
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor







class     lowercase		:




							def __init__(    self       ,A__       ,A__=2       ,A__=3       ,A__=4       ,A__=2       ,A__=7       ,A__=True       ,A__=True       ,A__=True       ,A__=True       ,A__=9_9       ,A__=3_6       ,A__=3       ,A__=4       ,A__=3_7       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=1_6       ,A__=2       ,A__=0.02       ,A__=6       ,A__=6       ,A__=3       ,A__=4       ,A__=None       ,A__=1_0_0_0       ,):
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						num_channels
												lowercase						     =						image_size
												lowercase						     =						patch_size
												lowercase						     =						text_seq_length
												lowercase						     =						is_training
												lowercase						     =						use_input_mask
												lowercase						     =						use_token_type_ids
												lowercase						     =						use_labels
												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_act
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						type_vocab_size
												lowercase						     =						type_sequence_label_size
												lowercase						     =						initializer_range
												lowercase						     =						coordinate_size
												lowercase						     =						shape_size
												lowercase						     =						num_labels
												lowercase						     =						num_choices
												lowercase						     =						scope
												lowercase						     =						range_bbox

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




							def 			A__				(    self):
												lowercase						     =						ids_tensor([self.batch_size, self.text_seq_length]       ,self.vocab_size)

												lowercase						     =						ids_tensor([self.batch_size, self.text_seq_length, 4]       ,self.range_bbox)
												# 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						     =						bbox[i, j, 3]
																											lowercase						     =						bbox[i, j, 1]
																											lowercase						     =						t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																											lowercase						     =						bbox[i, j, 2]
																											lowercase						     =						bbox[i, j, 0]
																											lowercase						     =						t

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

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

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

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

												lowercase						     =						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       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						LayoutLMvaModel(config=A__)
												model.to(A__)
												model.eval()

												# text + image
												lowercase						     =						model(A__       ,pixel_values=A__)
												lowercase						     =						model(
												    A__       ,bbox=A__       ,pixel_values=A__       ,attention_mask=A__       ,token_type_ids=A__)
												lowercase						     =						model(A__       ,bbox=A__       ,pixel_values=A__       ,token_type_ids=A__)
												lowercase						     =						model(A__       ,bbox=A__       ,pixel_values=A__)

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

												# text only
												lowercase						     =						model(A__)
												self.parent.assertEqual(
												    result.last_hidden_state.shape       ,(self.batch_size, self.text_seq_length, self.hidden_size))

												# image only
												lowercase						     =						model(pixel_values=A__)
												self.parent.assertEqual(
												    result.last_hidden_state.shape       ,(self.batch_size, self.image_seq_length, self.hidden_size))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_labels
												lowercase						     =						LayoutLMvaForSequenceClassification(A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(
												    A__       ,bbox=A__       ,pixel_values=A__       ,attention_mask=A__       ,token_type_ids=A__       ,labels=A__       ,)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_labels
												lowercase						     =						LayoutLMvaForTokenClassification(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(
												    A__       ,bbox=A__       ,pixel_values=A__       ,attention_mask=A__       ,token_type_ids=A__       ,labels=A__       ,)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.text_seq_length, self.num_labels))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						LayoutLMvaForQuestionAnswering(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(
												    A__       ,bbox=A__       ,pixel_values=A__       ,attention_mask=A__       ,token_type_ids=A__       ,start_positions=A__       ,end_positions=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 			A__				(    self):
												lowercase						     =						self.prepare_config_and_inputs()
												(
												    (
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						
												)						     =						config_and_inputs
												lowercase						     =						{
												    '''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_torch
class     lowercase		( SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	str	 =False
							lowercase_	:	Tuple	 =False
							lowercase_	:	List[Any]	 =False

							lowercase_	:	Union[str, Any]	 =(
							    (
							        LayoutLMvaModel,
							        LayoutLMvaForSequenceClassification,
							        LayoutLMvaForTokenClassification,
							        LayoutLMvaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_	:	int	 =(
							    {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
							    if is_torch_available()
							    else {}
							)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__):
												# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
												# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
												# the sequence dimension of the text embedding only.
												# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
												return True




							def 			A__				(    self):
												lowercase						     =						LayoutLMvaModelTester(self)
												lowercase						     =						ConfigTester(self       ,config_class=A__       ,hidden_size=3_7)




							def 			A__				(    self       ,A__       ,A__       ,A__=False):
												lowercase						     =						copy.deepcopy(A__)
												if model_class in get_values(A__):
																	lowercase						     =						{
																	    k: v.unsqueeze(1).expand(-1       ,self.model_tester.num_choices       ,-1).contiguous()
																	    if isinstance(A__       ,torch.Tensor) and v.ndim > 1
																	    else v
																	    for k, v in inputs_dict.items()
																	}
												if return_labels:
																	if model_class in get_values(A__):
																						lowercase						     =						torch.ones(self.model_tester.batch_size       ,dtype=torch.long       ,device=A__)
																	elif model_class in get_values(A__):
																						lowercase						     =						torch.zeros(
																						    self.model_tester.batch_size       ,dtype=torch.long       ,device=A__)
																						lowercase						     =						torch.zeros(
																						    self.model_tester.batch_size       ,dtype=torch.long       ,device=A__)
																	elif model_class in [
																	    *get_values(A__),
																	]:
																						lowercase						     =						torch.zeros(
																						    self.model_tester.batch_size       ,dtype=torch.long       ,device=A__)
																	elif model_class in [
																	    *get_values(A__),
																	]:
																						lowercase						     =						torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.text_seq_length)       ,dtype=torch.long       ,device=A__       ,)

												return inputs_dict




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




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																	lowercase						     =						type
																	self.model_tester.create_and_check_model(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*A__)





							@slow
							def 			A__				(    self):
												for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase						     =						LayoutLMvaModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
					return image







@require_torch
class     lowercase		( unittest.TestCase		):




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





							@slow
							def 			A__				(    self):
												lowercase						     =						LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''').to(A__)

												lowercase						     =						self.default_image_processor
												lowercase						     =						prepare_img()
												lowercase						     =						image_processor(images=A__       ,return_tensors='''pt''').pixel_values.to(A__)

												lowercase						     =						torch.tensor([[1, 2]])
												lowercase						     =						torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0)

												# forward pass
												lowercase						     =						model(
												    input_ids=input_ids.to(A__)       ,bbox=bbox.to(A__)       ,pixel_values=pixel_values.to(A__)       ,)

												# verify the logits
												lowercase						     =						torch.Size((1, 1_9_9, 7_6_8))
												self.assertEqual(outputs.last_hidden_state.shape       ,A__)

												lowercase						     =						torch.tensor(
												    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]).to(A__)

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






from typing import TYPE_CHECKING

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


lowercase__		:str			  =   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:str			  =   ["ViTFeatureExtractor"]
							lowercase__		:int			  =   ["ViTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:int			  =   [
							    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTForImageClassification",
							    "ViTForMaskedImageModeling",
							    "ViTModel",
							    "ViTPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Any			  =   [
							    "TFViTForImageClassification",
							    "TFViTModel",
							    "TFViTPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "FlaxViTForImageClassification",
							    "FlaxViTModel",
							    "FlaxViTPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_vit import ViTFeatureExtractor
														from .image_processing_vit import ViTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit import (
														    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTForImageClassification,
														    ViTForMaskedImageModeling,
														    ViTModel,
														    ViTPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
							import sys

							lowercase__		:Dict			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




