






'''simple docstring'''

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class        a_		(    __lowerCamelCase       ):
    lowercase  	=   ["""image_processor""", """tokenizer"""]
    lowercase  	=   """LayoutLMv3ImageProcessor"""
    lowercase  	=   ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")

    def __init__(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	**_SCREAMING_SNAKE_CASE   )						->   int:



          """simple docstring"""






          UpperCamelCase       						=      None
          if "feature_extractor" in kwargs:
                warnings.warn(
                    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                    """ instead."""    ,	UpperCAmelCase_    ,	)
                UpperCamelCase       						=      kwargs.pop("""feature_extractor"""   )

          UpperCamelCase       						=      image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
                raise ValueError("""You need to specify an `image_processor`."""   )
          if tokenizer is None:
                raise ValueError("""You need to specify a `tokenizer`."""   )

          super().__init__(UpperCAmelCase_    ,	UpperCAmelCase_   )

    def __call__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 0    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[str]:



          """simple docstring"""






          if self.image_processor.apply_ocr and (boxes is not None):
                raise ValueError(
                    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""   )

          if self.image_processor.apply_ocr and (word_labels is not None):
                raise ValueError(
                    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

          # first, apply the image processor
          UpperCamelCase       						=      self.image_processor(images=UpperCAmelCase_    ,	return_tensors=UpperCAmelCase_   )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
                if isinstance(UpperCAmelCase_    ,	UpperCAmelCase_   ):
                      UpperCamelCase       						=      [text]  # add batch dimension (as the image processor always adds a batch dimension)
                UpperCamelCase       						=      features['words']

          UpperCamelCase       						=      self.tokenizer(
              text=text if text is not None else features["""words"""]    ,	text_pair=text_pair if text_pair is not None else None    ,	boxes=boxes if boxes is not None else features["""boxes"""]    ,	word_labels=UpperCAmelCase_    ,	add_special_tokens=UpperCAmelCase_    ,	padding=UpperCAmelCase_    ,	truncation=UpperCAmelCase_    ,	max_length=UpperCAmelCase_    ,	stride=UpperCAmelCase_    ,	pad_to_multiple_of=UpperCAmelCase_    ,	return_token_type_ids=UpperCAmelCase_    ,	return_attention_mask=UpperCAmelCase_    ,	return_overflowing_tokens=UpperCAmelCase_    ,	return_special_tokens_mask=UpperCAmelCase_    ,	return_offsets_mapping=UpperCAmelCase_    ,	return_length=UpperCAmelCase_    ,	verbose=UpperCAmelCase_    ,	return_tensors=UpperCAmelCase_    ,	**UpperCAmelCase_    ,	)

          # add pixel values
          UpperCamelCase       						=      features.pop("""pixel_values"""   )
          if return_overflowing_tokens is True:
                UpperCamelCase       						=      self.get_overflowing_images(UpperCAmelCase_    ,	encoded_inputs["""overflow_to_sample_mapping"""]   )
          UpperCamelCase       						=      images

          return encoded_inputs

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



          """simple docstring"""






          UpperCamelCase       						=      []
          for sample_idx in overflow_to_sample_mapping:
                images_with_overflow.append(images[sample_idx]   )

          if len(UpperCAmelCase_   ) != len(UpperCAmelCase_   ):
                raise ValueError(
                    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
                    F" {len(UpperCAmelCase_   )} and {len(UpperCAmelCase_   )}"   )

          return images_with_overflow

    def 						A__		(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



          """simple docstring"""






          return self.tokenizer.batch_decode(*UpperCAmelCase_    ,	**UpperCAmelCase_   )

    def 						A__		(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



          """simple docstring"""






          return self.tokenizer.decode(*UpperCAmelCase_    ,	**UpperCAmelCase_   )

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



          """simple docstring"""






          return ["input_ids", "bbox", "attention_mask", "pixel_values"]

    @property
    def 						A__		(   self   )						->   Any:



          """simple docstring"""






          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,	UpperCAmelCase_    ,	)
          return self.image_processor_class


    @property
    def 						A__		(   self   )						->   Dict:



          """simple docstring"""






          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,	UpperCAmelCase_    ,	)
          return self.image_processor








'''simple docstring'''

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, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor






class        a_		(    lowerCamelCase       ):

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.config_class(**self.inputs_dict   )
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""tf_padding"""   )   )
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""depth_multiplier"""   )   )






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=13    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE=0.2_5    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE="relu6"    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=10    ,	_SCREAMING_SNAKE_CASE=None    ,	)						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      image_size
        UpperCamelCase       						=      depth_multiplier
        UpperCamelCase       						=      min_depth
        UpperCamelCase       						=      tf_padding
        UpperCamelCase       						=      int(last_hidden_size * depth_multiplier   )
        UpperCamelCase       						=      output_stride
        UpperCamelCase       						=      hidden_act
        UpperCamelCase       						=      classifier_dropout_prob
        UpperCamelCase       						=      use_labels
        UpperCamelCase       						=      is_training
        UpperCamelCase       						=      num_labels
        UpperCamelCase       						=      initializer_range
        UpperCamelCase       						=      scope

  def 						A__		(   self   )						->   int:



        """simple docstring"""






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

        UpperCamelCase       						=      None
        UpperCamelCase       						=      None
        if self.use_labels:
              UpperCamelCase       						=      ids_tensor([self.batch_size]    ,	self.num_labels   )
              UpperCamelCase       						=      ids_tensor([self.batch_size, self.image_size, self.image_size]    ,	self.num_labels   )

        UpperCamelCase       						=      self.get_config()

        return config, pixel_values, labels, pixel_labels

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



        """simple docstring"""






        return MobileNetVaConfig(
            num_channels=self.num_channels    ,	image_size=self.image_size    ,	depth_multiplier=self.depth_multiplier    ,	min_depth=self.min_depth    ,	tf_padding=self.tf_padding    ,	hidden_act=self.hidden_act    ,	classifier_dropout_prob=self.classifier_dropout_prob    ,	initializer_range=self.initializer_range    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        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,
            )    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.num_labels
        UpperCamelCase       						=      MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)   )


  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.prepare_config_and_inputs()
        UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      config_and_inputs
        UpperCamelCase       						=      {"""pixel_values""": pixel_values}
        return config, inputs_dict






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
  lowercase  	=   (
      {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
      if is_torch_available()
      else {}
  )

  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaModelTester(self   )
        UpperCamelCase       						=      MobileNetVaConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	has_text_modality=_SCREAMING_SNAKE_CASE   )

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



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not output attentions"""   )
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        pass

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



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCamelCase       						=      ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        def check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              model.to(_SCREAMING_SNAKE_CASE   )
              model.eval()

              with torch.no_grad():
                    UpperCamelCase       						=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )   )

              UpperCamelCase       						=      outputs.hidden_states

              UpperCamelCase       						=      26
              self.assertEqual(len(_SCREAMING_SNAKE_CASE   )    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCamelCase       						=      True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )


  @slow
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase       						=      MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def 					lowercase__				(   )->				Optional[Any]:
      UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image






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

  @cached_property
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        return (
            MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""   ) if is_vision_available() else None
        )


  @slow
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""   ).to(_SCREAMING_SNAKE_CASE   )

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

        # forward pass
        with torch.no_grad():
              UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE   )

        # verify the logits
        UpperCamelCase       						=      torch.Size((1, 1001)   )
        self.assertEqual(outputs.logits.shape    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   ).to(_SCREAMING_SNAKE_CASE   )

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