


"""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							:					Dict							,     SCREAMING_SNAKE_CASE__							:					List[str]							,     SCREAMING_SNAKE_CASE__							:					Any=2							,     SCREAMING_SNAKE_CASE__							:					int=3							,     SCREAMING_SNAKE_CASE__							:					Dict=4							,     SCREAMING_SNAKE_CASE__							:					Tuple=2							,     SCREAMING_SNAKE_CASE__							:					str=7							,     SCREAMING_SNAKE_CASE__							:					Optional[int]=True							,     SCREAMING_SNAKE_CASE__							:					Any=True							,     SCREAMING_SNAKE_CASE__							:					Tuple=True							,     SCREAMING_SNAKE_CASE__							:					Optional[int]=True							,     SCREAMING_SNAKE_CASE__							:					str=99							,     SCREAMING_SNAKE_CASE__							:					Any=36							,     SCREAMING_SNAKE_CASE__							:					Tuple=2							,     SCREAMING_SNAKE_CASE__							:					List[str]=4							,     SCREAMING_SNAKE_CASE__							:					str=37							,     SCREAMING_SNAKE_CASE__							:					Any="gelu"							,     SCREAMING_SNAKE_CASE__							:					Dict=0.1							,     SCREAMING_SNAKE_CASE__							:					Dict=0.1							,     SCREAMING_SNAKE_CASE__							:					int=5_12							,     SCREAMING_SNAKE_CASE__							:					List[Any]=16							,     SCREAMING_SNAKE_CASE__							:					int=2							,     SCREAMING_SNAKE_CASE__							:					Any=0.02							,     SCREAMING_SNAKE_CASE__							:					List[Any]=6							,     SCREAMING_SNAKE_CASE__							:					Optional[Any]=6							,     SCREAMING_SNAKE_CASE__							:					List[str]=3							,     SCREAMING_SNAKE_CASE__							:					Any=4							,     SCREAMING_SNAKE_CASE__							:					List[Any]=None							,     SCREAMING_SNAKE_CASE__							:					Optional[Any]=10_00							,     ):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		batch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		is_training
          lowerCamelCase__           =		use_input_mask
          lowerCamelCase__           =		use_token_type_ids
          lowerCamelCase__           =		use_labels
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		num_hidden_layers
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		type_vocab_size
          lowerCamelCase__           =		type_sequence_label_size
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		coordinate_size
          lowerCamelCase__           =		shape_size
          lowerCamelCase__           =		num_labels
          lowerCamelCase__           =		num_choices
          lowerCamelCase__           =		scope
          lowerCamelCase__           =		range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          lowerCamelCase__           =		text_seq_length
          lowerCamelCase__           =		(image_size // patch_size) ** 2 + 1
          lowerCamelCase__           =		self.text_seq_length + self.image_seq_length
    def      _UpperCamelCase       ( self							:					Tuple				):
          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      _UpperCamelCase       ( self							:					Any							,     SCREAMING_SNAKE_CASE__							:					List[Any]							,     SCREAMING_SNAKE_CASE__							:					Dict							,     SCREAMING_SNAKE_CASE__							:					List[Any]							,     SCREAMING_SNAKE_CASE__							:					int							,     SCREAMING_SNAKE_CASE__							:					List[Any]							,     SCREAMING_SNAKE_CASE__							:					int				):
          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      _UpperCamelCase       ( self							:					List[Any]							,     SCREAMING_SNAKE_CASE__							:					str							,     SCREAMING_SNAKE_CASE__							:					List[Any]							,     SCREAMING_SNAKE_CASE__							:					Optional[Any]							,     SCREAMING_SNAKE_CASE__							:					Tuple							,     SCREAMING_SNAKE_CASE__							:					str							,     SCREAMING_SNAKE_CASE__							:					int							,     SCREAMING_SNAKE_CASE__							:					str				):
          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      _UpperCamelCase       ( self							:					Union[str, Any]							,     SCREAMING_SNAKE_CASE__							:					int							,     SCREAMING_SNAKE_CASE__							:					Optional[Any]							,     SCREAMING_SNAKE_CASE__							:					List[Any]							,     SCREAMING_SNAKE_CASE__							:					List[Any]							,     SCREAMING_SNAKE_CASE__							:					Optional[Any]							,     SCREAMING_SNAKE_CASE__							:					Dict							,     SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          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      _UpperCamelCase       ( self							:					Dict							,     SCREAMING_SNAKE_CASE__							:					Optional[int]							,     SCREAMING_SNAKE_CASE__							:					Tuple							,     SCREAMING_SNAKE_CASE__							:					int							,     SCREAMING_SNAKE_CASE__							:					Any							,     SCREAMING_SNAKE_CASE__							:					Any							,     SCREAMING_SNAKE_CASE__							:					List[Any]							,     SCREAMING_SNAKE_CASE__							:					List[str]				):
          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      _UpperCamelCase       ( self							:					Tuple				):
          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      (      SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				Optional[int]             =    (
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    a_    :				Union[str, Any]             =    (
        {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    a_    :				Optional[Any]             =    False
    a_    :				List[Any]             =    False
    a_    :				str             =    False
    def      _UpperCamelCase       ( self							:					Dict							,     SCREAMING_SNAKE_CASE__							:					str							,     SCREAMING_SNAKE_CASE__							:					List[Any]							,     SCREAMING_SNAKE_CASE__							:					Optional[int]							,     SCREAMING_SNAKE_CASE__							:					List[str]							,     SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          return True
    def      _UpperCamelCase       ( self							:					Any							,     SCREAMING_SNAKE_CASE__							:					Tuple							,     SCREAMING_SNAKE_CASE__							:					List[str]							,     SCREAMING_SNAKE_CASE__							:					Any=False				):
          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      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		TFLayoutLMvaModelTester(self				)
          lowerCamelCase__           =		ConfigTester(self							,     config_class=SCREAMING_SNAKE_CASE__							,     hidden_size=37				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          self.config_tester.run_common_tests()
    def      _UpperCamelCase       ( self							:					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__           =		-1_00
                                  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      _UpperCamelCase       ( self							:					Dict				):
          (
              (
              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      _UpperCamelCase       ( self							:					Any				):
          (
              (
              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      _UpperCamelCase       ( 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_sequence_classification(
              SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          (
              (
              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      _UpperCamelCase       ( self							:					Optional[int]				):
          (
              (
              lowerCamelCase__  
          )    ,  (
              lowerCamelCase__  
          )    ,  (
              lowerCamelCase__  
          )    ,  (
              lowerCamelCase__  
          )    ,  (
              lowerCamelCase__  
          )    ,  (
              lowerCamelCase__  
          )    ,  (
              lowerCamelCase__  
          )    ,  (
              lowerCamelCase__  
          )    ,    
          )           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_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      _UpperCamelCase       ( self							:					Optional[int]				):
          for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                lowerCamelCase__           =		TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE__				)





def   snake_case						(				)->		Dict:
      '''simple docstring'''


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






@require_tf
class   _a      (      unittest.TestCase ):
    @cached_property
    def      _UpperCamelCase       ( self							:					str				):
          return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE__				) if is_vision_available() else None


    @slow
    def      _UpperCamelCase       ( self							:					Dict				):
          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, 1_99, 7_68)
          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"""


# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def   snake_case						(				_a:     Optional[Any]							)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[-1] * len(_a							)

      def dfs(_a:     Any		,   _a:     Optional[int]							):
            lowerCamelCase__           =		True
            lowerCamelCase__           =		c
            for u in graph[v]:
                  if not visited[u]:
                        dfs(_a		,   1 - c							)

      for i in range(len(_a							)							):
            if not visited[i]:
                  dfs(_a		,   0							)

      for i in range(len(_a							)							):
            for j in graph[i]:
                  if color[i] == color[j]:
                        return False

      return True


# Adjacency list of graph
_snake_case          =							{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}






print(check_bipartite_dfs(graph))



