





'''simple docstring'''






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  UpperCAmelCase     (			snake_case_							,		unittest.TestCase   ):
      _lowercase:			Union[str, Any]						     =   BarthezTokenizer
      _lowercase:			List[str]						     =   BarthezTokenizerFast
      _lowercase:			Tuple						     =   True
      _lowercase:			str						     =   True




      def 				lowercase__    (			self			: Optional[int]  )				->      Any:
            super().setUp()

            _lowerCAmelCase							     =							BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""  )
            tokenizer.save_pretrained(self.tmpdirname  )
            tokenizer.save_pretrained(self.tmpdirname			,			legacy_format=__snake_case  )
            _lowerCAmelCase							     =							tokenizer




      def 				lowercase__    (			self			: Optional[int]  )				->      int:
            _lowerCAmelCase							     =							"""<pad>"""
            _lowerCAmelCase							     =							1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case  )			,			__snake_case  )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case  )			,			__snake_case  )




      def 				lowercase__    (			self			: List[str]  )				->      Optional[int]:
            _lowerCAmelCase							     =							list(self.get_tokenizer().get_vocab().keys()  )

            self.assertEqual(vocab_keys[0]			,			"""<s>"""  )
            self.assertEqual(vocab_keys[1]			,			"""<pad>"""  )
            self.assertEqual(vocab_keys[-1]			,			"""<mask>"""  )
            self.assertEqual(len(__snake_case  )			,			10_11_22  )




      def 				lowercase__    (			self			: Any  )				->      Optional[Any]:
            self.assertEqual(self.get_tokenizer().vocab_size			,			10_11_22  )




      @require_torch
      def 				lowercase__    (			self			: int  )				->      Union[str, Any]:
            _lowerCAmelCase							     =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            _lowerCAmelCase							     =							[0, 57, 30_18, 7_03_07, 91, 2]

            _lowerCAmelCase							     =							self.tokenizer(
                __snake_case			,			max_length=len(__snake_case  )			,			padding=__snake_case			,			truncation=__snake_case			,			return_tensors="""pt"""  )
            self.assertIsInstance(__snake_case			,			__snake_case  )

            self.assertEqual((2, 6)			,			batch.input_ids.shape  )
            self.assertEqual((2, 6)			,			batch.attention_mask.shape  )
            _lowerCAmelCase							     =							batch.input_ids.tolist()[0]
            self.assertListEqual(__snake_case			,			__snake_case  )




      def 				lowercase__    (			self			: List[Any]  )				->      Any:
            if not self.test_rust_tokenizer:
                  return

            _lowerCAmelCase							     =							self.get_tokenizer()
            _lowerCAmelCase							     =							self.get_rust_tokenizer()

            _lowerCAmelCase							     =							"""I was born in 92000, and this is falsé."""

            _lowerCAmelCase							     =							tokenizer.tokenize(__snake_case  )
            _lowerCAmelCase							     =							rust_tokenizer.tokenize(__snake_case  )
            self.assertListEqual(__snake_case			,			__snake_case  )

            _lowerCAmelCase							     =							tokenizer.encode(__snake_case			,			add_special_tokens=__snake_case  )
            _lowerCAmelCase							     =							rust_tokenizer.encode(__snake_case			,			add_special_tokens=__snake_case  )
            self.assertListEqual(__snake_case			,			__snake_case  )

            _lowerCAmelCase							     =							self.get_rust_tokenizer()
            _lowerCAmelCase							     =							tokenizer.encode(__snake_case  )
            _lowerCAmelCase							     =							rust_tokenizer.encode(__snake_case  )
            self.assertListEqual(__snake_case			,			__snake_case  )




      @slow
      def 				lowercase__    (			self			: str  )				->      List[str]:
            # fmt: off
            _lowerCAmelCase							     =							{"""input_ids""": [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # moussaKam/mbarthez is a french model. So we also use french texts.
            _lowerCAmelCase							     =							[
                """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
                """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
                """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
                """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
                """telles que la traduction et la synthèse de texte.""",
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=__snake_case			,			model_name="""moussaKam/mbarthez"""			,			revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""			,			sequences=__snake_case			,			)









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       SCREAMING_SNAKE_CASE__					:





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

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





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       ids_tensor([self.batch_size, self.text_seq_length]						,   self.vocab_size)

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

         lowercase__    :   Dict      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__    :   Optional[Any]      =       None
         if self.use_input_mask:
              lowercase__    :   str      =       random_attention_mask([self.batch_size, self.text_seq_length])

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

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

         lowercase__    :   List[str]      =       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       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a):
         lowercase__    :   str      =       TFLayoutLMvaModel(config=a)

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

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

         # text only
         lowercase__    :   List[Any]      =       model(a						,   training=a)
         self.parent.assertEqual(
             result.last_hidden_state.shape						,   (self.batch_size, self.text_seq_length, self.hidden_size))

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





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a						,   a):
         lowercase__    :   Optional[Any]      =       self.num_labels
         lowercase__    :   Optional[Any]      =       TFLayoutLMvaForSequenceClassification(config=a)
         lowercase__    :   List[str]      =       model(
             a						,   bbox=a						,   pixel_values=a						,   attention_mask=a						,   token_type_ids=a						,   labels=a						,   training=a						,   )
         self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels))





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a						,   a):
         lowercase__    :   Tuple      =       self.num_labels
         lowercase__    :   Dict      =       TFLayoutLMvaForTokenClassification(config=a)
         lowercase__    :   Any      =       model(
             a						,   bbox=a						,   pixel_values=a						,   attention_mask=a						,   token_type_ids=a						,   labels=a						,   training=a						,   )
         self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.text_seq_length, self.num_labels))





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a						,   a):
         lowercase__    :   Optional[int]      =       2
         lowercase__    :   List[str]      =       TFLayoutLMvaForQuestionAnswering(config=a)
         lowercase__    :   Tuple      =       model(
             a						,   bbox=a						,   pixel_values=a						,   attention_mask=a						,   token_type_ids=a						,   start_positions=a						,   end_positions=a						,   training=a						,   )
         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       snake_case_				(      self):
         lowercase__    :   Union[str, Any]      =       self.prepare_config_and_inputs()
         ((lowercase__)      , (lowercase__)      , (lowercase__)      , (lowercase__)      , (lowercase__)      , (lowercase__)      , (lowercase__)      , (lowercase__))    :   Optional[Any]      =       config_and_inputs
         lowercase__    :   Optional[Any]      =       {
             '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       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       List[str]														=     (
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    __lowerCamelCase						:       Dict														=     (
        {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    __lowerCamelCase						:       Optional[Any]														=     False
    __lowerCamelCase						:       int														=     False
    __lowerCamelCase						:       int														=     False





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a):
         return True





    def       snake_case_				(      self						,   a						,   a						,   a=False):
         lowercase__    :   Tuple      =       copy.deepcopy(a)

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

         if return_labels:
              if model_class in get_values(a):
                   lowercase__    :   Union[str, Any]      =       tf.ones(self.model_tester.batch_size						,   dtype=tf.intaa)
              elif model_class in get_values(a):
                   lowercase__    :   Dict      =       tf.zeros(self.model_tester.batch_size						,   dtype=tf.intaa)
                   lowercase__    :   Optional[int]      =       tf.zeros(self.model_tester.batch_size						,   dtype=tf.intaa)
              elif model_class in get_values(a):
                   lowercase__    :   Optional[Any]      =       tf.zeros(self.model_tester.batch_size						,   dtype=tf.intaa)
              elif model_class in get_values(a):
                   lowercase__    :   Optional[int]      =       tf.zeros(
                       (self.model_tester.batch_size, self.model_tester.text_seq_length)						,   dtype=tf.intaa)

         return inputs_dict





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       TFLayoutLMvaModelTester(self)
         lowercase__    :   Optional[Any]      =       ConfigTester(self						,   config_class=a						,   hidden_size=37)





    def       snake_case_				(      self):
         self.config_tester.run_common_tests()





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

                   # Test that model correctly compute the loss with kwargs
                   lowercase__    :   Dict      =       self._prepare_for_class(inputs_dict.copy()						,   a						,   return_labels=a)
                   lowercase__    :   int      =       prepared_for_class.pop('input_ids')

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

                   # Get keys that were added with the _prepare_for_class function
                   lowercase__    :   int      =       prepared_for_class.keys() - inputs_dict.keys()
                   lowercase__    :   List[Any]      =       inspect.signature(model.call).parameters
                   lowercase__    :   List[str]      =       list(signature.keys())

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

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

                   for index, value in sorted_tuple_index_mapping:
                        lowercase__    :   Optional[int]      =       prepared_for_class[value]

                   lowercase__    :   Any      =       tuple(a)

                   # Send to model
                   lowercase__    :   List[str]      =       model(tuple_input[:-1])[0]

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





    def       snake_case_				(      self):
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(a						,   a						,   a						,   a						,   a						,   a)





    def       snake_case_				(      self):
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   int      =       self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              lowercase__    :   Dict      =       type
              self.model_tester.create_and_check_model(a						,   a						,   a						,   a						,   a						,   a)





    def       snake_case_				(      self):
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   Tuple      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(
             a						,   a						,   a						,   a						,   a						,   a						,   a)





    def       snake_case_				(      self):
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   Dict      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(
             a						,   a						,   a						,   a						,   a						,   a						,   a)





    def       snake_case_				(      self):
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   List[str]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(
             a						,   a						,   a						,   a						,   a						,   a						,   a)





    @slow
    def       snake_case_				(      self):
         for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__    :   str      =       TFLayoutLMvaModel.from_pretrained(a)
              self.assertIsNotNone(a)



def 	snake_case__			(    ):



     '''simple docstring'''





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


@require_tf
class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    @cached_property
    def       snake_case_				(      self):
         return LayoutLMvaImageProcessor(apply_ocr=a) if is_vision_available() else None





    @slow
    def       snake_case_				(      self):
         lowercase__    :   Optional[int]      =       TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base')

         lowercase__    :   Tuple      =       self.default_image_processor
         lowercase__    :   Union[str, Any]      =       prepare_img()
         lowercase__    :   Optional[int]      =       image_processor(images=a						,   return_tensors='tf').pixel_values

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

         # forward pass
         lowercase__    :   List[str]      =       model(input_ids=a						,   bbox=a						,   pixel_values=a						,   training=a)

         # verify the logits
         lowercase__    :   Optional[int]      =       (1, 199, 768)
         self.assertEqual(outputs.last_hidden_state.shape						,   a)

         lowercase__    :   Union[str, Any]      =       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]						,   a						,   atol=1e-4))

