



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  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=2	,				__UpperCAmelCase=3	,				__UpperCAmelCase=4	,				__UpperCAmelCase=2	,				__UpperCAmelCase=7	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=99	,				__UpperCAmelCase=36	,				__UpperCAmelCase=2	,				__UpperCAmelCase=4	,				__UpperCAmelCase=37	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=16	,				__UpperCAmelCase=2	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=6	,				__UpperCAmelCase=6	,				__UpperCAmelCase=3	,				__UpperCAmelCase=4	,				__UpperCAmelCase=None	,				__UpperCAmelCase=1000	,				):



    '''simple docstring'''
    __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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __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(__UpperCAmelCase				)

    __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        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    TFLayoutLMvaModel(config=__UpperCAmelCase				)

    # text + image
    __lowerCamelCase													=    model(__UpperCAmelCase	,				pixel_values=__UpperCAmelCase	,				training=__UpperCAmelCase				)
    __lowerCamelCase													=    model(
        __UpperCAmelCase	,				bbox=__UpperCAmelCase	,				pixel_values=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase	,				training=__UpperCAmelCase	,				)
    __lowerCamelCase													=    model(__UpperCAmelCase	,				bbox=__UpperCAmelCase	,				pixel_values=__UpperCAmelCase	,				training=__UpperCAmelCase				)

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

    # text only
    __lowerCamelCase													=    model(__UpperCAmelCase	,				training=__UpperCAmelCase				)
    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=__UpperCAmelCase				)
    self.parent.assertEqual(
        result.last_hidden_state.shape	,				(self.batch_size, self.image_seq_length, self.hidden_size)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.num_labels
    __lowerCamelCase													=    TFLayoutLMvaForSequenceClassification(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(
        __UpperCAmelCase	,				bbox=__UpperCAmelCase	,				pixel_values=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase	,				labels=__UpperCAmelCase	,				training=__UpperCAmelCase	,				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.num_labels
    __lowerCamelCase													=    TFLayoutLMvaForTokenClassification(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(
        __UpperCAmelCase	,				bbox=__UpperCAmelCase	,				pixel_values=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase	,				labels=__UpperCAmelCase	,				training=__UpperCAmelCase	,				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.text_seq_length, self.num_labels)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    2
    __lowerCamelCase													=    TFLayoutLMvaForQuestionAnswering(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(
        __UpperCAmelCase	,				bbox=__UpperCAmelCase	,				pixel_values=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase	,				start_positions=__UpperCAmelCase	,				end_positions=__UpperCAmelCase	,				training=__UpperCAmelCase	,				)
    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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __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  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (
     (
         TFLayoutLMvaModel,
         TFLayoutLMvaForQuestionAnswering,
         TFLayoutLMvaForSequenceClassification,
         TFLayoutLMvaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 lowerCAmelCase__       					=  (
     {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
     if is_tf_available()
     else {}
 )

 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    return True




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=False				):



    '''simple docstring'''
    __lowerCamelCase													=    copy.deepcopy(__UpperCAmelCase				)

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

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

    return inputs_dict




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFLayoutLMvaModelTester(self				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase	,				hidden_size=37				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
       __lowerCamelCase													=    model_class(__UpperCAmelCase				)
       if getattr(__UpperCAmelCase	,				'''hf_compute_loss'''	,				__UpperCAmelCase				):
          # 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()	,				__UpperCAmelCase	,				return_labels=__UpperCAmelCase				)
          __lowerCamelCase													=    prepared_for_class[
              sorted(prepared_for_class.keys() - inputs_dict.keys()	,				reverse=__UpperCAmelCase				)[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()	,				__UpperCAmelCase	,				return_labels=__UpperCAmelCase				)
          __lowerCamelCase													=    prepared_for_class.pop('''input_ids'''				)

          __lowerCamelCase													=    model(__UpperCAmelCase	,				**__UpperCAmelCase				)[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()	,				__UpperCAmelCase	,				return_labels=__UpperCAmelCase				)
          __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(__UpperCAmelCase				)
                __lowerCamelCase													=    model(__UpperCAmelCase	,				**__UpperCAmelCase				)[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()	,				__UpperCAmelCase	,				return_labels=__UpperCAmelCase				)
          __lowerCamelCase													=    model(__UpperCAmelCase				)[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()	,				__UpperCAmelCase	,				return_labels=__UpperCAmelCase				)

          # 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(__UpperCAmelCase				)
             __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(__UpperCAmelCase				)

          # 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        lowerCamelCase      (						self				):



    '''simple docstring'''
    (
        (
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,
    )													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    (
        (
        __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(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    (
        (
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,
    )													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(
        __UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    (
        (
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,
    )													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(
        __UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    (
        (
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,
    )													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(
        __UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCamelCase													=    TFLayoutLMvaModel.from_pretrained(__UpperCAmelCase				)
       self.assertIsNotNone(__UpperCAmelCase				)





def 		a__			(		):
   __lowerCamelCase													=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
   return image





@require_tf
class  __lowerCAmelCase     (	unittest.TestCase   ):




 @cached_property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase				) if is_vision_available() else None




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''				)

    __lowerCamelCase													=    self.default_image_processor
    __lowerCamelCase													=    prepare_img()
    __lowerCamelCase													=    image_processor(images=__UpperCAmelCase	,				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=__UpperCAmelCase	,				bbox=__UpperCAmelCase	,				pixel_values=__UpperCAmelCase	,				training=__UpperCAmelCase				)

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

    __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]	,				__UpperCAmelCase	,				atol=1E-4				)				)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_			=					logging.get_logger(__name__)

a_			=					{
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """visual_bert"""




 def __init__(						self	,				__UpperCAmelCase=30522	,				__UpperCAmelCase=768	,				__UpperCAmelCase=512	,				__UpperCAmelCase=12	,				__UpperCAmelCase=12	,				__UpperCAmelCase=3072	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=2	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=1E-1_2	,				__UpperCAmelCase=False	,				__UpperCAmelCase=True	,				__UpperCAmelCase=1	,				__UpperCAmelCase=0	,				__UpperCAmelCase=2	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    super().__init__(pad_token_id=__UpperCAmelCase	,				bos_token_id=__UpperCAmelCase	,				eos_token_id=__UpperCAmelCase	,				**__UpperCAmelCase				)

    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    visual_embedding_dim
    __lowerCamelCase													=    num_hidden_layers
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    type_vocab_size
    __lowerCamelCase													=    layer_norm_eps
    __lowerCamelCase													=    bypass_transformer
    __lowerCamelCase													=    special_visual_initialize






