowerCamelCase__					:	Union[str, Any]         =       None
														if self.use_labels:
																					# labels is a list of Dict (each Dict being the labels for a given example in the batch)
																					lowerCamelCase__					:	Any         =       []
																					for i in range(self.batch_size     ):
																												lowerCamelCase__					:	Dict         =       {}
																												lowerCamelCase__					:	Union[str, Any]         =       torch.randint(
																												    high=self.num_labels			,							size=(self.n_targets,)			,							device=SCREAMING_SNAKE_CASE__     )
																												lowerCamelCase__					:	Dict         =       torch.rand(self.n_targets			,							4			,							device=SCREAMING_SNAKE_CASE__     )
																												labels.append(SCREAMING_SNAKE_CASE__     )

														lowerCamelCase__					:	Union[str, Any]         =       self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														return YolosConfig(
														    image_size=self.image_size			,							patch_size=self.patch_size			,							num_channels=self.num_channels			,							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			,							is_decoder=SCREAMING_SNAKE_CASE__			,							initializer_range=self.initializer_range			,							num_detection_tokens=self.num_detection_tokens			,							num_labels=self.num_labels			,							)




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       YolosModel(config=SCREAMING_SNAKE_CASE__     )
														model.to(SCREAMING_SNAKE_CASE__     )
														model.eval()
														lowerCamelCase__					:	Dict         =       model(SCREAMING_SNAKE_CASE__     )
														self.parent.assertEqual(
														    result.last_hidden_state.shape			,							(self.batch_size, self.expected_seq_len, self.hidden_size)     )




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Optional[int]     ):
														lowerCamelCase__					:	int         =       YolosForObjectDetection(SCREAMING_SNAKE_CASE__     )
														model.to(SCREAMING_SNAKE_CASE__     )
														model.eval()

														lowerCamelCase__					:	Optional[int]         =       model(pixel_values=SCREAMING_SNAKE_CASE__     )
														lowerCamelCase__					:	Optional[Any]         =       model(SCREAMING_SNAKE_CASE__     )

														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_detection_tokens, self.num_labels + 1)     )
														self.parent.assertEqual(result.pred_boxes.shape			,							(self.batch_size, self.num_detection_tokens, 4)     )

														lowerCamelCase__					:	Optional[Any]         =       model(pixel_values=SCREAMING_SNAKE_CASE__			,							labels=SCREAMING_SNAKE_CASE__     )

														self.parent.assertEqual(result.loss.shape			,							()     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_detection_tokens, self.num_labels + 1)     )
														self.parent.assertEqual(result.pred_boxes.shape			,							(self.batch_size, self.num_detection_tokens, 4)     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	List[Any]         =       self.prepare_config_and_inputs()
														lowerCamelCase__					:	Optional[int]         =       config_and_inputs
														lowerCamelCase__					:	Optional[int]         =       {'''pixel_values''': pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							a				      =       (
							    {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Tuple=False     ):
														lowerCamelCase__					:	str         =       super()._prepare_for_class(SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							return_labels=SCREAMING_SNAKE_CASE__     )

														if return_labels:
																					if model_class.__name__ == "YolosForObjectDetection":
																												lowerCamelCase__					:	Dict         =       []
																												for i in range(self.model_tester.batch_size     ):
																																			lowerCamelCase__					:	Union[str, Any]         =       {}
																																			lowerCamelCase__					:	Optional[Any]         =       torch.ones(
																																			    size=(self.model_tester.n_targets,)			,							device=SCREAMING_SNAKE_CASE__			,							dtype=torch.long     )
																																			lowerCamelCase__					:	Optional[Any]         =       torch.ones(
																																			    self.model_tester.n_targets			,							4			,							device=SCREAMING_SNAKE_CASE__			,							dtype=torch.float     )
																																			labels.append(SCREAMING_SNAKE_CASE__     )
																												lowerCamelCase__					:	List[str]         =       labels

														return inputs_dict




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	int         =       YolosModelTester(self     )
														lowerCamelCase__					:	Optional[Any]         =       ConfigTester(self			,							config_class=SCREAMING_SNAKE_CASE__			,							has_text_modality=SCREAMING_SNAKE_CASE__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														self.config_tester.run_common_tests()




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														pass




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	List[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(SCREAMING_SNAKE_CASE__     )
																					self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module)     )
																					lowerCamelCase__					:	List[str]         =       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__			,							nn.Linear     )     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Union[str, Any]         =       model_class(SCREAMING_SNAKE_CASE__     )
																					lowerCamelCase__					:	Union[str, Any]         =       inspect.signature(model.forward     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	Optional[Any]         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	Union[str, Any]         =       ['''pixel_values''']
																					self.assertListEqual(arg_names[:1]			,							SCREAMING_SNAKE_CASE__     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Dict         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs_for_common()
														lowerCamelCase__					:	int         =       True

														# in YOLOS, the seq_len is different
														lowerCamelCase__					:	Optional[int]         =       self.model_tester.expected_seq_len
														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	str         =       True
																					lowerCamelCase__					:	int         =       False
																					lowerCamelCase__					:	Optional[Any]         =       True
																					lowerCamelCase__					:	int         =       model_class(SCREAMING_SNAKE_CASE__     )
																					model.to(SCREAMING_SNAKE_CASE__     )
																					model.eval()
																					with torch.no_grad():
																												lowerCamelCase__					:	str         =       model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__     )     )
																					lowerCamelCase__					:	Tuple         =       outputs.attentions
																					self.assertEqual(len(SCREAMING_SNAKE_CASE__     )			,							self.model_tester.num_hidden_layers     )

																					# check that output_attentions also work using config
																					del inputs_dict["output_attentions"]
																					lowerCamelCase__					:	List[str]         =       True
																					lowerCamelCase__					:	List[Any]         =       model_class(SCREAMING_SNAKE_CASE__     )
																					model.to(SCREAMING_SNAKE_CASE__     )
																					model.eval()
																					with torch.no_grad():
																												lowerCamelCase__					:	Any         =       model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__     )     )
																					lowerCamelCase__					:	Tuple         =       outputs.attentions
																					self.assertEqual(len(SCREAMING_SNAKE_CASE__     )			,							self.model_tester.num_hidden_layers     )

																					self.assertListEqual(
																					    list(attentions[0].shape[-3:]     )			,							[self.model_tester.num_attention_heads, seq_len, seq_len]			,							)
																					lowerCamelCase__					:	List[str]         =       len(SCREAMING_SNAKE_CASE__     )

																					# Check attention is always last and order is fine
																					lowerCamelCase__					:	str         =       True
																					lowerCamelCase__					:	Union[str, Any]         =       True
																					lowerCamelCase__					:	Union[str, Any]         =       model_class(SCREAMING_SNAKE_CASE__     )
																					model.to(SCREAMING_SNAKE_CASE__     )
																					model.eval()
																					with torch.no_grad():
																												lowerCamelCase__					:	List[str]         =       model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__     )     )

																					lowerCamelCase__					:	Dict         =       1
																					self.assertEqual(out_len + added_hidden_states			,							len(SCREAMING_SNAKE_CASE__     )     )

																					lowerCamelCase__					:	Optional[int]         =       outputs.attentions

																					self.assertEqual(len(SCREAMING_SNAKE_CASE__     )			,							self.model_tester.num_hidden_layers     )
																					self.assertListEqual(
																					    list(self_attentions[0].shape[-3:]     )			,							[self.model_tester.num_attention_heads, seq_len, seq_len]			,							)




							def 		lowerCamelCase_				(							self:				List[Any]     ):






														def check_hidden_states_output(UpperCamelCase__:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				Union[str, Any]     ):
																					lowerCamelCase__					:	Any         =       model_class(SCREAMING_SNAKE_CASE__     )
																					model.to(SCREAMING_SNAKE_CASE__     )
																					model.eval()

																					with torch.no_grad():
																												lowerCamelCase__					:	Optional[Any]         =       model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__     )     )

																					lowerCamelCase__					:	Tuple         =       outputs.hidden_states

																					lowerCamelCase__					:	List[str]         =       getattr(
																					    self.model_tester			,							"""expected_num_hidden_layers"""			,							self.model_tester.num_hidden_layers + 1     )
																					self.assertEqual(len(SCREAMING_SNAKE_CASE__     )			,							SCREAMING_SNAKE_CASE__     )

																					# YOLOS has a different seq_length
																					lowerCamelCase__					:	Optional[Any]         =       self.model_tester.expected_seq_len

																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]     )			,							[seq_length, self.model_tester.hidden_size]			,							)

														lowerCamelCase__					:	Tuple         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	int         =       True
																					check_hidden_states_output(SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__     )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCamelCase__					:	Any         =       True

																					check_hidden_states_output(SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_object_detection(*SCREAMING_SNAKE_CASE__     )




							@slow
							def 		lowerCamelCase_				(							self:				str     ):






														for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	Optional[Any]         =       YolosModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
																					self.assertIsNotNone(SCREAMING_SNAKE_CASE__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       str:
							lowerCamelCase__					:	Union[str, Any]         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_torch
@require_vision
class _lowercase	(				unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_				(							self:				int     ):
														return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""     ) if is_vision_available() else None




							@slow
							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[Any]         =       YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""     ).to(SCREAMING_SNAKE_CASE__     )

														lowerCamelCase__					:	Any         =       self.default_image_processor
														lowerCamelCase__					:	List[str]         =       prepare_img()
														lowerCamelCase__					:	List[Any]         =       image_processor(images=SCREAMING_SNAKE_CASE__			,							return_tensors="""pt"""     ).to(SCREAMING_SNAKE_CASE__     )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__					:	Union[str, Any]         =       model(inputs.pixel_values     )

														# verify outputs
														lowerCamelCase__					:	Optional[Any]         =       torch.Size((1, 100, 92)     )
														self.assertEqual(outputs.logits.shape			,							SCREAMING_SNAKE_CASE__     )

														lowerCamelCase__					:	str         =       torch.tensor(
														    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]			,							device=SCREAMING_SNAKE_CASE__			,							)
														lowerCamelCase__					:	int         =       torch.tensor(
														    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]			,							device=SCREAMING_SNAKE_CASE__     )
														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,							SCREAMING_SNAKE_CASE__			,							atol=1e-4     )     )
														self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]			,							SCREAMING_SNAKE_CASE__			,							atol=1e-4     )     )

														# verify postprocessing
														lowerCamelCase__					:	Any         =       image_processor.post_process_object_detection(
														    SCREAMING_SNAKE_CASE__			,							threshold=0.3			,							target_sizes=[image.size[::-1]]     )[0]
														lowerCamelCase__					:	Any         =       torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]     ).to(SCREAMING_SNAKE_CASE__     )
														lowerCamelCase__					:	int         =       [75, 75, 17, 63, 17]
														lowerCamelCase__					:	List[str]         =       torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]     ).to(SCREAMING_SNAKE_CASE__     )

														self.assertEqual(len(results["""scores"""]     )			,							5     )
														self.assertTrue(torch.allclose(results["""scores"""]			,							SCREAMING_SNAKE_CASE__			,							atol=1e-4     )     )
														self.assertSequenceEqual(results["""labels"""].tolist()			,							SCREAMING_SNAKE_CASE__     )
														self.assertTrue(torch.allclose(results["""boxes"""][0, :]			,							SCREAMING_SNAKE_CASE__     )     )








"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "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 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  '''xmod'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_05_22		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__="absolute"		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=("en_XX",)		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





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






class 					_SCREAMING_SNAKE_CASE(   A  ):




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





             """simple docstring"""






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