


"""simple docstring"""







import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_MAPPING,
					    BeitForImageClassification,
					    BeitForMaskedImageModeling,
					    BeitForSemanticSegmentation,
					    BeitModel,
					)
					from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					import PIL
					from PIL import Image

					from transformers import BeitImageProcessor


class 				SCREAMING_SNAKE_CASE__    :
							"""simple docstring"""


							def __init__(						self		,					snake_case__		,					snake_case__=100		,					snake_case__=13		,					snake_case__=30		,					snake_case__=2		,					snake_case__=3		,					snake_case__=True		,					snake_case__=True		,					snake_case__=32		,					snake_case__=4		,					snake_case__=4		,					snake_case__=37		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=10		,					snake_case__=0.02		,					snake_case__=3		,					snake_case__=None		,					snake_case__=[0, 1, 2, 3]		,					):






									"""simple docstring"""





									lowerCAmelCase					:					Tuple								=							parent
									lowerCAmelCase					:					Union[str, Any]								=							100
									lowerCAmelCase					:					Optional[int]								=							batch_size
									lowerCAmelCase					:					Any								=							image_size
									lowerCAmelCase					:					Optional[int]								=							patch_size
									lowerCAmelCase					:					Any								=							num_channels
									lowerCAmelCase					:					List[Any]								=							is_training
									lowerCAmelCase					:					Dict								=							use_labels
									lowerCAmelCase					:					Tuple								=							hidden_size
									lowerCAmelCase					:					Optional[Any]								=							num_hidden_layers
									lowerCAmelCase					:					int								=							num_attention_heads
									lowerCAmelCase					:					Dict								=							intermediate_size
									lowerCAmelCase					:					Dict								=							hidden_act
									lowerCAmelCase					:					Optional[Any]								=							hidden_dropout_prob
									lowerCAmelCase					:					str								=							attention_probs_dropout_prob
									lowerCAmelCase					:					Tuple								=							type_sequence_label_size
									lowerCAmelCase					:					Any								=							initializer_range
									lowerCAmelCase					:					List[str]								=							scope
									lowerCAmelCase					:					Union[str, Any]								=							out_indices
									lowerCAmelCase					:					List[str]								=							num_labels

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									lowerCAmelCase					:					Any								=							(image_size // patch_size) ** 2
									lowerCAmelCase					:					int								=							num_patches + 1

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Union[str, Any]								=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									lowerCAmelCase					:					List[str]								=							None
									lowerCAmelCase					:					Optional[int]								=							None
									if self.use_labels:
											lowerCAmelCase					:					List[Any]								=							ids_tensor([self.batch_size]		,					self.type_sequence_label_size						)
											lowerCAmelCase					:					Union[str, Any]								=							ids_tensor([self.batch_size, self.image_size, self.image_size]		,					self.num_labels						)

									lowerCAmelCase					:					Dict								=							self.get_config()

									return config, pixel_values, labels, pixel_labels

							def 	lowercase__							(						self						):






									"""simple docstring"""





									return BeitConfig(
									    vocab_size=self.vocab_size		,					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=snake_case__		,					initializer_range=self.initializer_range		,					out_indices=self.out_indices		,					)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					int								=							BeitModel(config=snake_case__						)
									model.to(snake_case__						)
									model.eval()
									lowerCAmelCase					:					Any								=							model(snake_case__						)
									self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)						)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					Tuple								=							BeitForMaskedImageModeling(config=snake_case__						)
									model.to(snake_case__						)
									model.eval()
									lowerCAmelCase					:					str								=							model(snake_case__						)
									self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length - 1, self.vocab_size)						)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					int								=							self.type_sequence_label_size
									lowerCAmelCase					:					Any								=							BeitForImageClassification(snake_case__						)
									model.to(snake_case__						)
									model.eval()
									lowerCAmelCase					:					Optional[Any]								=							model(snake_case__		,					labels=snake_case__						)
									self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size)						)

									# test greyscale images
									lowerCAmelCase					:					Optional[int]								=							1
									lowerCAmelCase					:					Tuple								=							BeitForImageClassification(snake_case__						)
									model.to(snake_case__						)
									model.eval()

									lowerCAmelCase					:					Optional[Any]								=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
									lowerCAmelCase					:					List[Any]								=							model(snake_case__		,					labels=snake_case__						)
									self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size)						)

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__						):






									"""simple docstring"""





									lowerCAmelCase					:					Tuple								=							self.num_labels
									lowerCAmelCase					:					int								=							BeitForSemanticSegmentation(snake_case__						)
									model.to(snake_case__						)
									model.eval()
									lowerCAmelCase					:					List[Any]								=							model(snake_case__						)
									self.parent.assertEqual(
									    result.logits.shape		,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
									lowerCAmelCase					:					List[Any]								=							model(snake_case__		,					labels=snake_case__						)
									self.parent.assertEqual(
									    result.logits.shape		,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)







							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Optional[int]								=							self.prepare_config_and_inputs()
									lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					Optional[int]								=							config_and_inputs
									lowerCAmelCase					:					List[Any]								=							{"pixel_values": pixel_values}
									return config, inputs_dict


@require_torch
class 				SCREAMING_SNAKE_CASE__    (		lowerCamelCase__       ,   lowerCamelCase__       ,   unittest.TestCase    ):
							"""simple docstring"""


							a  :    Tuple       =(
							    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							a  :    List[str]       =(
							    {
							        'feature-extraction': BeitModel,
							        'image-classification': BeitForImageClassification,
							        'image-segmentation': BeitForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							a  :    List[Any]       =False
							a  :    Optional[int]       =False
							a  :    List[str]       =False

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Optional[Any]								=							BeitModelTester(self						)
									lowerCAmelCase					:					Optional[Any]								=							ConfigTester(self		,					config_class=snake_case__		,					has_text_modality=snake_case__		,					hidden_size=37						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									self.config_tester.run_common_tests()

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






									"""simple docstring"""





									pass

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






									"""simple docstring"""





									pass

							def 	lowercase__							(						self						):






									"""simple docstring"""





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

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

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase						,  lowerCAmelCase					:					Optional[Any]								=							self.model_tester.prepare_config_and_inputs_for_common()

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

											lowerCAmelCase					:					str								=							["pixel_values"]
											self.assertListEqual(arg_names[:1]		,					snake_case__						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					int								=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case__						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					List[str]								=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*snake_case__						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Union[str, Any]								=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case__						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Any								=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__						)

							def 	lowercase__							(						self						):






									"""simple docstring"""





									if not self.model_tester.is_training:
											return

									lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase					:					Tuple								=							True

									for model_class in self.all_model_classes:
											# we don't test BeitForMaskedImageModeling
											if model_class in [*get_values(snake_case__						), BeitForMaskedImageModeling]:
													continue

											lowerCAmelCase					:					int								=							model_class(snake_case__						)
											model.to(snake_case__						)
											model.train()
											lowerCAmelCase					:					Optional[int]								=							self._prepare_for_class(snake_case__		,					snake_case__		,					return_labels=snake_case__						)
											lowerCAmelCase					:					Optional[int]								=							model(**snake_case__						).loss
											loss.backward()

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase						,  lowerCAmelCase					:					List[Any]								=							self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
											return

									lowerCAmelCase					:					int								=							False
									lowerCAmelCase					:					Union[str, Any]								=							True

									for model_class in self.all_model_classes:
											# we don't test BeitForMaskedImageModeling
											if (
											    model_class in [*get_values(snake_case__						), BeitForMaskedImageModeling]
											    or not model_class.supports_gradient_checkpointing
											):
													continue

											lowerCAmelCase					:					List[Any]								=							model_class(snake_case__						)
											model.gradient_checkpointing_enable()
											model.to(snake_case__						)
											model.train()
											lowerCAmelCase					:					Optional[int]								=							self._prepare_for_class(snake_case__		,					snake_case__		,					return_labels=snake_case__						)
											lowerCAmelCase					:					Dict								=							model(**snake_case__						).loss
											loss.backward()

							def 	lowercase__							(						self						):






									"""simple docstring"""





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

									lowerCAmelCase					:					int								=							_config_zero_init(snake_case__						)
									for model_class in self.all_model_classes:
											lowerCAmelCase					:					int								=							model_class(config=snake_case__						)
											for name, param in model.named_parameters():
													# we skip lambda parameters as these require special initial values
													# determined by config.layer_scale_init_value
													if "lambda" in name:
															continue
													if param.requires_grad:
															self.assertIn(
															    ((param.data.mean() * 1e9).round() / 1e9).item()		,					[0.0, 1.0]		,					msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,					)







							@slow
							def 	lowercase__							(						self						):






									"""simple docstring"""





									for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCAmelCase					:					str								=							BeitModel.from_pretrained(snake_case__						)
											self.assertIsNotNone(snake_case__						)

def  a__      (    ):


		'''simple docstring'''







		lowerCAmelCase					:					Optional[int]								=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image





@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
							"""simple docstring"""


							@cached_property
							def 	lowercase__							(						self						):






									"""simple docstring"""





									return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"						) if is_vision_available() else None

							@slow
							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					List[str]								=							BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"						).to(snake_case__						)

									lowerCAmelCase					:					str								=							self.default_image_processor
									lowerCAmelCase					:					int								=							prepare_img()
									lowerCAmelCase					:					Optional[int]								=							image_processor(images=snake_case__		,					return_tensors="pt"						).pixel_values.to(snake_case__						)

									# prepare bool_masked_pos
									lowerCAmelCase					:					Optional[Any]								=							torch.ones((1, 196)		,					dtype=torch.bool						).to(snake_case__						)

									# forward pass
									with torch.no_grad():
											lowerCAmelCase					:					Tuple								=							model(pixel_values=snake_case__		,					bool_masked_pos=snake_case__						)
									lowerCAmelCase					:					str								=							outputs.logits

									# verify the logits
									lowerCAmelCase					:					str								=							torch.Size((1, 196, 8_192)						)
									self.assertEqual(logits.shape		,					snake_case__						)

									lowerCAmelCase					:					int								=							torch.tensor(
									    [[-3.2437, 0.5072, -13.9_174], [-3.2456, 0.4948, -13.9_401], [-3.2033, 0.5121, -13.8_550]]						).to(snake_case__						)

									self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]		,					snake_case__		,					atol=1e-2						)						)

							@slow
							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Union[str, Any]								=							BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"						).to(snake_case__						)

									lowerCAmelCase					:					Any								=							self.default_image_processor
									lowerCAmelCase					:					Tuple								=							prepare_img()
									lowerCAmelCase					:					Any								=							image_processor(images=snake_case__		,					return_tensors="pt"						).to(snake_case__						)

									# forward pass
									with torch.no_grad():
											lowerCAmelCase					:					str								=							model(**snake_case__						)
									lowerCAmelCase					:					List[str]								=							outputs.logits

									# verify the logits
									lowerCAmelCase					:					List[str]								=							torch.Size((1, 1_000)						)
									self.assertEqual(logits.shape		,					snake_case__						)

									lowerCAmelCase					:					List[str]								=							torch.tensor([-1.2385, -1.0987, -1.0108]						).to(snake_case__						)

									self.assertTrue(torch.allclose(logits[0, :3]		,					snake_case__		,					atol=1e-4						)						)

									lowerCAmelCase					:					List[Any]								=							281
									self.assertEqual(logits.argmax(-1						).item()		,					snake_case__						)

							@slow
							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Optional[Any]								=							BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"						).to(
									    snake_case__						)

									lowerCAmelCase					:					str								=							self.default_image_processor
									lowerCAmelCase					:					Optional[int]								=							prepare_img()
									lowerCAmelCase					:					List[str]								=							image_processor(images=snake_case__		,					return_tensors="pt"						).to(snake_case__						)

									# forward pass
									with torch.no_grad():
											lowerCAmelCase					:					Optional[int]								=							model(**snake_case__						)
									lowerCAmelCase					:					str								=							outputs.logits

									# verify the logits
									lowerCAmelCase					:					str								=							torch.Size((1, 21_841)						)
									self.assertEqual(logits.shape		,					snake_case__						)

									lowerCAmelCase					:					List[str]								=							torch.tensor([1.6881, -0.2787, 0.5901]						).to(snake_case__						)

									self.assertTrue(torch.allclose(logits[0, :3]		,					snake_case__		,					atol=1e-4						)						)

									lowerCAmelCase					:					Optional[Any]								=							2_396
									self.assertEqual(logits.argmax(-1						).item()		,					snake_case__						)

							@slow
							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Optional[int]								=							BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"						)
									lowerCAmelCase					:					Optional[int]								=							model.to(snake_case__						)

									lowerCAmelCase					:					Any								=							BeitImageProcessor(do_resize=snake_case__		,					size=640		,					do_center_crop=snake_case__						)

									lowerCAmelCase					:					Optional[int]								=							load_dataset("hf-internal-testing/fixtures_ade20k"		,					split="test"						)
									lowerCAmelCase					:					Union[str, Any]								=							Image.open(ds[0]["file"]						)
									lowerCAmelCase					:					Optional[Any]								=							image_processor(images=snake_case__		,					return_tensors="pt"						).to(snake_case__						)

									# forward pass
									with torch.no_grad():
											lowerCAmelCase					:					Dict								=							model(**snake_case__						)
									lowerCAmelCase					:					List[Any]								=							outputs.logits

									# verify the logits
									lowerCAmelCase					:					Tuple								=							torch.Size((1, 150, 160, 160)						)
									self.assertEqual(logits.shape		,					snake_case__						)

									lowerCAmelCase					:					List[str]								=							version.parse(PIL.__version__						) < version.parse("9.0.0"						)

									if is_pillow_less_than_a:
											lowerCAmelCase					:					str								=							torch.tensor(
											    [
											        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
											        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
											        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
											    ]		,					device=snake_case__		,					)
									else:
											lowerCAmelCase					:					List[str]								=							torch.tensor(
											    [
											        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
											        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
											        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
											    ]		,					device=snake_case__		,					)

									self.assertTrue(torch.allclose(logits[0, :3, :3, :3]		,					snake_case__		,					atol=1e-4						)						)







							@slow
							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Tuple								=							BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"						)
									lowerCAmelCase					:					Optional[int]								=							model.to(snake_case__						)

									lowerCAmelCase					:					Optional[Any]								=							BeitImageProcessor(do_resize=snake_case__		,					size=640		,					do_center_crop=snake_case__						)

									lowerCAmelCase					:					List[str]								=							load_dataset("hf-internal-testing/fixtures_ade20k"		,					split="test"						)
									lowerCAmelCase					:					Any								=							Image.open(ds[0]["file"]						)
									lowerCAmelCase					:					Any								=							image_processor(images=snake_case__		,					return_tensors="pt"						).to(snake_case__						)

									# forward pass
									with torch.no_grad():
											lowerCAmelCase					:					int								=							model(**snake_case__						)

									lowerCAmelCase					:					List[Any]								=							outputs.logits.detach().cpu()

									lowerCAmelCase					:					str								=							image_processor.post_process_semantic_segmentation(outputs=snake_case__		,					target_sizes=[(500, 300)]						)
									lowerCAmelCase					:					List[Any]								=							torch.Size((500, 300)						)
									self.assertEqual(segmentation[0].shape		,					snake_case__						)

									lowerCAmelCase					:					str								=							image_processor.post_process_semantic_segmentation(outputs=snake_case__						)
									lowerCAmelCase					:					Union[str, Any]								=							torch.Size((160, 160)						)
									self.assertEqual(segmentation[0].shape		,					snake_case__						)









"""simple docstring"""







from ...configuration_utils import PretrainedConfig


lowerCAmelCase__				    =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Any       ="tapas"

       def __init__(						self		,					snake_case__=30_522		,					snake_case__=768		,					snake_case__=12		,					snake_case__=12		,					snake_case__=3_072		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=1_024		,					snake_case__=[3, 256, 256, 2, 256, 256, 10]		,					snake_case__=0.02		,					snake_case__=1e-12		,					snake_case__=0		,					snake_case__=10.0		,					snake_case__=0		,					snake_case__=1.0		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=False		,					snake_case__="ratio"		,					snake_case__=None		,					snake_case__=None		,					snake_case__=64		,					snake_case__=32		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=None		,					snake_case__=None		,					**snake_case__		,					):






         """simple docstring"""





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

         # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
         lowerCAmelCase					:					List[Any]								=							vocab_size
         lowerCAmelCase					:					List[str]								=							hidden_size
         lowerCAmelCase					:					Optional[Any]								=							num_hidden_layers
         lowerCAmelCase					:					List[str]								=							num_attention_heads
         lowerCAmelCase					:					Optional[Any]								=							hidden_act
         lowerCAmelCase					:					List[Any]								=							intermediate_size
         lowerCAmelCase					:					Optional[Any]								=							hidden_dropout_prob
         lowerCAmelCase					:					str								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Any								=							max_position_embeddings
         lowerCAmelCase					:					Dict								=							type_vocab_sizes
         lowerCAmelCase					:					Union[str, Any]								=							initializer_range
         lowerCAmelCase					:					str								=							layer_norm_eps

         # Fine-tuning task hyperparameters
         lowerCAmelCase					:					Dict								=							positive_label_weight
         lowerCAmelCase					:					Union[str, Any]								=							num_aggregation_labels
         lowerCAmelCase					:					Optional[Any]								=							aggregation_loss_weight
         lowerCAmelCase					:					List[Any]								=							use_answer_as_supervision
         lowerCAmelCase					:					Dict								=							answer_loss_importance
         lowerCAmelCase					:					List[Any]								=							use_normalized_answer_loss
         lowerCAmelCase					:					List[str]								=							huber_loss_delta
         lowerCAmelCase					:					Optional[int]								=							temperature
         lowerCAmelCase					:					Optional[int]								=							aggregation_temperature
         lowerCAmelCase					:					Any								=							use_gumbel_for_cells
         lowerCAmelCase					:					Union[str, Any]								=							use_gumbel_for_aggregation
         lowerCAmelCase					:					Union[str, Any]								=							average_approximation_function
         lowerCAmelCase					:					int								=							cell_selection_preference
         lowerCAmelCase					:					Dict								=							answer_loss_cutoff
         lowerCAmelCase					:					Optional[int]								=							max_num_rows
         lowerCAmelCase					:					Union[str, Any]								=							max_num_columns
         lowerCAmelCase					:					Any								=							average_logits_per_cell
         lowerCAmelCase					:					List[Any]								=							select_one_column
         lowerCAmelCase					:					Tuple								=							allow_empty_column_selection
         lowerCAmelCase					:					str								=							init_cell_selection_weights_to_zero
         lowerCAmelCase					:					List[Any]								=							reset_position_index_per_cell
         lowerCAmelCase					:					Optional[Any]								=							disable_per_token_loss

         # Aggregation hyperparameters
         lowerCAmelCase					:					List[str]								=							aggregation_labels
         lowerCAmelCase					:					List[str]								=							no_aggregation_label_index

         if isinstance(self.aggregation_labels		,					snake_case__						):
           lowerCAmelCase					:					Union[str, Any]								=							{int(snake_case__						): v for k, v in aggregation_labels.items()}





