

"""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 		_A       :





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=4    ,						SCREAMING_SNAKE_CASE__=2    ,						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__=36    ,						SCREAMING_SNAKE_CASE__=2    ,						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__=6    ,						SCREAMING_SNAKE_CASE__=6    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=4    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=1000    ,						)   ->					List[str]:
         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		)   ->					Union[str, Any]:
         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(SCREAMING_SNAKE_CASE__		)

         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    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE__		)

         # text + image
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						pixel_values=SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			model(
             SCREAMING_SNAKE_CASE__    ,						bbox=SCREAMING_SNAKE_CASE__    ,						pixel_values=SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						token_type_ids=SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						bbox=SCREAMING_SNAKE_CASE__    ,						pixel_values=SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__		)

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

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Dict:
         lowerCamelCase__						=			self.num_labels
         lowerCamelCase__						=			TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			model(
             SCREAMING_SNAKE_CASE__    ,						bbox=SCREAMING_SNAKE_CASE__    ,						pixel_values=SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						token_type_ids=SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__    ,						)
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			self.num_labels
         lowerCamelCase__						=			TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			model(
             SCREAMING_SNAKE_CASE__    ,						bbox=SCREAMING_SNAKE_CASE__    ,						pixel_values=SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						token_type_ids=SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__    ,						)
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.text_seq_length, self.num_labels)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Dict:
         lowerCamelCase__						=			2
         lowerCamelCase__						=			TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			model(
             SCREAMING_SNAKE_CASE__    ,						bbox=SCREAMING_SNAKE_CASE__    ,						pixel_values=SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						token_type_ids=SCREAMING_SNAKE_CASE__    ,						start_positions=SCREAMING_SNAKE_CASE__    ,						end_positions=SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__    ,						)
         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		)   ->					int:
         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 		_A       (			__a				,       __a				,       unittest.TestCase					):
      __a								=	(
          (
              TFLayoutLMvaModel,
              TFLayoutLMvaForQuestionAnswering,
              TFLayoutLMvaForSequenceClassification,
              TFLayoutLMvaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      __a								=	(
          {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
          if is_tf_available()
          else {}
      )

      __a								=	False
      __a								=	False
      __a								=	False





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         return True





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False		)   ->					dict:
         lowerCamelCase__						=			copy.deepcopy(SCREAMING_SNAKE_CASE__		)

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

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

         return inputs_dict





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			TFLayoutLMvaModelTester(self		)
         lowerCamelCase__						=			ConfigTester(self    ,						config_class=SCREAMING_SNAKE_CASE__    ,						hidden_size=37		)





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





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
            lowerCamelCase__						=			model_class(SCREAMING_SNAKE_CASE__		)
            if getattr(SCREAMING_SNAKE_CASE__    ,						"hf_compute_loss"    ,						SCREAMING_SNAKE_CASE__		):
               # 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()    ,						SCREAMING_SNAKE_CASE__    ,						return_labels=SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			prepared_for_class[
                   sorted(prepared_for_class.keys() - inputs_dict.keys()    ,						reverse=SCREAMING_SNAKE_CASE__		)[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()    ,						SCREAMING_SNAKE_CASE__    ,						return_labels=SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			prepared_for_class.pop("input_ids"		)

               lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)[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()    ,						SCREAMING_SNAKE_CASE__    ,						return_labels=SCREAMING_SNAKE_CASE__		)
               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(SCREAMING_SNAKE_CASE__		)
                     lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)[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()    ,						SCREAMING_SNAKE_CASE__    ,						return_labels=SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)[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()    ,						SCREAMING_SNAKE_CASE__    ,						return_labels=SCREAMING_SNAKE_CASE__		)

               # 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(SCREAMING_SNAKE_CASE__		)
                  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(SCREAMING_SNAKE_CASE__		)

               # 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		)   ->					Union[str, Any]:
         (
             (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,              
         )						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  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(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         (
             (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,              
         )						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					str:
         (
             (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,              
         )						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         (
             (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,       (
             lowerCamelCase__       
         )					,              
         )						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      @slow
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowerCamelCase__						=			TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)






def 	UpperCAmelCase__   (				)      ->      Optional[Any]:




   """simple docstring"""
   lowerCamelCase__						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
   return image


@require_tf
class 		_A       (			unittest.TestCase					):





      @cached_property
      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE__		) if is_vision_available() else None





      @slow
      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		)

         lowerCamelCase__						=			self.default_image_processor
         lowerCamelCase__						=			prepare_img()
         lowerCamelCase__						=			image_processor(images=SCREAMING_SNAKE_CASE__    ,						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=SCREAMING_SNAKE_CASE__    ,						bbox=SCREAMING_SNAKE_CASE__    ,						pixel_values=SCREAMING_SNAKE_CASE__    ,						training=SCREAMING_SNAKE_CASE__		)

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

         lowerCamelCase__						=			tf.constant(
             [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]		)

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







"""simple docstring"""




from __future__ import annotations

from typing import Any



def 	UpperCAmelCase__   (				A__   )      ->      None:




   """simple docstring"""
   create_state_space_tree(A__	,    []	,    0   )



def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      None:




   """simple docstring"""

   if index == len(A__   ):
      print(A__   )
      return

   create_state_space_tree(A__	,    A__	,    index + 1   )
   current_subsequence.append(sequence[index]   )
   create_state_space_tree(A__	,    A__	,    index + 1   )
   current_subsequence.pop()


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			list[Any]	  =			[3, 1, 2, 4]
     generate_all_subsequences(seq)

     seq.clear()
     seq.extend(['''A''', '''B''', '''C'''])
     generate_all_subsequences(seq)




