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 __snake_case    :




			def __init__(   self      :    Any	,     _lowercase      :    Tuple	,     _lowercase      :    str=2	,     _lowercase      :    List[Any]=3	,     _lowercase      :    Optional[Any]=4	,     _lowercase      :    Optional[Any]=2	,     _lowercase      :    str=7	,     _lowercase      :    Dict=True	,     _lowercase      :    List[str]=True	,     _lowercase      :    Union[str, Any]=True	,     _lowercase      :    Optional[int]=True	,     _lowercase      :    Dict=99	,     _lowercase      :    Dict=36	,     _lowercase      :    Tuple=2	,     _lowercase      :    Optional[int]=4	,     _lowercase      :    int=37	,     _lowercase      :    Tuple="gelu"	,     _lowercase      :    Optional[Any]=0.1	,     _lowercase      :    Tuple=0.1	,     _lowercase      :    str=5_12	,     _lowercase      :    Dict=16	,     _lowercase      :    int=2	,     _lowercase      :    int=0.02	,     _lowercase      :    Any=6	,     _lowercase      :    List[Any]=6	,     _lowercase      :    List[Any]=3	,     _lowercase      :    List[Any]=4	,     _lowercase      :    int=None	,     _lowercase      :    Optional[int]=10_00	,     ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				parent
					SCREAMING_SNAKE_CASE__      							=				batch_size
					SCREAMING_SNAKE_CASE__      							=				num_channels
					SCREAMING_SNAKE_CASE__      							=				image_size
					SCREAMING_SNAKE_CASE__      							=				patch_size
					SCREAMING_SNAKE_CASE__      							=				is_training
					SCREAMING_SNAKE_CASE__      							=				use_input_mask
					SCREAMING_SNAKE_CASE__      							=				use_token_type_ids
					SCREAMING_SNAKE_CASE__      							=				use_labels
					SCREAMING_SNAKE_CASE__      							=				vocab_size
					SCREAMING_SNAKE_CASE__      							=				hidden_size
					SCREAMING_SNAKE_CASE__      							=				num_hidden_layers
					SCREAMING_SNAKE_CASE__      							=				num_attention_heads
					SCREAMING_SNAKE_CASE__      							=				intermediate_size
					SCREAMING_SNAKE_CASE__      							=				hidden_act
					SCREAMING_SNAKE_CASE__      							=				hidden_dropout_prob
					SCREAMING_SNAKE_CASE__      							=				attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE__      							=				max_position_embeddings
					SCREAMING_SNAKE_CASE__      							=				type_vocab_size
					SCREAMING_SNAKE_CASE__      							=				type_sequence_label_size
					SCREAMING_SNAKE_CASE__      							=				initializer_range
					SCREAMING_SNAKE_CASE__      							=				coordinate_size
					SCREAMING_SNAKE_CASE__      							=				shape_size
					SCREAMING_SNAKE_CASE__      							=				num_labels
					SCREAMING_SNAKE_CASE__      							=				num_choices
					SCREAMING_SNAKE_CASE__      							=				scope
					SCREAMING_SNAKE_CASE__      							=				range_bbox

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




			def      __a						(   self      :    List[str]				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				ids_tensor([self.batch_size, self.text_seq_length]	,     self.vocab_size				)

					SCREAMING_SNAKE_CASE__      							=				ids_tensor([self.batch_size, self.text_seq_length, 4]	,     self.range_bbox				)
					SCREAMING_SNAKE_CASE__      							=				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]:
											SCREAMING_SNAKE_CASE__      							=				bbox[i, j, 3]
											SCREAMING_SNAKE_CASE__      							=				bbox[i, j, 1]
											SCREAMING_SNAKE_CASE__      							=				tmp_coordinate
									if bbox[i, j, 2] < bbox[i, j, 0]:
											SCREAMING_SNAKE_CASE__      							=				bbox[i, j, 2]
											SCREAMING_SNAKE_CASE__      							=				bbox[i, j, 0]
											SCREAMING_SNAKE_CASE__      							=				tmp_coordinate
					SCREAMING_SNAKE_CASE__      							=				tf.constant(_lowercase				)

					SCREAMING_SNAKE_CASE__      							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

					SCREAMING_SNAKE_CASE__      							=				None
					if self.use_input_mask:
							SCREAMING_SNAKE_CASE__      							=				random_attention_mask([self.batch_size, self.text_seq_length]				)

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

					SCREAMING_SNAKE_CASE__      							=				None
					SCREAMING_SNAKE_CASE__      							=				None
					if self.use_labels:
							SCREAMING_SNAKE_CASE__      							=				ids_tensor([self.batch_size]	,     self.type_sequence_label_size				)
							SCREAMING_SNAKE_CASE__      							=				ids_tensor([self.batch_size, self.text_seq_length]	,     self.num_labels				)

					SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    List[str]	,     _lowercase      :    Dict	,     _lowercase      :    List[Any]	,     _lowercase      :    str	,     _lowercase      :    Optional[int]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Tuple				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				TFLayoutLMvaModel(config=_lowercase				)

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

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

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

					# image only
					SCREAMING_SNAKE_CASE__      							=				model({"""pixel_values""": pixel_values}	,     training=_lowercase				)
					self.parent.assertEqual(
					    result.last_hidden_state.shape	,     (self.batch_size, self.image_seq_length, self.hidden_size)				)




			def      __a						(   self      :    int	,     _lowercase      :    int	,     _lowercase      :    Optional[int]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    Tuple	,     _lowercase      :    List[Any]	,     _lowercase      :    Tuple				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				self.num_labels
					SCREAMING_SNAKE_CASE__      							=				TFLayoutLMvaForSequenceClassification(config=_lowercase				)
					SCREAMING_SNAKE_CASE__      							=				model(
					    _lowercase	,     bbox=_lowercase	,     pixel_values=_lowercase	,     attention_mask=_lowercase	,     token_type_ids=_lowercase	,     labels=_lowercase	,     training=_lowercase	,     )
					self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.num_labels)				)




			def      __a						(   self      :    Any	,     _lowercase      :    Dict	,     _lowercase      :    Tuple	,     _lowercase      :    int	,     _lowercase      :    Optional[int]	,     _lowercase      :    Optional[int]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Tuple				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				self.num_labels
					SCREAMING_SNAKE_CASE__      							=				TFLayoutLMvaForTokenClassification(config=_lowercase				)
					SCREAMING_SNAKE_CASE__      							=				model(
					    _lowercase	,     bbox=_lowercase	,     pixel_values=_lowercase	,     attention_mask=_lowercase	,     token_type_ids=_lowercase	,     labels=_lowercase	,     training=_lowercase	,     )
					self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.text_seq_length, self.num_labels)				)




			def      __a						(   self      :    str	,     _lowercase      :    int	,     _lowercase      :    List[str]	,     _lowercase      :    str	,     _lowercase      :    str	,     _lowercase      :    List[str]	,     _lowercase      :    List[str]	,     _lowercase      :    Union[str, Any]				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				2
					SCREAMING_SNAKE_CASE__      							=				TFLayoutLMvaForQuestionAnswering(config=_lowercase				)
					SCREAMING_SNAKE_CASE__      							=				model(
					    _lowercase	,     bbox=_lowercase	,     pixel_values=_lowercase	,     attention_mask=_lowercase	,     token_type_ids=_lowercase	,     start_positions=_lowercase	,     end_positions=_lowercase	,     training=_lowercase	,     )
					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      __a						(   self      :    List[Any]				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				self.prepare_config_and_inputs()
					((SCREAMING_SNAKE_CASE__)							,					(SCREAMING_SNAKE_CASE__)							,					(SCREAMING_SNAKE_CASE__)							,					(SCREAMING_SNAKE_CASE__)							,					(SCREAMING_SNAKE_CASE__)							,					(SCREAMING_SNAKE_CASE__)							,					(SCREAMING_SNAKE_CASE__)							,					(SCREAMING_SNAKE_CASE__))      							=				config_and_inputs
					SCREAMING_SNAKE_CASE__      							=				{
					    """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 __snake_case    (						lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase		):
			lowerCAmelCase_														=							(
			    (
			        TFLayoutLMvaModel,
			        TFLayoutLMvaForQuestionAnswering,
			        TFLayoutLMvaForSequenceClassification,
			        TFLayoutLMvaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCAmelCase_														=							(
			    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
			    if is_tf_available()
			    else {}
			)

			lowerCAmelCase_														=							False
			lowerCAmelCase_														=							False
			lowerCAmelCase_														=							False




			def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    List[str]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    List[Any]				):



					"""simple docstring"""






					return True




			def      __a						(   self      :    List[str]	,     _lowercase      :    List[Any]	,     _lowercase      :    str	,     _lowercase      :    str=False				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				copy.deepcopy(_lowercase				)

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

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

					return inputs_dict




			def      __a						(   self      :    Tuple				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				TFLayoutLMvaModelTester(self				)
					SCREAMING_SNAKE_CASE__      							=				ConfigTester(self	,     config_class=_lowercase	,     hidden_size=37				)




			def      __a						(   self      :    Any				):



					"""simple docstring"""






					self.config_tester.run_common_tests()




			def      __a						(   self      :    Dict				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE__      							=				model_class(_lowercase				)
							if getattr(_lowercase	,     """hf_compute_loss"""	,     _lowercase				):
									# The number of elements in the loss should be the same as the number of elements in the label
									SCREAMING_SNAKE_CASE__      							=				self._prepare_for_class(inputs_dict.copy()	,     _lowercase	,     return_labels=_lowercase				)
									SCREAMING_SNAKE_CASE__      							=				prepared_for_class[
									    sorted(prepared_for_class.keys() - inputs_dict.keys()	,     reverse=_lowercase				)[0]
									]
									SCREAMING_SNAKE_CASE__      							=				added_label.shape.as_list()[:1]

									# Test that model correctly compute the loss with kwargs
									SCREAMING_SNAKE_CASE__      							=				self._prepare_for_class(inputs_dict.copy()	,     _lowercase	,     return_labels=_lowercase				)
									SCREAMING_SNAKE_CASE__      							=				prepared_for_class.pop("""input_ids"""				)

									SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     **_lowercase				)[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
									SCREAMING_SNAKE_CASE__      							=				self._prepare_for_class(inputs_dict.copy()	,     _lowercase	,     return_labels=_lowercase				)
									SCREAMING_SNAKE_CASE__      							=				prepared_for_class.pop("""input_ids"""				)
									if "labels" in prepared_for_class:
											SCREAMING_SNAKE_CASE__      							=				prepared_for_class["""labels"""].numpy()
											if len(labels.shape				) > 1 and labels.shape[1] != 1:
													SCREAMING_SNAKE_CASE__      							=				-1_00
													SCREAMING_SNAKE_CASE__      							=				tf.convert_to_tensor(_lowercase				)
													SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     **_lowercase				)[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
									SCREAMING_SNAKE_CASE__      							=				self._prepare_for_class(inputs_dict.copy()	,     _lowercase	,     return_labels=_lowercase				)
									SCREAMING_SNAKE_CASE__      							=				model(_lowercase				)[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
									SCREAMING_SNAKE_CASE__      							=				self._prepare_for_class(inputs_dict.copy()	,     _lowercase	,     return_labels=_lowercase				)

									# Get keys that were added with the _prepare_for_class function
									SCREAMING_SNAKE_CASE__      							=				prepared_for_class.keys() - inputs_dict.keys()
									SCREAMING_SNAKE_CASE__      							=				inspect.signature(model.call				).parameters
									SCREAMING_SNAKE_CASE__      							=				list(signature.keys()				)

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

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

									for index, value in sorted_tuple_index_mapping:
											SCREAMING_SNAKE_CASE__      							=				prepared_for_class[value]

									SCREAMING_SNAKE_CASE__      							=				tuple(_lowercase				)

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

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




			def      __a						(   self      :    Optional[int]				):



					"""simple docstring"""






					(
					    (
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,										
					)      							=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(_lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase				)




			def      __a						(   self      :    int				):



					"""simple docstring"""






					(
					    (
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,										
					)      							=				self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
							SCREAMING_SNAKE_CASE__      							=				type
							self.model_tester.create_and_check_model(_lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase				)




			def      __a						(   self      :    int				):



					"""simple docstring"""






					(
					    (
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,										
					)      							=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(
					    _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase				)




			def      __a						(   self      :    str				):



					"""simple docstring"""






					(
					    (
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,										
					)      							=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(
					    _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase				)




			def      __a						(   self      :    List[str]				):



					"""simple docstring"""






					(
					    (
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,					(
					    SCREAMING_SNAKE_CASE__					
					)							,										
					)      							=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(
					    _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase	,     _lowercase				)




			@slow
			def      __a						(   self      :    Tuple				):



					"""simple docstring"""






					for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							SCREAMING_SNAKE_CASE__      							=				TFLayoutLMvaModel.from_pretrained(_lowercase				)
							self.assertIsNotNone(_lowercase				)





def      __SCREAMING_SNAKE_CASE  ( )							-> List[Any]:







		"""simple docstring"""





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







@require_tf
class __snake_case    (						unittest.TestCase		):




			@cached_property
			def      __a						(   self      :    Optional[int]				):



					"""simple docstring"""






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




			@slow
			def      __a						(   self      :    Tuple				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""				)

					SCREAMING_SNAKE_CASE__      							=				self.default_image_processor
					SCREAMING_SNAKE_CASE__      							=				prepare_img()
					SCREAMING_SNAKE_CASE__      							=				image_processor(images=_lowercase	,     return_tensors="""tf"""				).pixel_values

					SCREAMING_SNAKE_CASE__      							=				tf.constant([[1, 2]]				)
					SCREAMING_SNAKE_CASE__      							=				tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]				)	,     axis=0				)

					# forward pass
					SCREAMING_SNAKE_CASE__      							=				model(input_ids=_lowercase	,     bbox=_lowercase	,     pixel_values=_lowercase	,     training=_lowercase				)

					# verify the logits
					SCREAMING_SNAKE_CASE__      							=				(1, 1_99, 7_68)
					self.assertEqual(outputs.last_hidden_state.shape	,     _lowercase				)

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

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

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :			Optional[int]							       =		{
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[Any]							       =		['''WhisperTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''WhisperForConditionalGeneration''',
       '''WhisperModel''',
       '''WhisperPreTrainedModel''',
       '''WhisperForAudioClassification''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Tuple							       =		[
       '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFWhisperForConditionalGeneration''',
       '''TFWhisperModel''',
       '''TFWhisperPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			List[Any]							       =		[
       '''FlaxWhisperForConditionalGeneration''',
       '''FlaxWhisperModel''',
       '''FlaxWhisperPreTrainedModel''',
       '''FlaxWhisperForAudioClassification''',
   ]


if TYPE_CHECKING:
   from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
   from .feature_extraction_whisper import WhisperFeatureExtractor
   from .processing_whisper import WhisperProcessor
   from .tokenization_whisper import WhisperTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_whisper_fast import WhisperTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_whisper import (
          WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WhisperForAudioClassification,
          WhisperForConditionalGeneration,
          WhisperModel,
          WhisperPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_whisper import (
          TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWhisperForConditionalGeneration,
          TFWhisperModel,
          TFWhisperPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_whisper import (
          FlaxWhisperForAudioClassification,
          FlaxWhisperForConditionalGeneration,
          FlaxWhisperModel,
          FlaxWhisperPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase      :			Tuple							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
