
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       _lowerCAmelCase   :




				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=1_000							,							)							-> Optional[Any]:
							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 	__a		(   self	)							-> Dict:
							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 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Optional[Any]:
							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 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> 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 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Tuple:
							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 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Optional[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 	__a		(   self	)							-> Optional[Any]:
							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       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_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 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> List[str]:
							# `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	)							-> Any:
							lowerCAmelCase_         =      LayoutLMvaModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							hidden_size=37	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase=False	)							-> List[Any]:
							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 	__a		(   self	)							-> Tuple:
							self.config_tester.run_common_tests()




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> Tuple:
							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 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase	)




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, 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       _lowerCAmelCase   (      unittest.TestCase			):




				@cached_property
				def 	__a		(   self	)							-> Dict:
							return LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase	) if is_vision_available() else None




				@slow
				def 	__a		(   self	)							-> Optional[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(
							    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	).to(_UpperCamelCase	)

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A  =	{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SEWForCTC",
			    "SEWForSequenceClassification",
			    "SEWModel",
			    "SEWPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sew import (
						    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SEWForCTC,
						    SEWForSequenceClassification,
						    SEWModel,
						    SEWPreTrainedModel,
						)


else:
			import sys

			_A  =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



