

"""simple docstring"""





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 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							Any  ,lowercase_     :							Tuple  ,lowercase_     :							str=2  ,lowercase_     :							Tuple=3  ,lowercase_     :							Union[str, Any]=4  ,lowercase_     :							Optional[Any]=2  ,lowercase_     :							Optional[Any]=7  ,lowercase_     :							Union[str, Any]=True  ,lowercase_     :							Dict=True  ,lowercase_     :							str=True  ,lowercase_     :							Dict=True  ,lowercase_     :							str=9_9  ,lowercase_     :							Tuple=3_6  ,lowercase_     :							Any=3  ,lowercase_     :							str=4  ,lowercase_     :							List[str]=3_7  ,lowercase_     :							Optional[Any]="gelu"  ,lowercase_     :							List[str]=0.1  ,lowercase_     :							Tuple=0.1  ,lowercase_     :							Optional[Any]=5_1_2  ,lowercase_     :							int=1_6  ,lowercase_     :							Tuple=2  ,lowercase_     :							int=0.02  ,lowercase_     :							Dict=6  ,lowercase_     :							Optional[int]=6  ,lowercase_     :							Tuple=3  ,lowercase_     :							Union[str, Any]=4  ,lowercase_     :							Dict=None  ,lowercase_     :							int=1_0_0_0  ,):
						lowerCAmelCase__      :		Any    							=      parent
						lowerCAmelCase__      :		Optional[Any]    							=      batch_size
						lowerCAmelCase__      :		int    							=      num_channels
						lowerCAmelCase__      :		List[str]    							=      image_size
						lowerCAmelCase__      :		Union[str, Any]    							=      patch_size
						lowerCAmelCase__      :		Tuple    							=      text_seq_length
						lowerCAmelCase__      :		str    							=      is_training
						lowerCAmelCase__      :		Dict    							=      use_input_mask
						lowerCAmelCase__      :		Any    							=      use_token_type_ids
						lowerCAmelCase__      :		Dict    							=      use_labels
						lowerCAmelCase__      :		str    							=      vocab_size
						lowerCAmelCase__      :		str    							=      hidden_size
						lowerCAmelCase__      :		List[Any]    							=      num_hidden_layers
						lowerCAmelCase__      :		Union[str, Any]    							=      num_attention_heads
						lowerCAmelCase__      :		List[Any]    							=      intermediate_size
						lowerCAmelCase__      :		Any    							=      hidden_act
						lowerCAmelCase__      :		int    							=      hidden_dropout_prob
						lowerCAmelCase__      :		Union[str, Any]    							=      attention_probs_dropout_prob
						lowerCAmelCase__      :		str    							=      max_position_embeddings
						lowerCAmelCase__      :		Dict    							=      type_vocab_size
						lowerCAmelCase__      :		Optional[Any]    							=      type_sequence_label_size
						lowerCAmelCase__      :		Tuple    							=      initializer_range
						lowerCAmelCase__      :		Optional[Any]    							=      coordinate_size
						lowerCAmelCase__      :		Dict    							=      shape_size
						lowerCAmelCase__      :		Tuple    							=      num_labels
						lowerCAmelCase__      :		str    							=      num_choices
						lowerCAmelCase__      :		Union[str, Any]    							=      scope
						lowerCAmelCase__      :		Any    							=      range_bbox

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







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      ids_tensor([self.batch_size, self.text_seq_length]  ,self.vocab_size						)

						lowerCAmelCase__      :		Any    							=      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]:
									lowerCAmelCase__      :		Optional[int]    							=      bbox[i, j, 3]
									lowerCAmelCase__      :		Optional[Any]    							=      bbox[i, j, 1]
									lowerCAmelCase__      :		Dict    							=      t
								if bbox[i, j, 2] < bbox[i, j, 0]:
									lowerCAmelCase__      :		List[str]    							=      bbox[i, j, 2]
									lowerCAmelCase__      :		str    							=      bbox[i, j, 0]
									lowerCAmelCase__      :		Optional[Any]    							=      t

						lowerCAmelCase__      :		List[str]    							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

						lowerCAmelCase__      :		Any    							=      None
						if self.use_input_mask:
							lowerCAmelCase__      :		int    							=      random_attention_mask([self.batch_size, self.text_seq_length]						)

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

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

						lowerCAmelCase__      :		Optional[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 			__lowerCAmelCase					(			self     :							Any  ,lowercase_     :							int  ,lowercase_     :							Optional[Any]  ,lowercase_     :							Optional[int]  ,lowercase_     :							Any  ,lowercase_     :							Tuple  ,lowercase_     :							Union[str, Any]  ,lowercase_     :							List[Any]  ,lowercase_     :							Tuple						):
						lowerCAmelCase__      :		Optional[Any]    							=      LayoutLMvaModel(config=lowercase_						)
						model.to(lowercase_						)
						model.eval()

						# text + image
						lowerCAmelCase__      :		Tuple    							=      model(lowercase_  ,pixel_values=lowercase_						)
						lowerCAmelCase__      :		Dict    							=      model(
						    lowercase_  ,bbox=lowercase_  ,pixel_values=lowercase_  ,attention_mask=lowercase_  ,token_type_ids=lowercase_						)
						lowerCAmelCase__      :		Union[str, Any]    							=      model(lowercase_  ,bbox=lowercase_  ,pixel_values=lowercase_  ,token_type_ids=lowercase_						)
						lowerCAmelCase__      :		Dict    							=      model(lowercase_  ,bbox=lowercase_  ,pixel_values=lowercase_						)

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

						# text only
						lowerCAmelCase__      :		Union[str, Any]    							=      model(lowercase_						)
						self.parent.assertEqual(
						    result.last_hidden_state.shape  ,(self.batch_size, self.text_seq_length, self.hidden_size)						)

						# image only
						lowerCAmelCase__      :		Optional[Any]    							=      model(pixel_values=lowercase_						)
						self.parent.assertEqual(
						    result.last_hidden_state.shape  ,(self.batch_size, self.image_seq_length, self.hidden_size)						)







					def 			__lowerCAmelCase					(			self     :							List[str]  ,lowercase_     :							List[str]  ,lowercase_     :							str  ,lowercase_     :							Tuple  ,lowercase_     :							str  ,lowercase_     :							List[Any]  ,lowercase_     :							List[Any]  ,lowercase_     :							str  ,lowercase_     :							Optional[Any]						):
						lowerCAmelCase__      :		str    							=      self.num_labels
						lowerCAmelCase__      :		Optional[Any]    							=      LayoutLMvaForSequenceClassification(lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		str    							=      model(
						    lowercase_  ,bbox=lowercase_  ,pixel_values=lowercase_  ,attention_mask=lowercase_  ,token_type_ids=lowercase_  ,labels=lowercase_  ,)
						self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							Any  ,lowercase_     :							str  ,lowercase_     :							Any  ,lowercase_     :							int  ,lowercase_     :							Tuple  ,lowercase_     :							List[Any]  ,lowercase_     :							str  ,lowercase_     :							Any						):
						lowerCAmelCase__      :		Any    							=      self.num_labels
						lowerCAmelCase__      :		Tuple    							=      LayoutLMvaForTokenClassification(config=lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		Union[str, Any]    							=      model(
						    lowercase_  ,bbox=lowercase_  ,pixel_values=lowercase_  ,attention_mask=lowercase_  ,token_type_ids=lowercase_  ,labels=lowercase_  ,)
						self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.text_seq_length, self.num_labels)						)







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Optional[Any]  ,lowercase_     :							Union[str, Any]  ,lowercase_     :							str  ,lowercase_     :							Optional[int]  ,lowercase_     :							int  ,lowercase_     :							Optional[Any]  ,lowercase_     :							Dict  ,lowercase_     :							Tuple						):
						lowerCAmelCase__      :		Union[str, Any]    							=      LayoutLMvaForQuestionAnswering(config=lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		Optional[int]    							=      model(
						    lowercase_  ,bbox=lowercase_  ,pixel_values=lowercase_  ,attention_mask=lowercase_  ,token_type_ids=lowercase_  ,start_positions=lowercase_  ,end_positions=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 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		int    							=      self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,(
						    lowerCAmelCase__
						)							,
						)      :		List[str]    							=      config_and_inputs
						lowerCAmelCase__      :		Optional[int]    							=      {
						    '''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 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False

					lowercase__												=			(
					    (
					        LayoutLMvaModel,
					        LayoutLMvaForSequenceClassification,
					        LayoutLMvaForTokenClassification,
					        LayoutLMvaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					lowercase__												=			(
					    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
					    if is_torch_available()
					    else {}
					)







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							Dict  ,lowercase_     :							str  ,lowercase_     :							Optional[int]  ,lowercase_     :							List[str]  ,lowercase_     :							int						):
						# `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 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		str    							=      LayoutLMvaModelTester(self						)
						lowerCAmelCase__      :		List[Any]    							=      ConfigTester(self  ,config_class=lowercase_  ,hidden_size=3_7						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]  ,lowercase_     :							str  ,lowercase_     :							str  ,lowercase_     :							Dict=False						):
						lowerCAmelCase__      :		str    							=      copy.deepcopy(lowercase_						)
						if model_class in get_values(lowercase_						):
							lowerCAmelCase__      :		Any    							=      {
							    k: v.unsqueeze(1						).expand(-1  ,self.model_tester.num_choices  ,-1						).contiguous()
							    if isinstance(lowercase_  ,torch.Tensor						) and v.ndim > 1
							    else v
							    for k, v in inputs_dict.items()
							}
						if return_labels:
							if model_class in get_values(lowercase_						):
								lowerCAmelCase__      :		Union[str, Any]    							=      torch.ones(self.model_tester.batch_size  ,dtype=torch.long  ,device=lowercase_						)
							elif model_class in get_values(lowercase_						):
								lowerCAmelCase__      :		Tuple    							=      torch.zeros(
								    self.model_tester.batch_size  ,dtype=torch.long  ,device=lowercase_						)
								lowerCAmelCase__      :		int    							=      torch.zeros(
								    self.model_tester.batch_size  ,dtype=torch.long  ,device=lowercase_						)
							elif model_class in [
							    *get_values(lowercase_						),
							]:
								lowerCAmelCase__      :		Union[str, Any]    							=      torch.zeros(
								    self.model_tester.batch_size  ,dtype=torch.long  ,device=lowercase_						)
							elif model_class in [
							    *get_values(lowercase_						),
							]:
								lowerCAmelCase__      :		List[Any]    							=      torch.zeros(
								    (self.model_tester.batch_size, self.model_tester.text_seq_length)  ,dtype=torch.long  ,device=lowercase_  ,)

						return inputs_dict







					def 			__lowerCAmelCase					(			self     :							Dict						):
						self.config_tester.run_common_tests()







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Any    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[str]    							=      self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
							lowerCAmelCase__      :		Any    							=      type
							self.model_tester.create_and_check_model(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		List[Any]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Tuple    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		str    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*lowercase_						)







					@slow
					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__      :		Any    							=      LayoutLMvaModel.from_pretrained(lowercase_						)
							self.assertIsNotNone(lowercase_						)

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		Any    							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
	return image







@require_torch
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					@cached_property
					def 			__lowerCAmelCase					(			self     :							Tuple						):
						return LayoutLMvaImageProcessor(apply_ocr=lowercase_						) if is_vision_available() else None







					@slow
					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						lowerCAmelCase__      :		List[Any]    							=      LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''						).to(lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      self.default_image_processor
						lowerCAmelCase__      :		Any    							=      prepare_img()
						lowerCAmelCase__      :		Tuple    							=      image_processor(images=lowercase_  ,return_tensors='''pt'''						).pixel_values.to(lowercase_						)

						lowerCAmelCase__      :		List[Any]    							=      torch.tensor([[1, 2]]						)
						lowerCAmelCase__      :		Optional[int]    							=      torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]						).unsqueeze(0						)

						# forward pass
						lowerCAmelCase__      :		Any    							=      model(
						    input_ids=input_ids.to(lowercase_						)  ,bbox=bbox.to(lowercase_						)  ,pixel_values=pixel_values.to(lowercase_						)  ,)

						# verify the logits
						lowerCAmelCase__      :		int    							=      torch.Size((1, 1_9_9, 7_6_8)						)
						self.assertEqual(outputs.last_hidden_state.shape  ,lowercase_						)

						lowerCAmelCase__      :		Optional[Any]    							=      torch.tensor(
						    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]						).to(lowercase_						)

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







"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCamelCase    : List[str]				 =						logging.get_logger(__name__)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,*lowercase_     :							Dict  ,**lowercase_     :							List[str]						):
						warnings.warn(
						    '''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
						    ''' Please use ImageGPTImageProcessor instead.'''  ,lowercase_  ,)
						super().__init__(*lowercase_  ,**lowercase_						)




