
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_ : List[str]       =False
							lowercase_ : int       =False
							lowercase_ : Union[str, Any]       =False

							lowercase_ : Optional[Any]       =(
							    (
							        LayoutLMvaModel,
							        LayoutLMvaForSequenceClassification,
							        LayoutLMvaForTokenClassification,
							        LayoutLMvaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_ : Union[str, Any]       =(
							    {'''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))






import logging

from transformers import PretrainedConfig


lowercase__	:int										=					logging.getLogger(__name__)


lowercase__	:Dict										=					{
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[int]       ='''bertabs'''




							def __init__(       self     ,A__=3_0_5_2_2     ,A__=5_1_2     ,A__=6     ,A__=5_1_2     ,A__=8     ,A__=5_1_2     ,A__=0.2     ,A__=6     ,A__=7_6_8     ,A__=8     ,A__=2_0_4_8     ,A__=0.2     ,**A__     ,):
												super().__init__(**A__)

												lowercase					=		vocab_size
												lowercase					=		max_pos

												lowercase					=		enc_layers
												lowercase					=		enc_hidden_size
												lowercase					=		enc_heads
												lowercase					=		enc_ff_size
												lowercase					=		enc_dropout

												lowercase					=		dec_layers
												lowercase					=		dec_hidden_size
												lowercase					=		dec_heads
												lowercase					=		dec_ff_size
												lowercase					=		dec_dropout




