


'''simple docstring'''




from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor

class 		UpperCAmelCase_      :





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      str=1_3    ,      UpperCAmelCase__       :      Dict=3_0    ,      UpperCAmelCase__       :      Optional[Any]=2    ,      UpperCAmelCase__       :      Dict=3    ,      UpperCAmelCase__       :      Dict=True    ,      UpperCAmelCase__       :      Dict=True    ,      UpperCAmelCase__       :      int=3_2    ,      UpperCAmelCase__       :      Optional[Any]=2    ,      UpperCAmelCase__       :      Dict=4    ,      UpperCAmelCase__       :      List[Any]=3_7    ,      UpperCAmelCase__       :      Any="gelu"    ,      UpperCAmelCase__       :      Union[str, Any]=0.1    ,      UpperCAmelCase__       :      List[Any]=0.1    ,      UpperCAmelCase__       :      Optional[Any]=1_0    ,      UpperCAmelCase__       :      Any=0.02    ,      UpperCAmelCase__       :      Union[str, Any]=3    ,      UpperCAmelCase__       :      Optional[Any]=0.6    ,      UpperCAmelCase__       :      str=None    ,      )				->  Any:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			image_size
        lowerCAmelCase     =			patch_size
        lowerCAmelCase     =			num_channels
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_labels
        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     =			type_sequence_label_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			mask_ratio
        lowerCAmelCase     =			scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        lowerCAmelCase     =			(image_size // patch_size) ** 2
        lowerCAmelCase     =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Tuple:
        lowerCAmelCase     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        lowerCAmelCase     =			None
        if self.use_labels:
           lowerCAmelCase     =			ids_tensor([self.batch_size]    ,      self.type_sequence_label_size    )

        lowerCAmelCase     =			self.get_config()

        return config, pixel_values, labels





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  int:
        return ViTMAEConfig(
            image_size=self.image_size    ,      patch_size=self.patch_size    ,      num_channels=self.num_channels    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      intermediate_size=self.intermediate_size    ,      decoder_hidden_size=self.hidden_size    ,      decoder_num_hidden_layers=self.num_hidden_layers    ,      decoder_num_attention_heads=self.num_attention_heads    ,      decoder_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    ,      is_decoder=UpperCAmelCase__    ,      initializer_range=self.initializer_range    ,      mask_ratio=self.mask_ratio    ,      )





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Any    )				->  Tuple:
        lowerCAmelCase     =			TFViTMAEModel(config=UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      training=UpperCAmelCase__    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)    )





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      str    )				->  List[str]:
        lowerCAmelCase     =			TFViTMAEForPreTraining(UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      training=UpperCAmelCase__    )
        # expected sequence length = num_patches
        lowerCAmelCase     =			(self.image_size // self.patch_size) ** 2
        lowerCAmelCase     =			self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, num_patches, expected_num_channels)    )

        # test greyscale images
        lowerCAmelCase     =			1
        lowerCAmelCase     =			TFViTMAEForPreTraining(UpperCAmelCase__    )

        lowerCAmelCase     =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      training=UpperCAmelCase__    )
        lowerCAmelCase     =			self.patch_size**2
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, num_patches, expected_num_channels)    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  int:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        ((lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase))     =			config_and_inputs
        lowerCAmelCase     =			{'pixel_values': pixel_values}
        return config, inputs_dict




@require_tf
class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	str		=     (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     lowerCamelCase				:	List[str]		=     {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

     lowerCamelCase				:	List[str]		=     False
     lowerCamelCase				:	Optional[int]		=     False
     lowerCamelCase				:	Optional[int]		=     False
     lowerCamelCase				:	str		=     False





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Dict:
        lowerCAmelCase     =			TFViTMAEModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    ,      has_text_modality=UpperCAmelCase__    ,      hidden_size=3_7    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  str:
        self.config_tester.run_common_tests()





     @unittest.skip(reason='ViTMAE does not use inputs_embeds'    )
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Dict:
        pass





     def 							__UpperCAmelCase      ( self       :      int    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           self.assertIsInstance(model.get_input_embeddings()    ,      (tf.keras.layers.Layer)    )
           lowerCAmelCase     =			model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(UpperCAmelCase__    ,      tf.keras.layers.Layer    )    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Tuple:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           lowerCAmelCase     =			inspect.signature(model.call    )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowerCAmelCase     =			[*signature.parameters.keys()]

           lowerCAmelCase     =			['pixel_values']
           self.assertListEqual(arg_names[:1]    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  int:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  List[Any]:
        # make the mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           lowerCAmelCase     =			self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

           lowerCAmelCase     =			copy.deepcopy(self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )    )
           lowerCAmelCase     =			model(**UpperCAmelCase__    ,      noise=UpperCAmelCase__    )
           lowerCAmelCase     =			outputs_dict[0].numpy()
           lowerCAmelCase     =			outputs_keywords[0].numpy()

           self.assertLess(np.sum(np.abs(output_dict - output_keywords    )    )    ,      1E-6    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Optional[int]:
        # make the mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

        def prepare_numpy_arrays(UpperCAmelCase__       :      Optional[int]    ):
           lowerCAmelCase     =			{}
           for k, v in inputs_dict.items():
              if tf.is_tensor(UpperCAmelCase__    ):
                 lowerCAmelCase     =			v.numpy()
              else:
                 lowerCAmelCase     =			np.array(UpperCAmelCase__    )

           return inputs_np_dict

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )
           lowerCAmelCase     =			prepare_numpy_arrays(UpperCAmelCase__    )

           lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )
           lowerCAmelCase     =			model(**UpperCAmelCase__    ,      noise=UpperCAmelCase__    )
           self.assert_outputs_same(UpperCAmelCase__    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Tuple:
        # make masks reproducible
        np.random.seed(2    )

        lowerCAmelCase     =			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
        lowerCAmelCase     =			tf.constant(UpperCAmelCase__    )

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        lowerCAmelCase     =			tf_noise

        super().check_pt_tf_models(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Tuple:
        # make mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			{
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__    ),)
            for module_member_name in dir(UpperCAmelCase__    )
            if module_member_name.endswith('MainLayer'    )
            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
            and module_member_name[: -len('MainLayer'    )] == model_class.__name__[: -len('Model'    )]
            for module_member in (getattr(UpperCAmelCase__    ,      UpperCAmelCase__    ),)
            if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    )
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(UpperCAmelCase__    ,      '_keras_serializable'    ,      UpperCAmelCase__    )
        }

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
        lowerCAmelCase     =			tf.convert_to_tensor(UpperCAmelCase__    )
        inputs_dict.update({'noise': noise}    )

        for main_layer_class in tf_main_layer_classes:
           lowerCAmelCase     =			main_layer_class(UpperCAmelCase__    )

           lowerCAmelCase     =			{
               name: tf.keras.Input(tensor.shape[1:]    ,      dtype=tensor.dtype    ) for name, tensor in inputs_dict.items()
           }

           lowerCAmelCase     =			tf.keras.Model(UpperCAmelCase__    ,      outputs=main_layer(UpperCAmelCase__    )    )
           lowerCAmelCase     =			model(UpperCAmelCase__    )

           with tempfile.TemporaryDirectory() as tmpdirname:
              lowerCAmelCase     =			os.path.join(UpperCAmelCase__    ,      'keras_model.h5'    )
              model.save(UpperCAmelCase__    )
              lowerCAmelCase     =			tf.keras.models.load_model(
                  UpperCAmelCase__    ,      custom_objects={main_layer_class.__name__: main_layer_class}    )
              assert isinstance(UpperCAmelCase__    ,      tf.keras.Model    )
              lowerCAmelCase     =			model(UpperCAmelCase__    )
              self.assert_outputs_same(UpperCAmelCase__    ,      UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  List[str]:
        # make mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           lowerCAmelCase     =			self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )
           lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

           if model_class.__name__ == "TFViTMAEModel":
              lowerCAmelCase     =			outputs.last_hidden_state.numpy()
              lowerCAmelCase     =			0
           else:
              lowerCAmelCase     =			outputs.logits.numpy()
              lowerCAmelCase     =			0

           with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(UpperCAmelCase__    ,      saved_model=UpperCAmelCase__    )
              lowerCAmelCase     =			model_class.from_pretrained(UpperCAmelCase__    )
              lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

              if model_class.__name__ == "TFViTMAEModel":
                 lowerCAmelCase     =			after_outputs['last_hidden_state'].numpy()
                 lowerCAmelCase     =			0
              else:
                 lowerCAmelCase     =			after_outputs['logits'].numpy()
                 lowerCAmelCase     =			0

              lowerCAmelCase     =			np.amax(np.abs(out_a - out_a    )    )
              self.assertLessEqual(UpperCAmelCase__    ,      1E-5    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Union[str, Any]:
        # make mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           lowerCAmelCase     =			self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )
           lowerCAmelCase     =			model.get_config()
           # make sure that returned config is jsonifiable, which is required by keras
           json.dumps(UpperCAmelCase__    )
           lowerCAmelCase     =			model_class.from_config(model.get_config()    )
           # make sure it also accepts a normal config
           lowerCAmelCase     =			model_class.from_config(model.config    )
           lowerCAmelCase     =			new_model(UpperCAmelCase__    )  # Build model
           new_model.set_weights(model.get_weights()    )
           lowerCAmelCase     =			new_model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

           self.assert_outputs_same(UpperCAmelCase__    ,      UpperCAmelCase__    )





     @unittest.skip(
         reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'    )
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Tuple:
        pass





     @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'    )
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Union[str, Any]:
        pass





     @slow
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Union[str, Any]:
        lowerCAmelCase     =			TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'    )
        self.assertIsNotNone(UpperCAmelCase__    )





def 	a_					( ):
   lowerCAmelCase     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
   return image




@require_tf
@require_vision
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @cached_property
     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Tuple:
        return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'    ) if is_vision_available() else None





     @slow
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  List[str]:
        # make random mask reproducible across the PT and TF model
        np.random.seed(2    )

        lowerCAmelCase     =			TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'    )

        lowerCAmelCase     =			self.default_image_processor
        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='tf'    )

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        lowerCAmelCase     =			ViTMAEConfig()
        lowerCAmelCase     =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(1, num_patches)    )

        # forward pass
        lowerCAmelCase     =			model(**UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

        # verify the logits
        lowerCAmelCase     =			tf.convert_to_tensor([1, 1_9_6, 7_6_8]    )
        self.assertEqual(outputs.logits.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			tf.convert_to_tensor(
            [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]    )

        tf.debugging.assert_near(outputs.logits[0, :3, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )









'''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 							__UpperCamelCase				:




	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						,				):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  parent
						__a			:       int					    =  batch_size
						__a			:       Any					    =  num_channels
						__a			:       Optional[int]					    =  image_size
						__a			:       Dict					    =  patch_size
						__a			:       int					    =  is_training
						__a			:       Union[str, Any]					    =  use_input_mask
						__a			:       Optional[int]					    =  use_token_type_ids
						__a			:       Dict					    =  use_labels
						__a			:       str					    =  vocab_size
						__a			:       List[Any]					    =  hidden_size
						__a			:       Union[str, Any]					    =  num_hidden_layers
						__a			:       str					    =  num_attention_heads
						__a			:       Union[str, Any]					    =  intermediate_size
						__a			:       Any					    =  hidden_act
						__a			:       List[str]					    =  hidden_dropout_prob
						__a			:       List[str]					    =  attention_probs_dropout_prob
						__a			:       List[Any]					    =  max_position_embeddings
						__a			:       Tuple					    =  type_vocab_size
						__a			:       Any					    =  type_sequence_label_size
						__a			:       Optional[int]					    =  initializer_range
						__a			:       Any					    =  coordinate_size
						__a			:       List[Any]					    =  shape_size
						__a			:       Optional[int]					    =  num_labels
						__a			:       Dict					    =  num_choices
						__a			:       Union[str, Any]					    =  scope
						__a			:       Union[str, Any]					    =  range_bbox

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  ids_tensor([self.batch_size, self.text_seq_length]						,				self.vocab_size							)

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

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

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

						__a			:       str					    =  None
						if self.use_token_type_ids:
											__a			:       List[Any]					    =  ids_tensor([self.batch_size, self.text_seq_length]						,				self.type_vocab_size							)

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

						__a			:       int					    =  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						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Dict					    =  TFLayoutLMvaModel(config=__a							)

						# text + image
						__a			:       List[Any]					    =  model(__a						,				pixel_values=__a						,				training=__a							)
						__a			:       Any					    =  model(
						    __a						,				bbox=__a						,				pixel_values=__a						,				attention_mask=__a						,				token_type_ids=__a						,				training=__a						,				)
						__a			:       Optional[int]					    =  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
						__a			:       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
						__a			:       str					    =  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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Any					    =  self.num_labels
						__a			:       Dict					    =  TFLayoutLMvaForSequenceClassification(config=__a							)
						__a			:       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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  self.num_labels
						__a			:       Optional[Any]					    =  TFLayoutLMvaForTokenClassification(config=__a							)
						__a			:       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.text_seq_length, self.num_labels)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       List[Any]					    =  2
						__a			:       Any					    =  TFLayoutLMvaForQuestionAnswering(config=__a							)
						__a			:       Any					    =  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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  self.prepare_config_and_inputs()
						((__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a))			:       Dict					    =  config_and_inputs
						__a			:       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 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						return True




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=False							):

						'''simple docstring'''





						__a			:       str					    =  copy.deepcopy(__a							)

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

						return inputs_dict




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  TFLayoutLMvaModelTester(self							)
						__a			:       Optional[int]					    =  ConfigTester(self						,				config_class=__a						,				hidden_size=37							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.config_tester.run_common_tests()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

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

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

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

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

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

																for index, value in sorted_tuple_index_mapping:
																					__a			:       Optional[Any]					    =  prepared_for_class[value]

																__a			:       str					    =  tuple(__a							)

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

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(__a						,				__a						,				__a						,				__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       Dict					    =  self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											__a			:       Any					    =  type
											self.model_tester.create_and_check_model(__a						,				__a						,				__a						,				__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(
						    __a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       Optional[Any]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(
						    __a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       int					    =  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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a			:       List[Any]					    =  TFLayoutLMvaModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)






def   lowerCamelCase							():
					__a			:       Optional[Any]					    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
					return image





@require_tf
class 							__UpperCamelCase				(		unittest.TestCase					):




	@cached_property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'							)

						__a			:       Tuple					    =  self.default_image_processor
						__a			:       List[Any]					    =  prepare_img()
						__a			:       int					    =  image_processor(images=__a						,				return_tensors='tf'							).pixel_values

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

						# forward pass
						__a			:       Tuple					    =  model(input_ids=__a						,				bbox=__a						,				pixel_values=__a						,				training=__a							)

						# verify the logits
						__a			:       List[Any]					    =  (1, 199, 768)
						self.assertEqual(outputs.last_hidden_state.shape						,				__a							)

						__a			:       Optional[Any]					    =  tf.constant(
						    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							)

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



