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				,    lowercase_				,    lowercase_=2				,    lowercase_=3				,    lowercase_=4				,    lowercase_=2				,    lowercase_=7				,    lowercase_=True				,    lowercase_=True				,    lowercase_=True				,    lowercase_=True				,    lowercase_=99				,    lowercase_=36				,    lowercase_=3				,    lowercase_=4				,    lowercase_=37				,    lowercase_="gelu"				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=512				,    lowercase_=16				,    lowercase_=2				,    lowercase_=0.02				,    lowercase_=6				,    lowercase_=6				,    lowercase_=3				,    lowercase_=4				,    lowercase_=None				,    lowercase_=1_000				,    ):
													_snake_case						:					Optional[Any]    =   parent
													_snake_case						:					List[str]    =   batch_size
													_snake_case						:					str    =   num_channels
													_snake_case						:					str    =   image_size
													_snake_case						:					Union[str, Any]    =   patch_size
													_snake_case						:					str    =   text_seq_length
													_snake_case						:					List[str]    =   is_training
													_snake_case						:					int    =   use_input_mask
													_snake_case						:					Any    =   use_token_type_ids
													_snake_case						:					List[Any]    =   use_labels
													_snake_case						:					Union[str, Any]    =   vocab_size
													_snake_case						:					List[str]    =   hidden_size
													_snake_case						:					str    =   num_hidden_layers
													_snake_case						:					List[str]    =   num_attention_heads
													_snake_case						:					str    =   intermediate_size
													_snake_case						:					int    =   hidden_act
													_snake_case						:					Optional[Any]    =   hidden_dropout_prob
													_snake_case						:					int    =   attention_probs_dropout_prob
													_snake_case						:					List[str]    =   max_position_embeddings
													_snake_case						:					Dict    =   type_vocab_size
													_snake_case						:					int    =   type_sequence_label_size
													_snake_case						:					str    =   initializer_range
													_snake_case						:					Tuple    =   coordinate_size
													_snake_case						:					Tuple    =   shape_size
													_snake_case						:					int    =   num_labels
													_snake_case						:					Any    =   num_choices
													_snake_case						:					Tuple    =   scope
													_snake_case						:					List[str]    =   range_bbox

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   ids_tensor([self.batch_size, self.text_seq_length]				,    self.vocab_size    )

													_snake_case						:					Union[str, 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]:
																															_snake_case						:					Union[str, Any]    =   bbox[i, j, 3]
																															_snake_case						:					List[Any]    =   bbox[i, j, 1]
																															_snake_case						:					Optional[Any]    =   t
																									if bbox[i, j, 2] < bbox[i, j, 0]:
																															_snake_case						:					List[Any]    =   bbox[i, j, 2]
																															_snake_case						:					Tuple    =   bbox[i, j, 0]
																															_snake_case						:					Optional[Any]    =   t

													_snake_case						:					Any    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

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

													_snake_case						:					List[Any]    =   None
													_snake_case						:					Tuple    =   None
													if self.use_labels:
																			_snake_case						:					List[str]    =   ids_tensor([self.batch_size]				,    self.type_sequence_label_size    )
																			_snake_case						:					Union[str, Any]    =   ids_tensor([self.batch_size, self.text_seq_length]				,    self.num_labels    )

													_snake_case						:					Dict    =   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				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					str    =   LayoutLMvaModel(config=_SCREAMING_SNAKE_CASE    )
													model.to(_SCREAMING_SNAKE_CASE    )
													model.eval()

													# text + image
													_snake_case						:					Any    =   model(_SCREAMING_SNAKE_CASE				,    pixel_values=_SCREAMING_SNAKE_CASE    )
													_snake_case						:					Any    =   model(
													    _SCREAMING_SNAKE_CASE				,    bbox=_SCREAMING_SNAKE_CASE				,    pixel_values=_SCREAMING_SNAKE_CASE				,    attention_mask=_SCREAMING_SNAKE_CASE				,    token_type_ids=_SCREAMING_SNAKE_CASE    )
													_snake_case						:					Union[str, Any]    =   model(_SCREAMING_SNAKE_CASE				,    bbox=_SCREAMING_SNAKE_CASE				,    pixel_values=_SCREAMING_SNAKE_CASE				,    token_type_ids=_SCREAMING_SNAKE_CASE    )
													_snake_case						:					List[Any]    =   model(_SCREAMING_SNAKE_CASE				,    bbox=_SCREAMING_SNAKE_CASE				,    pixel_values=_SCREAMING_SNAKE_CASE    )

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

													# text only
													_snake_case						:					int    =   model(_SCREAMING_SNAKE_CASE    )
													self.parent.assertEqual(
													    result.last_hidden_state.shape				,    (self.batch_size, self.text_seq_length, self.hidden_size)    )

													# image only
													_snake_case						:					List[Any]    =   model(pixel_values=_SCREAMING_SNAKE_CASE    )
													self.parent.assertEqual(
													    result.last_hidden_state.shape				,    (self.batch_size, self.image_seq_length, self.hidden_size)    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Union[str, Any]    =   self.num_labels
													_snake_case						:					Optional[int]    =   LayoutLMvaForSequenceClassification(_SCREAMING_SNAKE_CASE    )
													model.to(_SCREAMING_SNAKE_CASE    )
													model.eval()
													_snake_case						:					str    =   model(
													    _SCREAMING_SNAKE_CASE				,    bbox=_SCREAMING_SNAKE_CASE				,    pixel_values=_SCREAMING_SNAKE_CASE				,    attention_mask=_SCREAMING_SNAKE_CASE				,    token_type_ids=_SCREAMING_SNAKE_CASE				,    labels=_SCREAMING_SNAKE_CASE				,    )
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Optional[Any]    =   self.num_labels
													_snake_case						:					Dict    =   LayoutLMvaForTokenClassification(config=_SCREAMING_SNAKE_CASE    )
													model.to(_SCREAMING_SNAKE_CASE    )
													model.eval()
													_snake_case						:					List[str]    =   model(
													    _SCREAMING_SNAKE_CASE				,    bbox=_SCREAMING_SNAKE_CASE				,    pixel_values=_SCREAMING_SNAKE_CASE				,    attention_mask=_SCREAMING_SNAKE_CASE				,    token_type_ids=_SCREAMING_SNAKE_CASE				,    labels=_SCREAMING_SNAKE_CASE				,    )
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.text_seq_length, self.num_labels)    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					List[str]    =   LayoutLMvaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE    )
													model.to(_SCREAMING_SNAKE_CASE    )
													model.eval()
													_snake_case						:					int    =   model(
													    _SCREAMING_SNAKE_CASE				,    bbox=_SCREAMING_SNAKE_CASE				,    pixel_values=_SCREAMING_SNAKE_CASE				,    attention_mask=_SCREAMING_SNAKE_CASE				,    token_type_ids=_SCREAMING_SNAKE_CASE				,    start_positions=_SCREAMING_SNAKE_CASE				,    end_positions=_SCREAMING_SNAKE_CASE				,    )
													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    ):
													_snake_case						:					Dict    =   self.prepare_config_and_inputs()
													(
													    _snake_case     
													)						:					List[str]    =   config_and_inputs
													_snake_case						:					Union[str, 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_torch
class        lowercase_   (					__SCREAMING_SNAKE_CASE						,			__SCREAMING_SNAKE_CASE						,			unittest.TestCase		):
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False

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



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													return True



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   LayoutLMvaModelTester(self    )
													_snake_case						:					Union[str, Any]    =   ConfigTester(self				,    config_class=_SCREAMING_SNAKE_CASE				,    hidden_size=37    )



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

													return inputs_dict



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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																			_snake_case						:					int    =   type
																			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE    )



							@slow
							def 	UpperCamelCase	(						self    ):

													for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:					Dict    =   LayoutLMvaModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
																			self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )




def  snake_case						()	->	int:

						'''simple docstring'''



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



@require_torch
class        lowercase_   (					unittest.TestCase		):



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



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"    ).to(_SCREAMING_SNAKE_CASE    )

													_snake_case						:					List[Any]    =   self.default_image_processor
													_snake_case						:					Dict    =   prepare_img()
													_snake_case						:					int    =   image_processor(images=_SCREAMING_SNAKE_CASE				,    return_tensors="pt"    ).pixel_values.to(_SCREAMING_SNAKE_CASE    )

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

													# forward pass
													_snake_case						:					Optional[Any]    =   model(
													    input_ids=input_ids.to(_SCREAMING_SNAKE_CASE    )				,    bbox=bbox.to(_SCREAMING_SNAKE_CASE    )				,    pixel_values=pixel_values.to(_SCREAMING_SNAKE_CASE    )				,    )

													# verify the logits
													_snake_case						:					Union[str, Any]    =   torch.Size((1, 199, 768)    )
													self.assertEqual(outputs.last_hidden_state.shape				,    _SCREAMING_SNAKE_CASE    )

													_snake_case						:					Dict    =   torch.tensor(
													    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]    ).to(_SCREAMING_SNAKE_CASE    )

													self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]				,    _SCREAMING_SNAKE_CASE				,    atol=1e-4    )    )
def  snake_case						(__lowercase ,      __lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					Tuple    =   ""
						for word_or_phrase in separated:
												if not isinstance(__lowercase ,      __lowercase						):
																		raise Exception("join() accepts only strings to be joined"						)
												joined += word_or_phrase + separator
						return joined.strip(__lowercase						)


if __name__ == "__main__":
				from doctest import testmod

				testmod()