


import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         MobileBertForMaskedLM,
         MobileBertForMultipleChoice,
         MobileBertForNextSentencePrediction,
         MobileBertForPreTraining,
         MobileBertForQuestionAnswering,
         MobileBertForSequenceClassification,
         MobileBertForTokenClassification,
         MobileBertModel,
     )

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=99       ,_SCREAMING_SNAKE_CASE=64       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=None       ,)			->		Tuple:
         UpperCAmelCase_      :							List[Any]       =      parent
         UpperCAmelCase_      :							List[Any]       =      batch_size
         UpperCAmelCase_      :							Optional[int]       =      seq_length
         UpperCAmelCase_      :							int       =      is_training
         UpperCAmelCase_      :							Optional[Any]       =      use_input_mask
         UpperCAmelCase_      :							Tuple       =      use_token_type_ids
         UpperCAmelCase_      :							Union[str, Any]       =      use_labels
         UpperCAmelCase_      :							List[str]       =      vocab_size
         UpperCAmelCase_      :							int       =      hidden_size
         UpperCAmelCase_      :							str       =      embedding_size
         UpperCAmelCase_      :							str       =      num_hidden_layers
         UpperCAmelCase_      :							Union[str, Any]       =      num_attention_heads
         UpperCAmelCase_      :							Tuple       =      intermediate_size
         UpperCAmelCase_      :							int       =      hidden_act
         UpperCAmelCase_      :							List[Any]       =      hidden_dropout_prob
         UpperCAmelCase_      :							int       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Any       =      max_position_embeddings
         UpperCAmelCase_      :							int       =      type_vocab_size
         UpperCAmelCase_      :							Tuple       =      type_sequence_label_size
         UpperCAmelCase_      :							Optional[Any]       =      initializer_range
         UpperCAmelCase_      :							Optional[Any]       =      num_labels
         UpperCAmelCase_      :							Optional[int]       =      num_choices
         UpperCAmelCase_      :							List[Any]       =      scope





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							Optional[Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size  )

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

         UpperCAmelCase_      :							Any       =      None
         if self.use_token_type_ids:
              UpperCAmelCase_      :							List[Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.type_vocab_size  )

         UpperCAmelCase_      :							Dict       =      None
         UpperCAmelCase_      :							int       =      None
         UpperCAmelCase_      :							Dict       =      None
         if self.use_labels:
              UpperCAmelCase_      :							Tuple       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )
              UpperCAmelCase_      :							Optional[Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels  )
              UpperCAmelCase_      :							List[str]       =      ids_tensor([self.batch_size]       ,self.num_choices  )

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

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





    def 							a__				(				self  )			->		str:
         return MobileBertConfig(
             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       ,embedding_size=self.embedding_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       ,is_decoder=__lowerCAmelCase       ,initializer_range=self.initializer_range       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							List[Any]       =      MobileBertModel(config=__lowerCAmelCase  )
         model.to(__lowerCAmelCase  )
         model.eval()
         UpperCAmelCase_      :							int       =      model(__lowerCAmelCase       ,attention_mask=__lowerCAmelCase       ,token_type_ids=__lowerCAmelCase  )
         UpperCAmelCase_      :							Dict       =      model(__lowerCAmelCase       ,token_type_ids=__lowerCAmelCase  )
         UpperCAmelCase_      :							Any       =      model(__lowerCAmelCase  )

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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         UpperCAmelCase_      :							Tuple       =      MobileBertForMaskedLM(config=__lowerCAmelCase  )
         model.to(__lowerCAmelCase  )
         model.eval()
         UpperCAmelCase_      :							List[str]       =      model(__lowerCAmelCase       ,attention_mask=__lowerCAmelCase       ,token_type_ids=__lowerCAmelCase       ,labels=__lowerCAmelCase  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							Dict       =      MobileBertForNextSentencePrediction(config=__lowerCAmelCase  )
         model.to(__lowerCAmelCase  )
         model.eval()
         UpperCAmelCase_      :							int       =      model(
             __lowerCAmelCase       ,attention_mask=__lowerCAmelCase       ,token_type_ids=__lowerCAmelCase       ,labels=__lowerCAmelCase       ,)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, 2)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							str       =      MobileBertForPreTraining(config=__lowerCAmelCase  )
         model.to(__lowerCAmelCase  )
         model.eval()
         UpperCAmelCase_      :							Tuple       =      model(
             __lowerCAmelCase       ,attention_mask=__lowerCAmelCase       ,token_type_ids=__lowerCAmelCase       ,labels=__lowerCAmelCase       ,next_sentence_label=__lowerCAmelCase       ,)
         self.parent.assertEqual(result.prediction_logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)  )
         self.parent.assertEqual(result.seq_relationship_logits.shape       ,(self.batch_size, 2)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							List[Any]       =      MobileBertForQuestionAnswering(config=__lowerCAmelCase  )
         model.to(__lowerCAmelCase  )
         model.eval()
         UpperCAmelCase_      :							List[str]       =      model(
             __lowerCAmelCase       ,attention_mask=__lowerCAmelCase       ,token_type_ids=__lowerCAmelCase       ,start_positions=__lowerCAmelCase       ,end_positions=__lowerCAmelCase       ,)
         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 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							int       =      self.num_labels
         UpperCAmelCase_      :							Dict       =      MobileBertForSequenceClassification(__lowerCAmelCase  )
         model.to(__lowerCAmelCase  )
         model.eval()
         UpperCAmelCase_      :							Tuple       =      model(__lowerCAmelCase       ,attention_mask=__lowerCAmelCase       ,token_type_ids=__lowerCAmelCase       ,labels=__lowerCAmelCase  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         UpperCAmelCase_      :							Any       =      self.num_labels
         UpperCAmelCase_      :							Optional[Any]       =      MobileBertForTokenClassification(config=__lowerCAmelCase  )
         model.to(__lowerCAmelCase  )
         model.eval()
         UpperCAmelCase_      :							Optional[int]       =      model(__lowerCAmelCase       ,attention_mask=__lowerCAmelCase       ,token_type_ids=__lowerCAmelCase       ,labels=__lowerCAmelCase  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							Dict       =      self.num_choices
         UpperCAmelCase_      :							Dict       =      MobileBertForMultipleChoice(config=__lowerCAmelCase  )
         model.to(__lowerCAmelCase  )
         model.eval()
         UpperCAmelCase_      :							Any       =      input_ids.unsqueeze(1  ).expand(-1       ,self.num_choices       ,-1  ).contiguous()
         UpperCAmelCase_      :							Dict       =      token_type_ids.unsqueeze(1  ).expand(-1       ,self.num_choices       ,-1  ).contiguous()
         UpperCAmelCase_      :							List[str]       =      input_mask.unsqueeze(1  ).expand(-1       ,self.num_choices       ,-1  ).contiguous()
         UpperCAmelCase_      :							str       =      model(
             __lowerCAmelCase       ,attention_mask=__lowerCAmelCase       ,token_type_ids=__lowerCAmelCase       ,labels=__lowerCAmelCase       ,)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_choices)  )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							Any       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_       
         ),   (
             UpperCAmelCase_       
         ),   (
             UpperCAmelCase_       
         ),   (
             UpperCAmelCase_       
         ),   (
             UpperCAmelCase_       
         ),   (
             UpperCAmelCase_       
         ),   (
             UpperCAmelCase_       
         ),          
         )      :							Any       =      config_and_inputs
         UpperCAmelCase_      :							Tuple       =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				(
        (
            MobileBertModel,
            MobileBertForMaskedLM,
            MobileBertForMultipleChoice,
            MobileBertForNextSentencePrediction,
            MobileBertForPreTraining,
            MobileBertForQuestionAnswering,
            MobileBertForSequenceClassification,
            MobileBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase        =				(
        {
            """feature-extraction""": MobileBertModel,
            """fill-mask""": MobileBertForMaskedLM,
            """question-answering""": MobileBertForQuestionAnswering,
            """text-classification""": MobileBertForSequenceClassification,
            """token-classification""": MobileBertForTokenClassification,
            """zero-shot""": MobileBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCAmelCase        =				True





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False  )			->		str:
         UpperCAmelCase_      :							str       =      super()._prepare_for_class(__lowerCAmelCase       ,__lowerCAmelCase       ,return_labels=__lowerCAmelCase  )

         if return_labels:
              if model_class in get_values(__lowerCAmelCase  ):
                   UpperCAmelCase_      :							Dict       =      torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)       ,dtype=torch.long       ,device=__lowerCAmelCase  )
                   UpperCAmelCase_      :							Any       =      torch.zeros(
                       self.model_tester.batch_size       ,dtype=torch.long       ,device=__lowerCAmelCase  )
         return inputs_dict





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							List[str]       =      MobileBertModelTester(self  )
         UpperCAmelCase_      :							List[Any]       =      ConfigTester(self       ,config_class=__lowerCAmelCase       ,hidden_size=37  )





    def 							a__				(				self  )			->		Optional[Any]:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							List[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase  )





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase  )





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							str       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase  )





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							str       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Any       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							str       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase  )







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     return torch.tensor(
         a__			,						dtype=torch.long			,						device=a__			,						)


__a          =			1E-3



@require_torch
@require_sentencepiece
@require_tokenizers
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    @slow
    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							Dict       =      MobileBertModel.from_pretrained('''google/mobilebert-uncased'''  ).to(__lowerCAmelCase  )
         UpperCAmelCase_      :							Union[str, Any]       =      _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]  )
         with torch.no_grad():
              UpperCAmelCase_      :							Optional[int]       =      model(__lowerCAmelCase  )[0]
         UpperCAmelCase_      :							Dict       =      torch.Size((1, 9, 512)  )
         self.assertEqual(output.shape       ,__lowerCAmelCase  )
         UpperCAmelCase_      :							Any       =      torch.tensor(
             [
                 [
                     [-2.4_73_65_26e07, 8.2_69_16_56e04, 1.6_52_18_38e05],
                     [-5.7_54_17_04e-01, 3.9_05_60_22e00, 4.4_01_15_07e00],
                     [2.6_04_73_59e00, 1.5_67_76_52e00, -1.7_32_41_88e-01],
                 ]
             ]       ,device=__lowerCAmelCase       ,)

         # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
         # ~1 difference, it's therefore not a good idea to measure using addition.
         # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
         # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
         UpperCAmelCase_      :							List[str]       =      torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE  )
         UpperCAmelCase_      :							Optional[Any]       =      torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE  )

         self.assertTrue(lower_bound and upper_bound  )





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
     from ...processing_utils import ProcessorMixin
     from ...utils import TensorType


__a          =			logging.get_logger(__name__)

__a          =			{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''layoutlmv3'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=50_265       ,_SCREAMING_SNAKE_CASE=768       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=3_072       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=1e-5       ,_SCREAMING_SNAKE_CASE=1       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=1_024       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=64       ,_SCREAMING_SNAKE_CASE=256       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=224       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         super().__init__(
             vocab_size=_SCREAMING_SNAKE_CASE       ,hidden_size=_SCREAMING_SNAKE_CASE       ,num_hidden_layers=_SCREAMING_SNAKE_CASE       ,num_attention_heads=_SCREAMING_SNAKE_CASE       ,intermediate_size=_SCREAMING_SNAKE_CASE       ,hidden_act=_SCREAMING_SNAKE_CASE       ,hidden_dropout_prob=_SCREAMING_SNAKE_CASE       ,attention_probs_dropout_prob=_SCREAMING_SNAKE_CASE       ,max_position_embeddings=_SCREAMING_SNAKE_CASE       ,type_vocab_size=_SCREAMING_SNAKE_CASE       ,initializer_range=_SCREAMING_SNAKE_CASE       ,layer_norm_eps=_SCREAMING_SNAKE_CASE       ,pad_token_id=_SCREAMING_SNAKE_CASE       ,bos_token_id=_SCREAMING_SNAKE_CASE       ,eos_token_id=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							Dict       =      max_ad_position_embeddings
         UpperCAmelCase_      :							Any       =      coordinate_size
         UpperCAmelCase_      :							Tuple       =      shape_size
         UpperCAmelCase_      :							Optional[int]       =      has_relative_attention_bias
         UpperCAmelCase_      :							Union[str, Any]       =      rel_pos_bins
         UpperCAmelCase_      :							Dict       =      max_rel_pos
         UpperCAmelCase_      :							Union[str, Any]       =      has_spatial_attention_bias
         UpperCAmelCase_      :							Any       =      rel_ad_pos_bins
         UpperCAmelCase_      :							Tuple       =      max_rel_ad_pos
         UpperCAmelCase_      :							List[str]       =      text_embed
         UpperCAmelCase_      :							int       =      visual_embed
         UpperCAmelCase_      :							int       =      input_size
         UpperCAmelCase_      :							Dict       =      num_channels
         UpperCAmelCase_      :							int       =      patch_size
         UpperCAmelCase_      :							Dict       =      classifier_dropout


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				version.parse('''1.12'''       )





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         # The order of inputs is different for question answering and sequence classification
         if self.task in ["question-answering", "sequence-classification"]:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]  )
         else:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
                  ]  )





    @property
    def 							a__				(				self  )			->		float:
         return 1e-5





    @property
    def 							a__				(				self  )			->		int:
         return 12





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = 3       ,_SCREAMING_SNAKE_CASE = 40       ,_SCREAMING_SNAKE_CASE = 40       ,)			->		Mapping[str, Any]:
         setattr(processor.image_processor       ,'''apply_ocr'''       ,_SCREAMING_SNAKE_CASE  )

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCAmelCase_      :							List[str]       =      compute_effective_axis_dimension(
             _SCREAMING_SNAKE_CASE       ,fixed_dimension=OnnxConfig.default_fixed_batch       ,num_token_to_add=0  )
         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         UpperCAmelCase_      :							Optional[Any]       =      processor.tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      compute_effective_axis_dimension(
             _SCREAMING_SNAKE_CASE       ,fixed_dimension=OnnxConfig.default_fixed_sequence       ,num_token_to_add=_SCREAMING_SNAKE_CASE  )
         # Generate dummy inputs according to compute batch and sequence
         UpperCAmelCase_      :							Optional[Any]       =      [[''' '''.join([processor.tokenizer.unk_token]  ) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         UpperCAmelCase_      :							Tuple       =      [[[48, 84, 73, 128]]] * batch_size

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
         UpperCAmelCase_      :							Union[str, Any]       =      self._generate_dummy_images(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      dict(
             processor(
                 _SCREAMING_SNAKE_CASE       ,text=_SCREAMING_SNAKE_CASE       ,boxes=_SCREAMING_SNAKE_CASE       ,return_tensors=_SCREAMING_SNAKE_CASE       ,)  )

         return inputs