






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


lowerCamelCase : Optional[int]						   =       logging.get_logger(__name__)

lowerCamelCase : Tuple						   =       {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			int									=      '''layoutlmv3'''



					def __init__(  self				:	Dict		,     __a				:	List[str]=50265		,     __a				:	str=768		,     __a				:	List[Any]=12		,     __a				:	List[Any]=12		,     __a				:	List[str]=3072		,     __a				:	Optional[Any]="gelu"		,     __a				:	Optional[int]=0.1		,     __a				:	List[Any]=0.1		,     __a				:	Tuple=512		,     __a				:	int=2		,     __a				:	Any=0.02		,     __a				:	Union[str, Any]=1E-5		,     __a				:	List[str]=1		,     __a				:	List[Any]=0		,     __a				:	int=2		,     __a				:	str=1024		,     __a				:	str=128		,     __a				:	List[Any]=128		,     __a				:	Tuple=True		,     __a				:	Optional[int]=32		,     __a				:	Any=128		,     __a				:	List[Any]=64		,     __a				:	Tuple=256		,     __a				:	str=True		,     __a				:	int=True		,     __a				:	Optional[Any]=True		,     __a				:	Any=224		,     __a				:	str=3		,     __a				:	List[str]=16		,     __a				:	Union[str, Any]=None		,     **__a				:	List[Any]		,     )			->  List[str]:


												"""simple docstring"""

												super().__init__(
												    vocab_size=__a		,     hidden_size=__a		,     num_hidden_layers=__a		,     num_attention_heads=__a		,     intermediate_size=__a		,     hidden_act=__a		,     hidden_dropout_prob=__a		,     attention_probs_dropout_prob=__a		,     max_position_embeddings=__a		,     type_vocab_size=__a		,     initializer_range=__a		,     layer_norm_eps=__a		,     pad_token_id=__a		,     bos_token_id=__a		,     eos_token_id=__a		,     **__a		,     )
												__lowercase      :							int				=     max_ad_position_embeddings
												__lowercase      :							Any				=     coordinate_size
												__lowercase      :							Optional[Any]				=     shape_size
												__lowercase      :							str				=     has_relative_attention_bias
												__lowercase      :							int				=     rel_pos_bins
												__lowercase      :							Union[str, Any]				=     max_rel_pos
												__lowercase      :							str				=     has_spatial_attention_bias
												__lowercase      :							str				=     rel_ad_pos_bins
												__lowercase      :							List[Any]				=     max_rel_ad_pos
												__lowercase      :							Tuple				=     text_embed
												__lowercase      :							int				=     visual_embed
												__lowercase      :							Tuple				=     input_size
												__lowercase      :							Dict				=     num_channels
												__lowercase      :							str				=     patch_size
												__lowercase      :							Optional[int]				=     classifier_dropout







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			str									=      version.parse('''1.12'''			)



					@property
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Mapping[str, Mapping[int, str]]:


												"""simple docstring"""

												if self.task in ["question-answering", "sequence-classification"]:
																			return OrderedDict(
																			    [
																			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
																			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
																			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
																			        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
																			    ]							)
												else:
																			return OrderedDict(
																			    [
																			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
																			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
																			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
																			        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
																			    ]							)



					@property
					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  float:


												"""simple docstring"""

												return 1E-5



					@property
					def       lowerCAmelCase				(  self				:	str							)			->  int:


												"""simple docstring"""

												return 12



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	"ProcessorMixin"		,     __a				:	int = -1		,     __a				:	int = -1		,     __a				:	bool = False		,     __a				:	Optional["TensorType"] = None		,     __a				:	int = 3		,     __a				:	int = 40		,     __a				:	int = 40		,     )			->  Mapping[str, Any]:


												"""simple docstring"""

												setattr(processor.image_processor		,     """apply_ocr"""		,     __a							)

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												__lowercase      :							Tuple				=     compute_effective_axis_dimension(
												    __a		,     fixed_dimension=OnnxConfig.default_fixed_batch		,     num_token_to_add=0							)
												# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
												__lowercase      :							Tuple				=     processor.tokenizer.num_special_tokens_to_add(__a							)
												__lowercase      :							Tuple				=     compute_effective_axis_dimension(
												    __a		,     fixed_dimension=OnnxConfig.default_fixed_sequence		,     num_token_to_add=__a							)
												# Generate dummy inputs according to compute batch and sequence
												__lowercase      :							Union[str, Any]				=     [[""" """.join([processor.tokenizer.unk_token]							) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												__lowercase      :							Tuple				=     [[[48, 84, 73, 128]]] * batch_size

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
												__lowercase      :							Tuple				=     self._generate_dummy_images(__a		,     __a		,     __a		,     __a							)

												__lowercase      :							int				=     dict(
												    processor(
												        __a		,     text=__a		,     boxes=__a		,     return_tensors=__a		,     )							)

												return inputs







import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
	from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import MobileNetVaImageProcessor

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def       lowerCAmelCase				(  self				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							str				=     self.config_class(**self.inputs_dict							)
												self.parent.assertTrue(hasattr(__a		,     """tf_padding"""							)							)
												self.parent.assertTrue(hasattr(__a		,     """depth_multiplier"""							)							)







class 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	Union[str, Any]		,     __a				:	Tuple		,     __a				:	str=13		,     __a				:	Dict=3		,     __a				:	List[Any]=32		,     __a				:	Any=0.25		,     __a				:	Any=8		,     __a				:	Optional[int]=8		,     __a				:	Optional[int]=6		,     __a				:	Dict=32		,     __a				:	Tuple=True		,     __a				:	List[Any]=True		,     __a				:	Optional[int]=True		,     __a				:	Tuple="relu6"		,     __a				:	Optional[Any]=1280		,     __a				:	str=0.1		,     __a				:	str=0.02		,     __a				:	Optional[Any]=True		,     __a				:	Tuple=True		,     __a				:	Dict=10		,     __a				:	Optional[Any]=None		,     )			->  Any:


												"""simple docstring"""

												__lowercase      :							List[str]				=     parent
												__lowercase      :							Tuple				=     batch_size
												__lowercase      :							Dict				=     num_channels
												__lowercase      :							Optional[int]				=     image_size
												__lowercase      :							int				=     depth_multiplier
												__lowercase      :							str				=     depth_divisible_by
												__lowercase      :							int				=     min_depth
												__lowercase      :							Tuple				=     expand_ratio
												__lowercase      :							Optional[int]				=     tf_padding
												__lowercase      :							Dict				=     output_stride
												__lowercase      :							Dict				=     first_layer_is_expansion
												__lowercase      :							Optional[Any]				=     finegrained_output
												__lowercase      :							str				=     hidden_act
												__lowercase      :							Union[str, Any]				=     last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier							)
												__lowercase      :							Optional[int]				=     classifier_dropout_prob
												__lowercase      :							int				=     use_labels
												__lowercase      :							Optional[int]				=     is_training
												__lowercase      :							Dict				=     num_labels
												__lowercase      :							Tuple				=     initializer_range
												__lowercase      :							Optional[Any]				=     scope



					def       lowerCAmelCase				(  self				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Tuple				=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

												__lowercase      :							List[Any]				=     None
												__lowercase      :							Optional[Any]				=     None
												if self.use_labels:
																			__lowercase      :							List[Any]				=     ids_tensor([self.batch_size]		,     self.num_labels							)
																			__lowercase      :							Optional[int]				=     ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels							)

												__lowercase      :							List[Any]				=     self.get_config()

												return config, pixel_values, labels, pixel_labels



					def       lowerCAmelCase				(  self				:	str							)			->  Union[str, Any]:


												"""simple docstring"""

												return MobileNetVaConfig(
												    num_channels=self.num_channels		,     image_size=self.image_size		,     depth_multiplier=self.depth_multiplier		,     depth_divisible_by=self.depth_divisible_by		,     min_depth=self.min_depth		,     expand_ratio=self.expand_ratio		,     output_stride=self.output_stride		,     first_layer_is_expansion=self.first_layer_is_expansion		,     finegrained_output=self.finegrained_output		,     hidden_act=self.hidden_act		,     tf_padding=self.tf_padding		,     classifier_dropout_prob=self.classifier_dropout_prob		,     initializer_range=self.initializer_range		,     )



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	Dict		,     __a				:	Tuple		,     __a				:	Optional[int]		,     __a				:	Union[str, Any]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     MobileNetVaModel(config=__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Tuple				=     model(__a							)
												self.parent.assertEqual(
												    result.last_hidden_state.shape		,     (
												        self.batch_size,
												        self.last_hidden_size,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )		,     )
												self.parent.assertEqual(
												    result.pooler_output.shape		,     (self.batch_size, self.last_hidden_size)		,     )



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Optional[int]		,     __a				:	List[str]		,     __a				:	str		,     __a				:	Optional[int]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.num_labels
												__lowercase      :							Dict				=     MobileNetVaForImageClassification(__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Dict				=     model(__a		,     labels=__a							)
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)							)



					def       lowerCAmelCase				(  self				:	int		,     __a				:	List[str]		,     __a				:	Tuple		,     __a				:	Any		,     __a				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							int				=     self.num_labels
												__lowercase      :							List[Any]				=     MobileNetVaForSemanticSegmentation(__a							)
												model.to(__a							)
												model.eval()
												__lowercase      :							Dict				=     model(__a							)
												self.parent.assertEqual(
												    result.logits.shape		,     (
												        self.batch_size,
												        self.num_labels,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )		,     )
												__lowercase      :							str				=     model(__a		,     labels=__a							)
												self.parent.assertEqual(
												    result.logits.shape		,     (
												        self.batch_size,
												        self.num_labels,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )		,     )



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     self.prepare_config_and_inputs()
												__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							List[str]				=     config_and_inputs
												__lowercase      :							List[Any]				=     {"""pixel_values""": pixel_values}
												return config, inputs_dict







@require_torch
class 		lowerCAmelCase     ( __a      ,       __a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			Tuple									=      (
					    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					_A						:			Optional[Any]									=      (
					    {
					        '''feature-extraction''': MobileNetVaModel,
					        '''image-classification''': MobileNetVaForImageClassification,
					        '''image-segmentation''': MobileNetVaForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					_A						:			Tuple									=      False
					_A						:			List[str]									=      False
					_A						:			List[str]									=      False
					_A						:			Optional[int]									=      False



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     MobileNetVaModelTester(self							)
												__lowercase      :							int				=     MobileNetVaConfigTester(self		,     config_class=__a		,     has_text_modality=__a							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  List[Any]:


												"""simple docstring"""

												self.config_tester.run_common_tests()



					@unittest.skip(reason="""MobileNetV2 does not use inputs_embeds"""							)
					def       lowerCAmelCase				(  self				:	List[str]							)			->  Union[str, Any]:


												"""simple docstring"""

												pass



					@unittest.skip(reason="""MobileNetV2 does not support input and output embeddings"""							)
					def       lowerCAmelCase				(  self				:	Any							)			->  Tuple:


												"""simple docstring"""

												pass



					@unittest.skip(reason="""MobileNetV2 does not output attentions"""							)
					def       lowerCAmelCase				(  self				:	List[str]							)			->  int:


												"""simple docstring"""

												pass



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Dict:


												"""simple docstring"""

												__lowercase			,			__lowercase      :							Optional[int]				=     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__lowercase      :							List[Any]				=     model_class(__a							)
																			__lowercase      :							Any				=     inspect.signature(model.forward							)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__lowercase      :							int				=     [*signature.parameters.keys()]

																			__lowercase      :							Any				=     ["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]		,     __a							)



					def       lowerCAmelCase				(  self				:	Dict							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__a							)



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Tuple:


												"""simple docstring"""

												def check_hidden_states_output(__a				:	List[Any]		,     __a				:	Tuple		,     __a				:	List[str]							):
																			__lowercase      :							Optional[Any]				=     model_class(__a							)
																			model.to(__a							)
																			model.eval()

																			with torch.no_grad():
																										__lowercase      :							List[Any]				=     model(**self._prepare_for_class(__a		,     __a							)							)

																			__lowercase      :							Tuple				=     outputs.hidden_states

																			__lowercase      :							str				=     16
																			self.assertEqual(len(__a							)		,     __a							)

												__lowercase			,			__lowercase      :							int				=     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__lowercase      :							Any				=     True
																			check_hidden_states_output(__a		,     __a		,     __a							)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			__lowercase      :							Union[str, Any]				=     True

																			check_hidden_states_output(__a		,     __a		,     __a							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Dict				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__a							)



					def       lowerCAmelCase				(  self				:	List[str]							)			->  int:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*__a							)



					@slow
					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Optional[Any]:


												"""simple docstring"""

												for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__lowercase      :							Optional[int]				=     MobileNetVaModel.from_pretrained(__a							)
																			self.assertIsNotNone(__a							)







def       snake_case_		(     ):
							__lowercase      :							List[Any]				=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_torch
@require_vision
class 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

					@cached_property
					def       lowerCAmelCase				(  self				:	Any							)			->  Union[str, Any]:


												"""simple docstring"""

												return (
												    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224"""							) if is_vision_available() else None
												)



					@slow
					def       lowerCAmelCase				(  self				:	str							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224"""							).to(__a							)

												__lowercase      :							str				=     self.default_image_processor
												__lowercase      :							Tuple				=     prepare_img()
												__lowercase      :							Tuple				=     image_processor(images=__a		,     return_tensors="""pt"""							).to(__a							)

												# forward pass
												with torch.no_grad():
																			__lowercase      :							str				=     model(**__a							)

												# verify the logits
												__lowercase      :							Union[str, Any]				=     torch.Size((1, 1001)							)
												self.assertEqual(outputs.logits.shape		,     __a							)

												__lowercase      :							str				=     torch.tensor([0.2445, -1.1993, 0.1905]							).to(__a							)

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



					@slow
					def       lowerCAmelCase				(  self				:	Tuple							)			->  Any:


												"""simple docstring"""

												__lowercase      :							int				=     MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""							)
												__lowercase      :							Dict				=     model.to(__a							)

												__lowercase      :							Tuple				=     MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""							)

												__lowercase      :							List[str]				=     prepare_img()
												__lowercase      :							Optional[int]				=     image_processor(images=__a		,     return_tensors="""pt"""							).to(__a							)

												# forward pass
												with torch.no_grad():
																			__lowercase      :							Union[str, Any]				=     model(**__a							)
												__lowercase      :							Any				=     outputs.logits

												# verify the logits
												__lowercase      :							Dict				=     torch.Size((1, 21, 65, 65)							)
												self.assertEqual(logits.shape		,     __a							)

												__lowercase      :							str				=     torch.tensor(
												    [
												        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
												        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
												        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
												    ]		,     device=__a		,     )

												self.assertTrue(torch.allclose(logits[0, :3, :3, :3]		,     __a		,     atol=1E-4							)							)