'''simple docstring'''

from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    TFLayoutLMvaForQuestionAnswering,
							    TFLayoutLMvaForSequenceClassification,
							    TFLayoutLMvaForTokenClassification,
							    TFLayoutLMvaModel,
							)

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=2		,						__A=3		,						__A=4		,						__A=2		,						__A=7		,						__A=True		,						__A=True		,						__A=True		,						__A=True		,						__A=99		,						__A=36		,						__A=2		,						__A=4		,						__A=37		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=16		,						__A=2		,						__A=0.02		,						__A=6		,						__A=6		,						__A=3		,						__A=4		,						__A=None		,						__A=1000		,						) ->     List[Any]:
								_lowerCAmelCase										=parent
								_lowerCAmelCase										=batch_size
								_lowerCAmelCase										=num_channels
								_lowerCAmelCase										=image_size
								_lowerCAmelCase										=patch_size
								_lowerCAmelCase										=is_training
								_lowerCAmelCase										=use_input_mask
								_lowerCAmelCase										=use_token_type_ids
								_lowerCAmelCase										=use_labels
								_lowerCAmelCase										=vocab_size
								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=hidden_dropout_prob
								_lowerCAmelCase										=attention_probs_dropout_prob
								_lowerCAmelCase										=max_position_embeddings
								_lowerCAmelCase										=type_vocab_size
								_lowerCAmelCase										=type_sequence_label_size
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=coordinate_size
								_lowerCAmelCase										=shape_size
								_lowerCAmelCase										=num_labels
								_lowerCAmelCase										=num_choices
								_lowerCAmelCase										=scope
								_lowerCAmelCase										=range_bbox

								# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
								_lowerCAmelCase										=text_seq_length
								_lowerCAmelCase										=(image_size // patch_size) ** 2 + 1
								_lowerCAmelCase										=self.text_seq_length + self.image_seq_length






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase										=ids_tensor([self.batch_size, self.text_seq_length]		,						self.vocab_size				)

								_lowerCAmelCase										=ids_tensor([self.batch_size, self.text_seq_length, 4]		,						self.range_bbox				)
								_lowerCAmelCase										=bbox.numpy()
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]				):
														for j in range(bbox.shape[1]				):
																				if bbox[i, j, 3] < bbox[i, j, 1]:
																										_lowerCAmelCase										=bbox[i, j, 3]
																										_lowerCAmelCase										=bbox[i, j, 1]
																										_lowerCAmelCase										=tmp_coordinate
																				if bbox[i, j, 2] < bbox[i, j, 0]:
																										_lowerCAmelCase										=bbox[i, j, 2]
																										_lowerCAmelCase										=bbox[i, j, 0]
																										_lowerCAmelCase										=tmp_coordinate
								_lowerCAmelCase										=tf.constant(lowercase_				)

								_lowerCAmelCase										=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								_lowerCAmelCase										=None
								if self.use_input_mask:
														_lowerCAmelCase										=random_attention_mask([self.batch_size, self.text_seq_length]				)

								_lowerCAmelCase										=None
								if self.use_token_type_ids:
														_lowerCAmelCase										=ids_tensor([self.batch_size, self.text_seq_length]		,						self.type_vocab_size				)

								_lowerCAmelCase										=None
								_lowerCAmelCase										=None
								if self.use_labels:
														_lowerCAmelCase										=ids_tensor([self.batch_size]		,						self.type_sequence_label_size				)
														_lowerCAmelCase										=ids_tensor([self.batch_size, self.text_seq_length]		,						self.num_labels				)

								_lowerCAmelCase										=LayoutLMvaConfig(
								    vocab_size=self.vocab_size		,						hidden_size=self.hidden_size		,						num_hidden_layers=self.num_hidden_layers		,						num_attention_heads=self.num_attention_heads		,						intermediate_size=self.intermediate_size		,						hidden_act=self.hidden_act		,						hidden_dropout_prob=self.hidden_dropout_prob		,						attention_probs_dropout_prob=self.attention_probs_dropout_prob		,						max_position_embeddings=self.max_position_embeddings		,						type_vocab_size=self.type_vocab_size		,						initializer_range=self.initializer_range		,						coordinate_size=self.coordinate_size		,						shape_size=self.shape_size		,						input_size=self.image_size		,						patch_size=self.patch_size		,						)

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Dict:
								_lowerCAmelCase										=TFLayoutLMvaModel(config=lowercase_				)

								# text + image
								_lowerCAmelCase										=model(lowercase_		,						pixel_values=lowercase_		,						training=lowercase_				)
								_lowerCAmelCase										=model(
								    lowercase_		,						bbox=lowercase_		,						pixel_values=lowercase_		,						attention_mask=lowercase_		,						token_type_ids=lowercase_		,						training=lowercase_		,						)
								_lowerCAmelCase										=model(lowercase_		,						bbox=lowercase_		,						pixel_values=lowercase_		,						training=lowercase_				)

								self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)				)

								# text only
								_lowerCAmelCase										=model(lowercase_		,						training=lowercase_				)
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,						(self.batch_size, self.text_seq_length, self.hidden_size)				)

								# image only
								_lowerCAmelCase										=model({'pixel_values': pixel_values}		,						training=lowercase_				)
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,						(self.batch_size, self.image_seq_length, self.hidden_size)				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Dict:
								_lowerCAmelCase										=self.num_labels
								_lowerCAmelCase										=TFLayoutLMvaForSequenceClassification(config=lowercase_				)
								_lowerCAmelCase										=model(
								    lowercase_		,						bbox=lowercase_		,						pixel_values=lowercase_		,						attention_mask=lowercase_		,						token_type_ids=lowercase_		,						labels=lowercase_		,						training=lowercase_		,						)
								self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     str:
								_lowerCAmelCase										=self.num_labels
								_lowerCAmelCase										=TFLayoutLMvaForTokenClassification(config=lowercase_				)
								_lowerCAmelCase										=model(
								    lowercase_		,						bbox=lowercase_		,						pixel_values=lowercase_		,						attention_mask=lowercase_		,						token_type_ids=lowercase_		,						labels=lowercase_		,						training=lowercase_		,						)
								self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.text_seq_length, self.num_labels)				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Optional[int]:
								_lowerCAmelCase										=2
								_lowerCAmelCase										=TFLayoutLMvaForQuestionAnswering(config=lowercase_				)
								_lowerCAmelCase										=model(
								    lowercase_		,						bbox=lowercase_		,						pixel_values=lowercase_		,						attention_mask=lowercase_		,						token_type_ids=lowercase_		,						start_positions=lowercase_		,						end_positions=lowercase_		,						training=lowercase_		,						)
								self.parent.assertEqual(result.start_logits.shape		,						(self.batch_size, self.seq_length)				)
								self.parent.assertEqual(result.end_logits.shape		,						(self.batch_size, self.seq_length)				)






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=self.prepare_config_and_inputs()
								((_lowerCAmelCase)						,				(_lowerCAmelCase)						,				(_lowerCAmelCase)						,				(_lowerCAmelCase)						,				(_lowerCAmelCase)						,				(_lowerCAmelCase)						,				(_lowerCAmelCase)						,				(_lowerCAmelCase))										=config_and_inputs
								_lowerCAmelCase										={
								    'input_ids': input_ids,
								    'bbox': bbox,
								    'pixel_values': pixel_values,
								    'token_type_ids': token_type_ids,
								    'attention_mask': input_mask,
								}
								return config, inputs_dict

@require_tf
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      Any       =							(
		    (
		        TFLayoutLMvaModel,
		        TFLayoutLMvaForQuestionAnswering,
		        TFLayoutLMvaForSequenceClassification,
		        TFLayoutLMvaForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		lowercase   :      List[Any]       =							(
		    {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
		    if is_tf_available()
		    else {}
		)

		lowercase   :      List[Any]       =							False
		lowercase   :      List[Any]       =							False
		lowercase   :      List[Any]       =							False






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Optional[int]:
								return True






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A=False				) ->     dict:
								_lowerCAmelCase										=copy.deepcopy(lowercase_				)

								if model_class in get_values(lowercase_				):
														_lowerCAmelCase										={
														    k: tf.tile(tf.expand_dims(lowercase_		,						1				)		,						(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)				)
														    if isinstance(lowercase_		,						tf.Tensor				) and v.ndim > 0
														    else v
														    for k, v in inputs_dict.items()
														}

								if return_labels:
														if model_class in get_values(lowercase_				):
																				_lowerCAmelCase										=tf.ones(self.model_tester.batch_size		,						dtype=tf.intaa				)
														elif model_class in get_values(lowercase_				):
																				_lowerCAmelCase										=tf.zeros(self.model_tester.batch_size		,						dtype=tf.intaa				)
																				_lowerCAmelCase										=tf.zeros(self.model_tester.batch_size		,						dtype=tf.intaa				)
														elif model_class in get_values(lowercase_				):
																				_lowerCAmelCase										=tf.zeros(self.model_tester.batch_size		,						dtype=tf.intaa				)
														elif model_class in get_values(lowercase_				):
																				_lowerCAmelCase										=tf.zeros(
																				    (self.model_tester.batch_size, self.model_tester.text_seq_length)		,						dtype=tf.intaa				)

								return inputs_dict






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=TFLayoutLMvaModelTester(self				)
								_lowerCAmelCase										=ConfigTester(self		,						config_class=lowercase_		,						hidden_size=37				)






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								self.config_tester.run_common_tests()






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
														_lowerCAmelCase										=model_class(lowercase_				)
														if getattr(lowercase_		,						'hf_compute_loss'		,						lowercase_				):
																				# The number of elements in the loss should be the same as the number of elements in the label
																				_lowerCAmelCase										=self._prepare_for_class(inputs_dict.copy()		,						lowercase_		,						return_labels=lowercase_				)
																				_lowerCAmelCase										=prepared_for_class[
																				    sorted(prepared_for_class.keys() - inputs_dict.keys()		,						reverse=lowercase_				)[0]
																				]
																				_lowerCAmelCase										=added_label.shape.as_list()[:1]

																				# Test that model correctly compute the loss with kwargs
																				_lowerCAmelCase										=self._prepare_for_class(inputs_dict.copy()		,						lowercase_		,						return_labels=lowercase_				)
																				_lowerCAmelCase										=prepared_for_class.pop('input_ids'				)

																				_lowerCAmelCase										=model(lowercase_		,						**lowercase_				)[0]
																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)

																				# Test that model correctly compute the loss when we mask some positions
																				_lowerCAmelCase										=self._prepare_for_class(inputs_dict.copy()		,						lowercase_		,						return_labels=lowercase_				)
																				_lowerCAmelCase										=prepared_for_class.pop('input_ids'				)
																				if "labels" in prepared_for_class:
																										_lowerCAmelCase										=prepared_for_class['labels'].numpy()
																										if len(labels.shape				) > 1 and labels.shape[1] != 1:
																																_lowerCAmelCase										=-100
																																_lowerCAmelCase										=tf.convert_to_tensor(lowercase_				)
																																_lowerCAmelCase										=model(lowercase_		,						**lowercase_				)[0]
																																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)
																																self.assertTrue(not np.any(np.isnan(loss.numpy()				)				)				)

																																     # Test that model correctly compute the loss with a dict
																				_lowerCAmelCase										=self._prepare_for_class(inputs_dict.copy()		,						lowercase_		,						return_labels=lowercase_				)
																				_lowerCAmelCase										=model(lowercase_				)[0]
																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)

																				# Test that model correctly compute the loss with a tuple
																				_lowerCAmelCase										=self._prepare_for_class(inputs_dict.copy()		,						lowercase_		,						return_labels=lowercase_				)

																				# Get keys that were added with the _prepare_for_class function
																				_lowerCAmelCase										=prepared_for_class.keys() - inputs_dict.keys()
																				_lowerCAmelCase										=inspect.signature(model.call				).parameters
																				_lowerCAmelCase										=list(signature.keys()				)

																				# Create a dictionary holding the location of the tensors in the tuple
																				_lowerCAmelCase										={0: 'input_ids'}
																				for label_key in label_keys:
																										_lowerCAmelCase										=signature_names.index(lowercase_				)
																										_lowerCAmelCase										=label_key
																				_lowerCAmelCase										=sorted(tuple_index_mapping.items()				)
																				# Initialize a list with their default values, update the values and convert to a tuple
																				_lowerCAmelCase										=[]

																				for name in signature_names:
																										if name != "kwargs":
																																list_input.append(signature[name].default				)

																				for index, value in sorted_tuple_index_mapping:
																										_lowerCAmelCase										=prepared_for_class[value]

																				_lowerCAmelCase										=tuple(lowercase_				)

																				# Send to model
																				_lowerCAmelCase										=model(tuple_input[:-1]				)[0]

																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								(
								    (
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,					
								)										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_				)






		def 		UpperCamelCase__       (   self				) ->     Dict:
								(
								    (
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,					
								)										=self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
														_lowerCAmelCase										=type
														self.model_tester.create_and_check_model(lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								(
								    (
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,					
								)										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(
								    lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								(
								    (
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,					
								)										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(
								    lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_				)






		def 		UpperCamelCase__       (   self				) ->     int:
								(
								    (
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,				(
								    _lowerCAmelCase	
								)						,					
								)										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(
								    lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_				)






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







								for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCAmelCase										=TFLayoutLMvaModel.from_pretrained(lowercase_				)
														self.assertIsNotNone(lowercase_				)



def 							UpperCamelCase__			(			)     ->   Any:


						'''simple docstring'''






						_lowerCAmelCase										=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
						return image


@require_tf
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		@cached_property
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								return LayoutLMvaImageProcessor(apply_ocr=lowercase_				) if is_vision_available() else None






		@slow
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'				)

								_lowerCAmelCase										=self.default_image_processor
								_lowerCAmelCase										=prepare_img()
								_lowerCAmelCase										=image_processor(images=lowercase_		,						return_tensors='tf'				).pixel_values

								_lowerCAmelCase										=tf.constant([[1, 2]]				)
								_lowerCAmelCase										=tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]				)		,						axis=0				)

								# forward pass
								_lowerCAmelCase										=model(input_ids=lowercase_		,						bbox=lowercase_		,						pixel_values=lowercase_		,						training=lowercase_				)

								# verify the logits
								_lowerCAmelCase										=(1, 199, 768)
								self.assertEqual(outputs.last_hidden_state.shape		,						lowercase_				)

								_lowerCAmelCase										=tf.constant(
								    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]				)

								self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]		,						lowercase_		,						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


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							'data2vec-text'






		def __init__(   self		,						__A=3_0522		,						__A=768		,						__A=12		,						__A=12		,						__A=3072		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=2		,						__A=0.02		,						__A=1E-12		,						__A=1		,						__A=0		,						__A=2		,						__A="absolute"		,						__A=True		,						__A=None		,						**__A		,						) ->     List[Any]:
								super().__init__(pad_token_id=__A		,						bos_token_id=__A		,						eos_token_id=__A		,						**__A				)

								_lowerCAmelCase										=vocab_size
								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=hidden_dropout_prob
								_lowerCAmelCase										=attention_probs_dropout_prob
								_lowerCAmelCase										=max_position_embeddings
								_lowerCAmelCase										=type_vocab_size
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=layer_norm_eps
								_lowerCAmelCase										=position_embedding_type
								_lowerCAmelCase										=use_cache
								_lowerCAmelCase										=classifier_dropout

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






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





