



"""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  lowercase__  :







       '''simple docstring'''


       def __init__(				self     :    Optional[Any]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    List[str]=2						,							_UpperCAmelCase     :    Union[str, Any]=3						,							_UpperCAmelCase     :    int=4						,							_UpperCAmelCase     :    Tuple=2						,							_UpperCAmelCase     :    Any=7						,							_UpperCAmelCase     :    Any=True						,							_UpperCAmelCase     :    Any=True						,							_UpperCAmelCase     :    Optional[int]=True						,							_UpperCAmelCase     :    int=True						,							_UpperCAmelCase     :    str=99						,							_UpperCAmelCase     :    Optional[Any]=36						,							_UpperCAmelCase     :    str=2						,							_UpperCAmelCase     :    Any=4						,							_UpperCAmelCase     :    Any=37						,							_UpperCAmelCase     :    List[Any]="gelu"						,							_UpperCAmelCase     :    Any=0.1						,							_UpperCAmelCase     :    Dict=0.1						,							_UpperCAmelCase     :    Union[str, Any]=512						,							_UpperCAmelCase     :    List[str]=16						,							_UpperCAmelCase     :    List[str]=2						,							_UpperCAmelCase     :    Optional[Any]=0.02						,							_UpperCAmelCase     :    Any=6						,							_UpperCAmelCase     :    Optional[int]=6						,							_UpperCAmelCase     :    List[Any]=3						,							_UpperCAmelCase     :    Tuple=4						,							_UpperCAmelCase     :    Optional[int]=None						,							_UpperCAmelCase     :    List[str]=1000						,							)					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				parent
             UpperCAmelCase_     					=				batch_size
             UpperCAmelCase_     					=				num_channels
             UpperCAmelCase_     					=				image_size
             UpperCAmelCase_     					=				patch_size
             UpperCAmelCase_     					=				is_training
             UpperCAmelCase_     					=				use_input_mask
             UpperCAmelCase_     					=				use_token_type_ids
             UpperCAmelCase_     					=				use_labels
             UpperCAmelCase_     					=				vocab_size
             UpperCAmelCase_     					=				hidden_size
             UpperCAmelCase_     					=				num_hidden_layers
             UpperCAmelCase_     					=				num_attention_heads
             UpperCAmelCase_     					=				intermediate_size
             UpperCAmelCase_     					=				hidden_act
             UpperCAmelCase_     					=				hidden_dropout_prob
             UpperCAmelCase_     					=				attention_probs_dropout_prob
             UpperCAmelCase_     					=				max_position_embeddings
             UpperCAmelCase_     					=				type_vocab_size
             UpperCAmelCase_     					=				type_sequence_label_size
             UpperCAmelCase_     					=				initializer_range
             UpperCAmelCase_     					=				coordinate_size
             UpperCAmelCase_     					=				shape_size
             UpperCAmelCase_     					=				num_labels
             UpperCAmelCase_     					=				num_choices
             UpperCAmelCase_     					=				scope
             UpperCAmelCase_     					=				range_bbox

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



       def 	lowercase__							(				self     :    Optional[Any]       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				ids_tensor([self.batch_size, self.text_seq_length]						,							self.vocab_size       )

             UpperCAmelCase_     					=				ids_tensor([self.batch_size, self.text_seq_length, 4]						,							self.range_bbox       )
             UpperCAmelCase_     					=				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]:
                               UpperCAmelCase_     					=				bbox[i, j, 3]
                               UpperCAmelCase_     					=				bbox[i, j, 1]
                               UpperCAmelCase_     					=				tmp_coordinate
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                               UpperCAmelCase_     					=				bbox[i, j, 2]
                               UpperCAmelCase_     					=				bbox[i, j, 0]
                               UpperCAmelCase_     					=				tmp_coordinate
             UpperCAmelCase_     					=				tf.constant(_UpperCAmelCase       )

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

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

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

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

             UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    str       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				TFLayoutLMvaModel(config=_UpperCAmelCase       )

             # text + image
             UpperCAmelCase_     					=				model(_UpperCAmelCase						,							pixel_values=_UpperCAmelCase						,							training=_UpperCAmelCase       )
             UpperCAmelCase_     					=				model(
                 _UpperCAmelCase						,							bbox=_UpperCAmelCase						,							pixel_values=_UpperCAmelCase						,							attention_mask=_UpperCAmelCase						,							token_type_ids=_UpperCAmelCase						,							training=_UpperCAmelCase						,							)
             UpperCAmelCase_     					=				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
             UpperCAmelCase_     					=				model(_UpperCAmelCase						,							training=_UpperCAmelCase       )
             self.parent.assertEqual(
                 result.last_hidden_state.shape						,							(self.batch_size, self.text_seq_length, self.hidden_size)       )

             # image only
             UpperCAmelCase_     					=				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 	lowercase__							(				self     :    str						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Any       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.num_labels
             UpperCAmelCase_     					=				TFLayoutLMvaForSequenceClassification(config=_UpperCAmelCase       )
             UpperCAmelCase_     					=				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 	lowercase__							(				self     :    int						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Any       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.num_labels
             UpperCAmelCase_     					=				TFLayoutLMvaForTokenClassification(config=_UpperCAmelCase       )
             UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    Union[str, Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				2
             UpperCAmelCase_     					=				TFLayoutLMvaForQuestionAnswering(config=_UpperCAmelCase       )
             UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Optional[Any]       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.prepare_config_and_inputs()
             ((UpperCAmelCase_)					,       (UpperCAmelCase_)					,       (UpperCAmelCase_)					,       (UpperCAmelCase_)					,       (UpperCAmelCase_)					,       (UpperCAmelCase_)					,       (UpperCAmelCase_)					,       (UpperCAmelCase_))     					=				config_and_inputs
             UpperCAmelCase_     					=				{
                 "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  lowercase__  (       SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	(
           (
               TFLayoutLMvaModel,
               TFLayoutLMvaForQuestionAnswering,
               TFLayoutLMvaForSequenceClassification,
               TFLayoutLMvaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       UpperCamelCase		=	(
           {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
           if is_tf_available()
           else {}
       )

       UpperCamelCase		=	False
       UpperCamelCase		=	False
       UpperCamelCase		=	False



       def 	lowercase__							(				self     :    List[Any]						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    int       )					->		List[Any]:


             '''simple docstring'''




             return True



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    List[Any]=False       )					->		dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				copy.deepcopy(_UpperCAmelCase       )

             if model_class in get_values(_UpperCAmelCase       ):
                   UpperCAmelCase_     					=				{
                       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       ):
                         UpperCAmelCase_     					=				tf.ones(self.model_tester.batch_size						,							dtype=tf.intaa       )
                   elif model_class in get_values(_UpperCAmelCase       ):
                         UpperCAmelCase_     					=				tf.zeros(self.model_tester.batch_size						,							dtype=tf.intaa       )
                         UpperCAmelCase_     					=				tf.zeros(self.model_tester.batch_size						,							dtype=tf.intaa       )
                   elif model_class in get_values(_UpperCAmelCase       ):
                         UpperCAmelCase_     					=				tf.zeros(self.model_tester.batch_size						,							dtype=tf.intaa       )
                   elif model_class in get_values(_UpperCAmelCase       ):
                         UpperCAmelCase_     					=				tf.zeros(
                             (self.model_tester.batch_size, self.model_tester.text_seq_length)						,							dtype=tf.intaa       )

             return inputs_dict



       def 	lowercase__							(				self     :    Union[str, Any]       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				TFLayoutLMvaModelTester(self       )
             UpperCAmelCase_     					=				ConfigTester(self						,							config_class=_UpperCAmelCase						,							hidden_size=37       )



       def 	lowercase__							(				self     :    Tuple       )					->		List[Any]:


             '''simple docstring'''




             self.config_tester.run_common_tests()



       def 	lowercase__							(				self     :    Tuple       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_     					=				self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                   UpperCAmelCase_     					=				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
                         UpperCAmelCase_     					=				self._prepare_for_class(inputs_dict.copy()						,							_UpperCAmelCase						,							return_labels=_UpperCAmelCase       )
                         UpperCAmelCase_     					=				prepared_for_class[
                             sorted(prepared_for_class.keys() - inputs_dict.keys()						,							reverse=_UpperCAmelCase       )[0]
                         ]
                         UpperCAmelCase_     					=				added_label.shape.as_list()[:1]

                         # Test that model correctly compute the loss with kwargs
                         UpperCAmelCase_     					=				self._prepare_for_class(inputs_dict.copy()						,							_UpperCAmelCase						,							return_labels=_UpperCAmelCase       )
                         UpperCAmelCase_     					=				prepared_for_class.pop("input_ids"       )

                         UpperCAmelCase_     					=				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
                         UpperCAmelCase_     					=				self._prepare_for_class(inputs_dict.copy()						,							_UpperCAmelCase						,							return_labels=_UpperCAmelCase       )
                         UpperCAmelCase_     					=				prepared_for_class.pop("input_ids"       )
                         if "labels" in prepared_for_class:
                               UpperCAmelCase_     					=				prepared_for_class["labels"].numpy()
                               if len(labels.shape       ) > 1 and labels.shape[1] != 1:
                                     UpperCAmelCase_     					=				-100
                                     UpperCAmelCase_     					=				tf.convert_to_tensor(_UpperCAmelCase       )
                                     UpperCAmelCase_     					=				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
                         UpperCAmelCase_     					=				self._prepare_for_class(inputs_dict.copy()						,							_UpperCAmelCase						,							return_labels=_UpperCAmelCase       )
                         UpperCAmelCase_     					=				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
                         UpperCAmelCase_     					=				self._prepare_for_class(inputs_dict.copy()						,							_UpperCAmelCase						,							return_labels=_UpperCAmelCase       )

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

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

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

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

                         UpperCAmelCase_     					=				tuple(_UpperCAmelCase       )

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

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



       def 	lowercase__							(				self     :    int       )					->		str:


             '''simple docstring'''




             (
                 (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       
             )     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Optional[Any]       )					->		Tuple:


             '''simple docstring'''




             (
                 (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       
             )     					=				self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                   UpperCAmelCase_     					=				type
                   self.model_tester.create_and_check_model(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       )



       def 	lowercase__							(				self     :    List[Any]       )					->		List[Any]:


             '''simple docstring'''




             (
                 (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       
             )     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(
                 _UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Any       )					->		List[str]:


             '''simple docstring'''




             (
                 (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       
             )     					=				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(
                 _UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Union[str, Any]       )					->		str:


             '''simple docstring'''




             (
                 (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       (
                 UpperCAmelCase_
             )					,       
             )     					=				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 	lowercase__							(				self     :    Optional[int]       )					->		Dict:


             '''simple docstring'''




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



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


@require_tf
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       @cached_property
       def 	lowercase__							(				self     :    Union[str, Any]       )					->		Optional[Any]:


             '''simple docstring'''




             return LayoutLMvaImageProcessor(apply_ocr=_UpperCAmelCase       ) if is_vision_available() else None







       @slow
       def 	lowercase__							(				self     :    Union[str, Any]       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"       )

             UpperCAmelCase_     					=				self.default_image_processor
             UpperCAmelCase_     					=				prepare_img()
             UpperCAmelCase_     					=				image_processor(images=_UpperCAmelCase						,							return_tensors="tf"       ).pixel_values

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

             # forward pass
             UpperCAmelCase_     					=				model(input_ids=_UpperCAmelCase						,							bbox=_UpperCAmelCase						,							pixel_values=_UpperCAmelCase						,							training=_UpperCAmelCase       )

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

             UpperCAmelCase_     					=				tf.constant(
                 [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]       )

             self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]						,							_UpperCAmelCase						,							atol=1e-4       )       )












"""simple docstring"""
import operator as op





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    []
				_SCREAMING_SNAKE_CASE   :		Dict       		=    lambda lowerCamelCase,						lowerCamelCase       : int(x / y       )  # noqa: E731 integer division operation
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    {
				    '^': op.pow,
				    '*': op.mul,
				    '/': div,
				    '+': op.add,
				    '-': op.sub,
				}  # operators & their respective operation

				# print table header
				print('Symbol'.center(8       ),						'Action'.center(12       ),						'Stack',						sep=' | '       )
				print('-' * (30 + len(lowerCamelCase       ))       )

				for x in post_fix:
								if x.isdigit():  # if x in digit
												stack.append(lowerCamelCase       )  # append x to stack
												# output in tabular format
												print(x.rjust(8       ),						('push(' + x + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )
								else:
												_SCREAMING_SNAKE_CASE   :		int       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												_SCREAMING_SNAKE_CASE   :		Tuple       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + a + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												stack.append(
												    str(opr[x](int(lowerCamelCase       ),						int(lowerCamelCase       )       )       )       )  # evaluate the 2 values popped from stack & push result to stack
												# output in tabular format
												print(
												    x.rjust(8       ),						('push(' + a + x + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | ',						)

				return int(stack[0]       )


if __name__ == "__main__":
			lowerCAmelCase__    						=			input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
			print('\n\tResult = ', solve(Postfix))

