


'''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       :


     """simple docstring"""






     def __init__(       self  :	Any							,				snake_case  :	Tuple							,				snake_case  :	Dict=2							,				snake_case  :	Optional[Any]=3							,				snake_case  :	Tuple=4							,				snake_case  :	Any=2							,				snake_case  :	List[str]=7							,				snake_case  :	int=True							,				snake_case  :	Optional[int]=True							,				snake_case  :	str=True							,				snake_case  :	List[str]=True							,				snake_case  :	Union[str, Any]=99							,				snake_case  :	Union[str, Any]=36							,				snake_case  :	Tuple=2							,				snake_case  :	Union[str, Any]=4							,				snake_case  :	Union[str, Any]=37							,				snake_case  :	Tuple="gelu"							,				snake_case  :	int=0.1							,				snake_case  :	Dict=0.1							,				snake_case  :	int=512							,				snake_case  :	Tuple=16							,				snake_case  :	Any=2							,				snake_case  :	Any=0.02							,				snake_case  :	str=6							,				snake_case  :	List[Any]=6							,				snake_case  :	Tuple=3							,				snake_case  :	List[str]=4							,				snake_case  :	Tuple=None							,				snake_case  :	Any=1000							,				)		->							Any:
          __UpperCAmelCase			:				List[Any]													=    parent
          __UpperCAmelCase			:				str													=    batch_size
          __UpperCAmelCase			:				str													=    num_channels
          __UpperCAmelCase			:				Union[str, Any]													=    image_size
          __UpperCAmelCase			:				Union[str, Any]													=    patch_size
          __UpperCAmelCase			:				Union[str, Any]													=    is_training
          __UpperCAmelCase			:				int													=    use_input_mask
          __UpperCAmelCase			:				Union[str, Any]													=    use_token_type_ids
          __UpperCAmelCase			:				Union[str, Any]													=    use_labels
          __UpperCAmelCase			:				Dict													=    vocab_size
          __UpperCAmelCase			:				List[Any]													=    hidden_size
          __UpperCAmelCase			:				str													=    num_hidden_layers
          __UpperCAmelCase			:				int													=    num_attention_heads
          __UpperCAmelCase			:				List[Any]													=    intermediate_size
          __UpperCAmelCase			:				Optional[int]													=    hidden_act
          __UpperCAmelCase			:				List[Any]													=    hidden_dropout_prob
          __UpperCAmelCase			:				int													=    attention_probs_dropout_prob
          __UpperCAmelCase			:				str													=    max_position_embeddings
          __UpperCAmelCase			:				Any													=    type_vocab_size
          __UpperCAmelCase			:				List[str]													=    type_sequence_label_size
          __UpperCAmelCase			:				Tuple													=    initializer_range
          __UpperCAmelCase			:				Union[str, Any]													=    coordinate_size
          __UpperCAmelCase			:				Any													=    shape_size
          __UpperCAmelCase			:				List[Any]													=    num_labels
          __UpperCAmelCase			:				Optional[Any]													=    num_choices
          __UpperCAmelCase			:				List[str]													=    scope
          __UpperCAmelCase			:				int													=    range_bbox

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

     def 				lowerCamelCase__				(       self  :	Any    )		->							Dict:
          __UpperCAmelCase			:				Optional[Any]													=    ids_tensor([self.batch_size, self.text_seq_length]							,				self.vocab_size    )

          __UpperCAmelCase			:				Dict													=    ids_tensor([self.batch_size, self.text_seq_length, 4]							,				self.range_bbox    )
          __UpperCAmelCase			:				List[Any]													=    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			:				int													=    bbox[i, j, 3]
                         __UpperCAmelCase			:				Tuple													=    bbox[i, j, 1]
                         __UpperCAmelCase			:				int													=    tmp_coordinate
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         __UpperCAmelCase			:				Any													=    bbox[i, j, 2]
                         __UpperCAmelCase			:				str													=    bbox[i, j, 0]
                         __UpperCAmelCase			:				Union[str, Any]													=    tmp_coordinate
          __UpperCAmelCase			:				List[Any]													=    tf.constant(lowerCAmelCase_    )

          __UpperCAmelCase			:				Optional[int]													=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

          __UpperCAmelCase			:				Dict													=    None
          if self.use_input_mask:
               __UpperCAmelCase			:				List[str]													=    random_attention_mask([self.batch_size, self.text_seq_length]    )

          __UpperCAmelCase			:				Dict													=    None
          if self.use_token_type_ids:
               __UpperCAmelCase			:				Union[str, Any]													=    ids_tensor([self.batch_size, self.text_seq_length]							,				self.type_vocab_size    )

          __UpperCAmelCase			:				Union[str, Any]													=    None
          __UpperCAmelCase			:				List[Any]													=    None
          if self.use_labels:
               __UpperCAmelCase			:				Union[str, Any]													=    ids_tensor([self.batch_size]							,				self.type_sequence_label_size    )
               __UpperCAmelCase			:				Optional[Any]													=    ids_tensor([self.batch_size, self.text_seq_length]							,				self.num_labels    )

          __UpperCAmelCase			:				Tuple													=    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  :	int							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[str]							,				snake_case  :	List[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	Dict							,				snake_case  :	List[str]    )		->							Any:
          __UpperCAmelCase			:				Tuple													=    TFLayoutLMvaModel(config=lowerCAmelCase_    )

          # text + image
          __UpperCAmelCase			:				Union[str, Any]													=    model(lowerCAmelCase_							,				pixel_values=lowerCAmelCase_							,				training=lowerCAmelCase_    )
          __UpperCAmelCase			:				Union[str, Any]													=    model(
              lowerCAmelCase_							,				bbox=lowerCAmelCase_							,				pixel_values=lowerCAmelCase_							,				attention_mask=lowerCAmelCase_							,				token_type_ids=lowerCAmelCase_							,				training=lowerCAmelCase_							,				)
          __UpperCAmelCase			:				List[str]													=    model(lowerCAmelCase_							,				bbox=lowerCAmelCase_							,				pixel_values=lowerCAmelCase_							,				training=lowerCAmelCase_    )

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

          # text only
          __UpperCAmelCase			:				Optional[Any]													=    model(lowerCAmelCase_							,				training=lowerCAmelCase_    )
          self.parent.assertEqual(
              result.last_hidden_state.shape							,				(self.batch_size, self.text_seq_length, self.hidden_size)    )

          # image only
          __UpperCAmelCase			:				str													=    model({'''pixel_values''': pixel_values}							,				training=lowerCAmelCase_    )
          self.parent.assertEqual(
              result.last_hidden_state.shape							,				(self.batch_size, self.image_seq_length, self.hidden_size)    )

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Dict							,				snake_case  :	int							,				snake_case  :	Any							,				snake_case  :	Any							,				snake_case  :	int							,				snake_case  :	List[str]							,				snake_case  :	int    )		->							List[str]:
          __UpperCAmelCase			:				Optional[Any]													=    self.num_labels
          __UpperCAmelCase			:				Any													=    TFLayoutLMvaForSequenceClassification(config=lowerCAmelCase_    )
          __UpperCAmelCase			:				Optional[Any]													=    model(
              lowerCAmelCase_							,				bbox=lowerCAmelCase_							,				pixel_values=lowerCAmelCase_							,				attention_mask=lowerCAmelCase_							,				token_type_ids=lowerCAmelCase_							,				labels=lowerCAmelCase_							,				training=lowerCAmelCase_							,				)
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)    )

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	Tuple							,				snake_case  :	Any							,				snake_case  :	int							,				snake_case  :	Union[str, Any]							,				snake_case  :	Optional[int]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Dict    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Optional[Any]													=    self.num_labels
          __UpperCAmelCase			:				List[str]													=    TFLayoutLMvaForTokenClassification(config=lowerCAmelCase_    )
          __UpperCAmelCase			:				Union[str, Any]													=    model(
              lowerCAmelCase_							,				bbox=lowerCAmelCase_							,				pixel_values=lowerCAmelCase_							,				attention_mask=lowerCAmelCase_							,				token_type_ids=lowerCAmelCase_							,				labels=lowerCAmelCase_							,				training=lowerCAmelCase_							,				)
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.text_seq_length, self.num_labels)    )

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	int							,				snake_case  :	Optional[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Any							,				snake_case  :	List[Any]							,				snake_case  :	Dict							,				snake_case  :	Tuple    )		->							int:
          __UpperCAmelCase			:				List[str]													=    2
          __UpperCAmelCase			:				int													=    TFLayoutLMvaForQuestionAnswering(config=lowerCAmelCase_    )
          __UpperCAmelCase			:				int													=    model(
              lowerCAmelCase_							,				bbox=lowerCAmelCase_							,				pixel_values=lowerCAmelCase_							,				attention_mask=lowerCAmelCase_							,				token_type_ids=lowerCAmelCase_							,				start_positions=lowerCAmelCase_							,				end_positions=lowerCAmelCase_							,				training=lowerCAmelCase_							,				)
          self.parent.assertEqual(result.start_logits.shape							,				(self.batch_size, self.seq_length)    )
          self.parent.assertEqual(result.end_logits.shape							,				(self.batch_size, self.seq_length)    )






     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Optional[Any]:
          __UpperCAmelCase			:				Tuple													=    self.prepare_config_and_inputs()
          ((__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase))			:				str													=    config_and_inputs
          __UpperCAmelCase			:				Dict													=    {
              '''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       (	__lowerCAmelCase			,						__lowerCAmelCase			,						unittest.TestCase ):


     """simple docstring"""






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

     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     False
     SCREAMING_SNAKE_CASE :				List[Any]							     =     False
     SCREAMING_SNAKE_CASE :				int							     =     False

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Optional[int]							,				snake_case  :	Dict							,				snake_case  :	Optional[Any]							,				snake_case  :	Dict							,				snake_case  :	Dict    )		->							Any:
          return True

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	List[str]							,				snake_case  :	int							,				snake_case  :	Union[str, Any]=False    )		->							dict:
          __UpperCAmelCase			:				Dict													=    copy.deepcopy(lowerCAmelCase_    )

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

          if return_labels:
               if model_class in get_values(lowerCAmelCase_    ):
                    __UpperCAmelCase			:				int													=    tf.ones(self.model_tester.batch_size							,				dtype=tf.intaa    )
               elif model_class in get_values(lowerCAmelCase_    ):
                    __UpperCAmelCase			:				Optional[Any]													=    tf.zeros(self.model_tester.batch_size							,				dtype=tf.intaa    )
                    __UpperCAmelCase			:				List[Any]													=    tf.zeros(self.model_tester.batch_size							,				dtype=tf.intaa    )
               elif model_class in get_values(lowerCAmelCase_    ):
                    __UpperCAmelCase			:				int													=    tf.zeros(self.model_tester.batch_size							,				dtype=tf.intaa    )
               elif model_class in get_values(lowerCAmelCase_    ):
                    __UpperCAmelCase			:				List[Any]													=    tf.zeros(
                        (self.model_tester.batch_size, self.model_tester.text_seq_length)							,				dtype=tf.intaa    )

          return inputs_dict

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							List[str]:
          __UpperCAmelCase			:				int													=    TFLayoutLMvaModelTester(self    )
          __UpperCAmelCase			:				int													=    ConfigTester(self							,				config_class=lowerCAmelCase_							,				hidden_size=37    )

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

     def 				lowerCamelCase__				(       self  :	Any    )		->							Tuple:
          __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
               __UpperCAmelCase			:				str													=    model_class(lowerCAmelCase_    )
               if getattr(lowerCAmelCase_							,				'''hf_compute_loss'''							,				lowerCAmelCase_    ):
                    # The number of elements in the loss should be the same as the number of elements in the label
                    __UpperCAmelCase			:				Optional[int]													=    self._prepare_for_class(inputs_dict.copy()							,				lowerCAmelCase_							,				return_labels=lowerCAmelCase_    )
                    __UpperCAmelCase			:				Union[str, Any]													=    prepared_for_class[
                        sorted(prepared_for_class.keys() - inputs_dict.keys()							,				reverse=lowerCAmelCase_    )[0]
                    ]
                    __UpperCAmelCase			:				int													=    added_label.shape.as_list()[:1]

                    # Test that model correctly compute the loss with kwargs
                    __UpperCAmelCase			:				Optional[Any]													=    self._prepare_for_class(inputs_dict.copy()							,				lowerCAmelCase_							,				return_labels=lowerCAmelCase_    )
                    __UpperCAmelCase			:				Optional[int]													=    prepared_for_class.pop('''input_ids'''    )

                    __UpperCAmelCase			:				str													=    model(lowerCAmelCase_							,				**lowerCAmelCase_    )[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			:				str													=    self._prepare_for_class(inputs_dict.copy()							,				lowerCAmelCase_							,				return_labels=lowerCAmelCase_    )
                    __UpperCAmelCase			:				Any													=    prepared_for_class.pop('''input_ids'''    )
                    if "labels" in prepared_for_class:
                         __UpperCAmelCase			:				Tuple													=    prepared_for_class['''labels'''].numpy()
                         if len(labels.shape    ) > 1 and labels.shape[1] != 1:
                              __UpperCAmelCase			:				Dict													=    -100
                              __UpperCAmelCase			:				Any													=    tf.convert_to_tensor(lowerCAmelCase_    )
                              __UpperCAmelCase			:				List[str]													=    model(lowerCAmelCase_							,				**lowerCAmelCase_    )[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			:				int													=    self._prepare_for_class(inputs_dict.copy()							,				lowerCAmelCase_							,				return_labels=lowerCAmelCase_    )
                    __UpperCAmelCase			:				Tuple													=    model(lowerCAmelCase_    )[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			:				Optional[int]													=    self._prepare_for_class(inputs_dict.copy()							,				lowerCAmelCase_							,				return_labels=lowerCAmelCase_    )

                    # Get keys that were added with the _prepare_for_class function
                    __UpperCAmelCase			:				Optional[Any]													=    prepared_for_class.keys() - inputs_dict.keys()
                    __UpperCAmelCase			:				Dict													=    inspect.signature(model.call    ).parameters
                    __UpperCAmelCase			:				Optional[Any]													=    list(signature.keys()    )

                    # Create a dictionary holding the location of the tensors in the tuple
                    __UpperCAmelCase			:				int													=    {0: '''input_ids'''}
                    for label_key in label_keys:
                         __UpperCAmelCase			:				Any													=    signature_names.index(lowerCAmelCase_    )
                         __UpperCAmelCase			:				Tuple													=    label_key
                    __UpperCAmelCase			:				Union[str, Any]													=    sorted(tuple_index_mapping.items()    )
                    # Initialize a list with their default values, update the values and convert to a tuple
                    __UpperCAmelCase			:				Dict													=    []

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

                    for index, value in sorted_tuple_index_mapping:
                         __UpperCAmelCase			:				str													=    prepared_for_class[value]

                    __UpperCAmelCase			:				Optional[Any]													=    tuple(lowerCAmelCase_    )

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

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

     def 				lowerCamelCase__				(       self  :	str    )		->							Dict:
          (
              (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     						
          )			:				str													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Optional[Any]:
          (
              (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     						
          )			:				Dict													=    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               __UpperCAmelCase			:				Tuple													=    type
               self.model_tester.create_and_check_model(lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_    )

     def 				lowerCamelCase__				(       self  :	str    )		->							Any:
          (
              (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     						
          )			:				List[str]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(
              lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Dict:
          (
              (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     						
          )			:				Dict													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(
              lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_    )

     def 				lowerCamelCase__				(       self  :	str    )		->							str:
          (
              (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     (
              __UpperCAmelCase						
          )							,     						
          )			:				Any													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(
              lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_    )






     @slow
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							List[Any]:
          for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase			:				List[str]													=    TFLayoutLMvaModel.from_pretrained(lowerCAmelCase_    )
               self.assertIsNotNone(lowerCAmelCase_    )




def 			_a			(		)     ->   str:





     '''simple docstring'''






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



@require_tf
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     @cached_property
     def 				lowerCamelCase__				(       self  :	Dict    )		->							List[Any]:
          return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase_    ) if is_vision_available() else None






     @slow
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Tuple:
          __UpperCAmelCase			:				List[str]													=    TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''    )

          __UpperCAmelCase			:				Dict													=    self.default_image_processor
          __UpperCAmelCase			:				List[Any]													=    prepare_img()
          __UpperCAmelCase			:				Union[str, Any]													=    image_processor(images=lowerCAmelCase_							,				return_tensors='''tf'''    ).pixel_values

          __UpperCAmelCase			:				List[str]													=    tf.constant([[1, 2]]    )
          __UpperCAmelCase			:				Tuple													=    tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]    )							,				axis=0    )

          # forward pass
          __UpperCAmelCase			:				Optional[Any]													=    model(input_ids=lowerCAmelCase_							,				bbox=lowerCAmelCase_							,				pixel_values=lowerCAmelCase_							,				training=lowerCAmelCase_    )

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

          __UpperCAmelCase			:				Tuple													=    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]							,				lowerCAmelCase_							,				atol=1E-4    )    )



'''simple docstring'''



def 			_a			(		_lowercase      :			List[str]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    len(_lowercase						)
     while cur > 1:
          # Find the maximum number in arr
          __UpperCAmelCase			:				Union[str, Any]													=    arr.index(max(arr[0:cur]						)						)
          # Reverse from 0 to mi
          __UpperCAmelCase			:				Any													=    arr[mi::-1] + arr[mi + 1 : len(_lowercase						)]
          # Reverse whole list
          __UpperCAmelCase			:				List[str]													=    arr[cur - 1 :: -1] + arr[cur : len(_lowercase						)]
          cur -= 1
     return arr


if __name__ == "__main__":
      __UpperCAmelCase    :Any				       =     input("Enter numbers separated by a comma:\n").strip()
      __UpperCAmelCase    :Optional[int]				       =     [int(item) for item in user_input.split(",")]
      print(pancake_sort(unsorted))