


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
   import torch

   from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

   if is_vision_available():
      from transformers import MaskFormerImageProcessor

if is_vision_available():
   from PIL import Image
class 	UpperCAmelCase_					:

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A=2						,				__A=True						,				__A=False						,				__A=10						,				__A=3						,				__A=32 * 4						,				__A=32 * 6						,				__A=4						,				__A=32						,				):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				parent
     lowerCamelCase :					Any    =				batch_size
     lowerCamelCase :					Optional[Any]    =				is_training
     lowerCamelCase :					List[Any]    =				use_auxiliary_loss
     lowerCamelCase :					Dict    =				num_queries
     lowerCamelCase :					Any    =				num_channels
     lowerCamelCase :					Tuple    =				min_size
     lowerCamelCase :					Optional[int]    =				max_size
     lowerCamelCase :					Optional[Any]    =				num_labels
     lowerCamelCase :					Optional[Any]    =				mask_feature_size






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
         lowercase_ )

     lowerCamelCase :					Optional[int]    =				torch.ones([self.batch_size, self.min_size, self.max_size]						,				device=lowercase_ )

     lowerCamelCase :					Any    =				(
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,				device=lowercase_ ) > 0.5
     ).float()
     lowerCamelCase :					int    =				(torch.rand((self.batch_size, self.num_labels)						,				device=lowercase_ ) > 0.5).long()

     lowerCamelCase :					Optional[Any]    =				self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels






   def _snake_case    (		self ):






     """simple docstring"""





     return MaskFormerConfig.from_backbone_and_decoder_configs(
         backbone_config=SwinConfig(
             depths=[1, 1, 1, 1]						,				)						,				decoder_config=DetrConfig(
             decoder_ffn_dim=128						,				num_queries=self.num_queries						,				decoder_attention_heads=2						,				d_model=self.mask_feature_size						,				)						,				mask_feature_size=self.mask_feature_size						,				fpn_feature_size=self.mask_feature_size						,				num_channels=self.num_channels						,				num_labels=self.num_labels						,				)






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					int    =				self.prepare_config_and_inputs()
     lowerCamelCase :					Tuple    =				{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
     return config, inputs_dict






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				output.encoder_hidden_states
     lowerCamelCase :					Dict    =				output.pixel_decoder_hidden_states
     lowerCamelCase :					Union[str, Any]    =				output.transformer_decoder_hidden_states

     self.parent.assertTrue(len(lowercase_ )						,				len(config.backbone_config.depths ) )
     self.parent.assertTrue(len(lowercase_ )						,				len(config.backbone_config.depths ) )
     self.parent.assertTrue(len(lowercase_ )						,				config.decoder_config.decoder_layers )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A=False ):






     """simple docstring"""





     with torch.no_grad():
       lowerCamelCase :					Tuple    =				MaskFormerModel(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()

       lowerCamelCase :					Dict    =				model(pixel_values=lowercase_						,				pixel_mask=lowercase_ )
       lowerCamelCase :					Any    =				model(lowercase_						,				output_hidden_states=lowercase_ )
     # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
     # encoder and pixel decoder
     self.parent.assertEqual(
         output.transformer_decoder_last_hidden_state.shape						,				(self.batch_size, self.num_queries, self.mask_feature_size)						,				)
     # let's ensure the other two hidden state exists
     self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
     self.parent.assertTrue(output.encoder_last_hidden_state is not None )

     if output_hidden_states:
       self.check_output_hidden_state(lowercase_						,				lowercase_ )






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				MaskFormerForInstanceSegmentation(config=lowercase_ )
     model.to(lowercase_ )
     model.eval()

     def comm_check_on_output(__A ):
       # let's still check that all the required stuff is there
       self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
       self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
       self.parent.assertTrue(result.encoder_last_hidden_state is not None )
       # okay, now we need to check the logits shape
       # due to the encoder compression, masks have a //4 spatial size
       self.parent.assertEqual(
           result.masks_queries_logits.shape						,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)						,				)
       # + 1 for null class
       self.parent.assertEqual(
           result.class_queries_logits.shape						,				(self.batch_size, self.num_queries, self.num_labels + 1) )

     with torch.no_grad():
       lowerCamelCase :					Dict    =				model(pixel_values=lowercase_						,				pixel_mask=lowercase_ )
       lowerCamelCase :					Tuple    =				model(lowercase_ )

       comm_check_on_output(lowercase_ )

       lowerCamelCase :					Any    =				model(
           pixel_values=lowercase_						,				pixel_mask=lowercase_						,				mask_labels=lowercase_						,				class_labels=lowercase_ )

     comm_check_on_output(lowercase_ )

     self.parent.assertTrue(result.loss is not None )
     self.parent.assertEqual(result.loss.shape						,				torch.Size([1] ) )



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

   '''simple docstring'''




   __A :      Optional[int]     		=		(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
   __A :      List[Any]     		=		(
       {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
       if is_torch_available()
       else {}
   )

   __A :      Tuple     		=		False
   __A :      List[str]     		=		False
   __A :      List[Any]     		=		False
   __A :      List[str]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					int    =				MaskFormerModelTester(self )
     lowerCamelCase :					Optional[int]    =				ConfigTester(self						,				config_class=lowercase_						,				has_text_modality=lowercase_ )






   def _snake_case    (		self ):






     """simple docstring"""





     self.config_tester.run_common_tests()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase    ,	lowerCamelCase :					Tuple    =				self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(lowercase_						,				**lowercase_						,				output_hidden_states=lowercase_ )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowercase_ )






   @unittest.skip(reason="MaskFormer does not use inputs_embeds" )
   def _snake_case    (		self ):






     """simple docstring"""





     pass






   @unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
   def _snake_case    (		self ):






     """simple docstring"""





     pass






   @unittest.skip(reason="MaskFormer is not a generative model" )
   def _snake_case    (		self ):






     """simple docstring"""





     pass






   @unittest.skip(reason="MaskFormer does not use token embeddings" )
   def _snake_case    (		self ):






     """simple docstring"""





     pass






   @require_torch_multi_gpu
   @unittest.skip(
       reason="MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`" )
   def _snake_case    (		self ):






     """simple docstring"""





     pass






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






     """simple docstring"""





     pass






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase    ,	lowerCamelCase :					int    =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       lowerCamelCase :					Tuple    =				model_class(lowercase_ )
       lowerCamelCase :					Any    =				inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       lowerCamelCase :					str    =				[*signature.parameters.keys()]

       lowerCamelCase :					Dict    =				["pixel_values"]
       self.assertListEqual(arg_names[:1]						,				lowercase_ )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     for model_name in ["facebook/maskformer-swin-small-coco"]:
       lowerCamelCase :					Dict    =				MaskFormerModel.from_pretrained(lowercase_ )
       self.assertIsNotNone(lowercase_ )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				(self.model_tester.min_size,) * 2
     lowerCamelCase :					Optional[int]    =				{
         "pixel_values": torch.randn((2, 3, *size)						,				device=lowercase_ ),
         "mask_labels": torch.randn((2, 10, *size)						,				device=lowercase_ ),
         "class_labels": torch.zeros(2						,				10						,				device=lowercase_ ).long(),
     }

     lowerCamelCase :					Any    =				MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(lowercase_ )
     lowerCamelCase :					Optional[int]    =				model(**lowercase_ )
     self.assertTrue(outputs.loss is not None )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(lowercase_						,				**lowercase_						,				output_hidden_states=lowercase_ )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       lowerCamelCase :					Dict    =				model_class(lowercase_ ).to(lowercase_ )
       lowerCamelCase :					List[str]    =				model(**lowercase_						,				output_attentions=lowercase_ )
       self.assertTrue(outputs.attentions is not None )






   def _snake_case    (		self ):






     """simple docstring"""





     if not self.model_tester.is_training:
       return
     # only MaskFormerForInstanceSegmentation has the loss
     lowerCamelCase :					int    =				self.all_model_classes[1]
     lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				self.model_tester.prepare_config_and_inputs()

     lowerCamelCase :					Tuple    =				model_class(lowercase_ )
     model.to(lowercase_ )
     model.train()

     lowerCamelCase :					Any    =				model(lowercase_						,				mask_labels=lowercase_						,				class_labels=lowercase_ ).loss
     loss.backward()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.all_model_classes[1]
     lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					str    =				self.model_tester.prepare_config_and_inputs()
     lowerCamelCase :					Optional[Any]    =				True
     lowerCamelCase :					List[Any]    =				True

     lowerCamelCase :					List[Any]    =				model_class(lowercase_ )
     model.to(lowercase_ )
     model.train()

     lowerCamelCase :					Dict    =				model(lowercase_						,				mask_labels=lowercase_						,				class_labels=lowercase_ )

     lowerCamelCase :					List[Any]    =				outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     lowerCamelCase :					List[str]    =				outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()
     # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
     lowerCamelCase :					Union[str, Any]    =				outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     lowerCamelCase :					Tuple    =				outputs.attentions[0]
     attentions.retain_grad()

     outputs.loss.backward(retain_graph=lowercase_ )

     self.assertIsNotNone(encoder_hidden_states.grad )
     self.assertIsNotNone(pixel_decoder_hidden_states.grad )
     self.assertIsNotNone(transformer_decoder_hidden_states.grad )
     self.assertIsNotNone(attentions.grad )



_snake_case             =     1E-4






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					int    =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
  return image







@require_vision
@slow
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   @cached_property
   def _snake_case    (		self ):






     """simple docstring"""





     return (
         MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
         if is_vision_available()
         else None
     )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(lowercase_ )
     lowerCamelCase :					List[Any]    =				self.default_image_processor
     lowerCamelCase :					Optional[Any]    =				prepare_img()
     lowerCamelCase :					Optional[Any]    =				image_processor(lowercase_						,				return_tensors="pt" ).to(lowercase_ )
     lowerCamelCase :					Tuple    =				inputs["pixel_values"].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
     # check size
     self.assertEqual(lowercase_						,				(1, 3, 800, 1088) )

     with torch.no_grad():
       lowerCamelCase :					Optional[int]    =				model(**lowercase_ )

     lowerCamelCase :					Dict    =				torch.tensor(
         [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]] ).to(lowercase_ )
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]						,				lowercase_						,				atol=lowercase_ ) )

     lowerCamelCase :					int    =				torch.tensor(
         [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]] ).to(lowercase_ )
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,				lowercase_						,				atol=lowercase_ ) )

     lowerCamelCase :					Union[str, Any]    =				torch.tensor(
         [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]] ).to(lowercase_ )
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,				lowercase_						,				atol=lowercase_ ) )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				(
         MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
         .to(lowercase_ )
         .eval()
     )
     lowerCamelCase :					Dict    =				self.default_image_processor
     lowerCamelCase :					Optional[int]    =				prepare_img()
     lowerCamelCase :					List[Any]    =				image_processor(lowercase_						,				return_tensors="pt" ).to(lowercase_ )
     lowerCamelCase :					Tuple    =				inputs["pixel_values"].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
     # check size
     self.assertEqual(lowercase_						,				(1, 3, 800, 1088) )

     with torch.no_grad():
       lowerCamelCase :					int    =				model(**lowercase_ )
     # masks_queries_logits
     lowerCamelCase :					List[str]    =				outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape						,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						,				)
     lowerCamelCase :					Any    =				[
         [-1.3737124, -1.7724937, -1.9364233],
         [-1.5977281, -1.9867939, -2.1523695],
         [-1.5795398, -1.9269832, -2.093942],
     ]
     lowerCamelCase :					Dict    =				torch.tensor(lowercase_ ).to(lowercase_ )
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,				lowercase_						,				atol=lowercase_ ) )
     # class_queries_logits
     lowerCamelCase :					Optional[Any]    =				outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape						,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
     lowerCamelCase :					Union[str, Any]    =				torch.tensor(
         [
             [1.65_12e00, -5.25_72e00, -3.35_19e00],
             [3.61_69e-02, -5.90_25e00, -2.93_13e00],
             [1.07_66e-04, -7.76_30e00, -5.12_63e00],
         ] ).to(lowercase_ )
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,				lowercase_						,				atol=lowercase_ ) )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					int    =				(
         MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
         .to(lowercase_ )
         .eval()
     )
     lowerCamelCase :					Optional[Any]    =				self.default_image_processor
     lowerCamelCase :					Union[str, Any]    =				prepare_img()
     lowerCamelCase :					int    =				image_processor(lowercase_						,				return_tensors="pt" ).to(lowercase_ )
     lowerCamelCase :					Optional[int]    =				inputs["pixel_values"].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
     # check size
     self.assertEqual(lowercase_						,				(1, 3, 800, 1088) )

     with torch.no_grad():
       lowerCamelCase :					Optional[Any]    =				model(**lowercase_ )
     # masks_queries_logits
     lowerCamelCase :					List[Any]    =				outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape						,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						,				)
     lowerCamelCase :					Tuple    =				[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
     lowerCamelCase :					List[str]    =				torch.tensor(lowercase_ ).to(lowercase_ )
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,				lowercase_						,				atol=lowercase_ ) )
     # class_queries_logits
     lowerCamelCase :					Union[str, Any]    =				outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape						,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
     lowerCamelCase :					Optional[Any]    =				torch.tensor(
         [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]] ).to(lowercase_ )
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,				lowercase_						,				atol=lowercase_ ) )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				(
         MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
         .to(lowercase_ )
         .eval()
     )
     lowerCamelCase :					str    =				self.default_image_processor

     lowerCamelCase :					Optional[Any]    =				image_processor(
         [np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )]						,				segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )]						,				return_tensors="pt"						,				)

     lowerCamelCase :					Tuple    =				inputs["pixel_values"].to(lowercase_ )
     lowerCamelCase :					Dict    =				[el.to(lowercase_ ) for el in inputs["mask_labels"]]
     lowerCamelCase :					Union[str, Any]    =				[el.to(lowercase_ ) for el in inputs["class_labels"]]

     with torch.no_grad():
       lowerCamelCase :					Any    =				model(**lowercase_ )

     self.assertTrue(outputs.loss is not None )









'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


a  :				str									=  logging.get_logger(__name__)

a  :				str									=  {
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"big_bird"


				def __init__(		self    :					Union[str, Any]       ,				lowercase_    :					List[Any]=5_0358       ,				lowercase_    :					Tuple=768       ,				lowercase_    :					Dict=12       ,				lowercase_    :					str=12       ,				lowercase_    :					Tuple=3072       ,				lowercase_    :					Any="gelu_new"       ,				lowercase_    :					Optional[Any]=0.1       ,				lowercase_    :					List[Any]=0.1       ,				lowercase_    :					List[Any]=4096       ,				lowercase_    :					List[Any]=2       ,				lowercase_    :					List[str]=0.02       ,				lowercase_    :					Optional[int]=1e-12       ,				lowercase_    :					Tuple=True       ,				lowercase_    :					Tuple=0       ,				lowercase_    :					str=1       ,				lowercase_    :					Union[str, Any]=2       ,				lowercase_    :					Optional[Any]=66       ,				lowercase_    :					Optional[int]="block_sparse"       ,				lowercase_    :					Any=True       ,				lowercase_    :					List[str]=False       ,				lowercase_    :					Any=64       ,				lowercase_    :					Tuple=3       ,				lowercase_    :					Tuple=None       ,				**lowercase_    :					Tuple       ,				):
									super().__init__(
									    pad_token_id=lowercase_       ,				bos_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				sep_token_id=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_size
									snake_case_									=				max_position_embeddings
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				initializer_range
									snake_case_									=				type_vocab_size
									snake_case_									=				layer_norm_eps
									snake_case_									=				use_cache

									snake_case_									=				rescale_embeddings
									snake_case_									=				attention_type
									snake_case_									=				use_bias
									snake_case_									=				block_size
									snake_case_									=				num_random_blocks
									snake_case_									=				classifier_dropout







class 						a				(   _lowerCamelCase     ):


				@property
				def   A_ (		self    :					str					):
									if self.task == "multiple-choice":
														snake_case_									=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
														snake_case_									=				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ]					)

