



'''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 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase=2			,				UpperCamelCase=3			,				UpperCamelCase=4			,				UpperCamelCase=2			,				UpperCamelCase=7			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=99			,				UpperCamelCase=36			,				UpperCamelCase=3			,				UpperCamelCase=4			,				UpperCamelCase=37			,				UpperCamelCase="gelu"			,				UpperCamelCase=0.1			,				UpperCamelCase=0.1			,				UpperCamelCase=512			,				UpperCamelCase=16			,				UpperCamelCase=2			,				UpperCamelCase=0.02			,				UpperCamelCase=6			,				UpperCamelCase=6			,				UpperCamelCase=3			,				UpperCamelCase=4			,				UpperCamelCase=None			,				UpperCamelCase=1000			,				)     ->			Tuple:
						__lowerCAmelCase												=  parent
						__lowerCAmelCase												=  batch_size
						__lowerCAmelCase												=  num_channels
						__lowerCAmelCase												=  image_size
						__lowerCAmelCase												=  patch_size
						__lowerCAmelCase												=  text_seq_length
						__lowerCAmelCase												=  is_training
						__lowerCAmelCase												=  use_input_mask
						__lowerCAmelCase												=  use_token_type_ids
						__lowerCAmelCase												=  use_labels
						__lowerCAmelCase												=  vocab_size
						__lowerCAmelCase												=  hidden_size
						__lowerCAmelCase												=  num_hidden_layers
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  intermediate_size
						__lowerCAmelCase												=  hidden_act
						__lowerCAmelCase												=  hidden_dropout_prob
						__lowerCAmelCase												=  attention_probs_dropout_prob
						__lowerCAmelCase												=  max_position_embeddings
						__lowerCAmelCase												=  type_vocab_size
						__lowerCAmelCase												=  type_sequence_label_size
						__lowerCAmelCase												=  initializer_range
						__lowerCAmelCase												=  coordinate_size
						__lowerCAmelCase												=  shape_size
						__lowerCAmelCase												=  num_labels
						__lowerCAmelCase												=  num_choices
						__lowerCAmelCase												=  scope
						__lowerCAmelCase												=  range_bbox

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



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  ids_tensor([self.batch_size, self.text_seq_length]			,				self.vocab_size     )

						__lowerCAmelCase												=  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												=  bbox[i, j, 3]
									__lowerCAmelCase												=  bbox[i, j, 1]
									__lowerCAmelCase												=  t
								if bbox[i, j, 2] < bbox[i, j, 0]:
									__lowerCAmelCase												=  bbox[i, j, 2]
									__lowerCAmelCase												=  bbox[i, j, 0]
									__lowerCAmelCase												=  t

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

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

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

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

						__lowerCAmelCase												=  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			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			int:
						__lowerCAmelCase												=  LayoutLMvaModel(config=UpperCamelCase     )
						model.to(UpperCamelCase     )
						model.eval()

						# text + image
						__lowerCAmelCase												=  model(UpperCamelCase			,				pixel_values=UpperCamelCase     )
						__lowerCAmelCase												=  model(
						    UpperCamelCase			,				bbox=UpperCamelCase			,				pixel_values=UpperCamelCase			,				attention_mask=UpperCamelCase			,				token_type_ids=UpperCamelCase     )
						__lowerCAmelCase												=  model(UpperCamelCase			,				bbox=UpperCamelCase			,				pixel_values=UpperCamelCase			,				token_type_ids=UpperCamelCase     )
						__lowerCAmelCase												=  model(UpperCamelCase			,				bbox=UpperCamelCase			,				pixel_values=UpperCamelCase     )

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

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

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



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[Any]:
						__lowerCAmelCase												=  self.num_labels
						__lowerCAmelCase												=  LayoutLMvaForSequenceClassification(UpperCamelCase     )
						model.to(UpperCamelCase     )
						model.eval()
						__lowerCAmelCase												=  model(
						    UpperCamelCase			,				bbox=UpperCamelCase			,				pixel_values=UpperCamelCase			,				attention_mask=UpperCamelCase			,				token_type_ids=UpperCamelCase			,				labels=UpperCamelCase			,				)
						self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.num_labels
						__lowerCAmelCase												=  LayoutLMvaForTokenClassification(config=UpperCamelCase     )
						model.to(UpperCamelCase     )
						model.eval()
						__lowerCAmelCase												=  model(
						    UpperCamelCase			,				bbox=UpperCamelCase			,				pixel_values=UpperCamelCase			,				attention_mask=UpperCamelCase			,				token_type_ids=UpperCamelCase			,				labels=UpperCamelCase			,				)
						self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.text_seq_length, self.num_labels)     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Any:
						__lowerCAmelCase												=  LayoutLMvaForQuestionAnswering(config=UpperCamelCase     )
						model.to(UpperCamelCase     )
						model.eval()
						__lowerCAmelCase												=  model(
						    UpperCamelCase			,				bbox=UpperCamelCase			,				pixel_values=UpperCamelCase			,				attention_mask=UpperCamelCase			,				token_type_ids=UpperCamelCase			,				start_positions=UpperCamelCase			,				end_positions=UpperCamelCase			,				)
						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     )     ->			Tuple:
						__lowerCAmelCase												=  self.prepare_config_and_inputs()
						(
						    (
						    __lowerCAmelCase
						)							,       (
						    __lowerCAmelCase
						)							,       (
						    __lowerCAmelCase
						)							,       (
						    __lowerCAmelCase
						)							,       (
						    __lowerCAmelCase
						)							,       (
						    __lowerCAmelCase
						)							,       (
						    __lowerCAmelCase
						)							,       (
						    __lowerCAmelCase
						)							,       
						)												=  config_and_inputs
						__lowerCAmelCase												=  {
						    "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 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__							,       unittest.TestCase ):
					a      :     Dict        =							False
					a      :     List[Any]        =							False
					a      :     Optional[Any]        =							False

					a      :     Optional[int]        =							(
					    (
					        LayoutLMvaModel,
					        LayoutLMvaForSequenceClassification,
					        LayoutLMvaForTokenClassification,
					        LayoutLMvaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					a      :     List[str]        =							(
					    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
					    if is_torch_available()
					    else {}
					)



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Tuple:
						# `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        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  LayoutLMvaModelTester(self     )
						__lowerCAmelCase												=  ConfigTester(self			,				config_class=UpperCamelCase			,				hidden_size=37     )



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

						return inputs_dict



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						self.config_tester.run_common_tests()



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
							__lowerCAmelCase												=  type
							self.model_tester.create_and_check_model(*UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*UpperCamelCase     )



					@slow
					def        UpperCAmelCase_ (    self     )     ->			Any:
						for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase												=  LayoutLMvaModel.from_pretrained(UpperCamelCase     )
							self.assertIsNotNone(UpperCamelCase     )



def   __lowerCAmelCase					(							):






	'''simple docstring'''




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






@require_torch
class 					UpperCAmelCase__	(		unittest.TestCase ):



					@cached_property
					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase     ) if is_vision_available() else None



					@slow
					def        UpperCAmelCase_ (    self     )     ->			int:
						__lowerCAmelCase												=  LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"     ).to(UpperCamelCase     )

						__lowerCAmelCase												=  self.default_image_processor
						__lowerCAmelCase												=  prepare_img()
						__lowerCAmelCase												=  image_processor(images=UpperCamelCase			,				return_tensors="pt"     ).pixel_values.to(UpperCamelCase     )

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

						# forward pass
						__lowerCAmelCase												=  model(
						    input_ids=input_ids.to(UpperCamelCase     )			,				bbox=bbox.to(UpperCamelCase     )			,				pixel_values=pixel_values.to(UpperCamelCase     )			,				)

						# verify the logits
						__lowerCAmelCase												=  torch.Size((1, 199, 768)     )
						self.assertEqual(outputs.last_hidden_state.shape			,				UpperCamelCase     )

						__lowerCAmelCase												=  torch.tensor(
						    [[-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]]     ).to(UpperCamelCase     )

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




'''simple docstring'''



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


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

lowerCAmelCase	:		Dict[Optional[str], Type[Formatter]]				=   {}
lowerCAmelCase	:		Dict[Optional[str], str]				=   {}
lowerCAmelCase	:		Dict[Optional[str], Exception]				=   {}



def   __lowerCAmelCase					(							lowerCamelCase   :						type				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''					)
	__lowerCAmelCase												=  formatter_cls
	for alias in set(aliases + [format_type]					):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''					)
		__lowerCAmelCase												=  format_type



def   __lowerCAmelCase					(							lowerCamelCase   :						Exception				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None					):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	for alias in set(aliases + [format_type]					):
		__lowerCAmelCase												=  unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	lowerCAmelCase	:		Optional[int]				=   ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	lowerCAmelCase	:		str				=   ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	lowerCAmelCase	:		Any				=   ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]					):






	'''simple docstring'''




	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type




def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]				,		**lowerCamelCase   :						Tuple					):






	'''simple docstring'''




	__lowerCAmelCase												=  get_format_type_from_alias(lowerCamelCase					)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**lowerCamelCase					)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None					)}, but got \'{format_type}\''''					)