import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 (
          MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          MODEL_MAPPING,
          LevitForImageClassification,
          LevitForImageClassificationWithTeacher,
          LevitModel,
      )
      from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import LevitImageProcessor






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: str=   self.config_class(**self.inputs_dict )
     self.parent.assertTrue(hasattr(lowerCAmelCase			,			'''hidden_sizes''' ) )
     self.parent.assertTrue(hasattr(lowerCAmelCase			,			'''num_attention_heads''' ) )






class 		_lowerCamelCase		:
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=13			,			lowerCAmelCase=64			,			lowerCAmelCase=3			,			lowerCAmelCase=3			,			lowerCAmelCase=2			,			lowerCAmelCase=1			,			lowerCAmelCase=16			,			lowerCAmelCase=[128, 256, 384]			,			lowerCAmelCase=[4, 6, 8]			,			lowerCAmelCase=[2, 3, 4]			,			lowerCAmelCase=[16, 16, 16]			,			lowerCAmelCase=0			,			lowerCAmelCase=[2, 2, 2]			,			lowerCAmelCase=[2, 2, 2]			,			lowerCAmelCase=0.02			,			lowerCAmelCase=True			,			lowerCAmelCase=True			,			lowerCAmelCase=2			,			)							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   parent
     SCREAMING_SNAKE_CASE__: str=   batch_size
     SCREAMING_SNAKE_CASE__: int=   image_size
     SCREAMING_SNAKE_CASE__: Any=   num_channels
     SCREAMING_SNAKE_CASE__: Tuple=   kernel_size
     SCREAMING_SNAKE_CASE__: List[str]=   stride
     SCREAMING_SNAKE_CASE__: Tuple=   padding
     SCREAMING_SNAKE_CASE__: str=   hidden_sizes
     SCREAMING_SNAKE_CASE__: List[Any]=   num_attention_heads
     SCREAMING_SNAKE_CASE__: Any=   depths
     SCREAMING_SNAKE_CASE__: Tuple=   key_dim
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   drop_path_rate
     SCREAMING_SNAKE_CASE__: Optional[Any]=   patch_size
     SCREAMING_SNAKE_CASE__: List[str]=   attention_ratio
     SCREAMING_SNAKE_CASE__: List[str]=   mlp_ratio
     SCREAMING_SNAKE_CASE__: Dict=   initializer_range
     SCREAMING_SNAKE_CASE__: str=   [
         ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
         ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
     ]
     SCREAMING_SNAKE_CASE__: str=   is_training
     SCREAMING_SNAKE_CASE__: Optional[Any]=   use_labels
     SCREAMING_SNAKE_CASE__: str=   num_labels
     SCREAMING_SNAKE_CASE__: Optional[int]=   initializer_range
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: int=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     SCREAMING_SNAKE_CASE__: Optional[int]=   None
     if self.use_labels:
      SCREAMING_SNAKE_CASE__: int=   ids_tensor([self.batch_size]			,			self.num_labels )

     SCREAMING_SNAKE_CASE__: List[Any]=   self.get_config()
     return config, pixel_values, labels
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     return LevitConfig(
         image_size=self.image_size			,			num_channels=self.num_channels			,			kernel_size=self.kernel_size			,			stride=self.stride			,			padding=self.padding			,			patch_size=self.patch_size			,			hidden_sizes=self.hidden_sizes			,			num_attention_heads=self.num_attention_heads			,			depths=self.depths			,			key_dim=self.key_dim			,			drop_path_rate=self.drop_path_rate			,			mlp_ratio=self.mlp_ratio			,			attention_ratio=self.attention_ratio			,			initializer_range=self.initializer_range			,			down_ops=self.down_ops			,			)
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       str:
     SCREAMING_SNAKE_CASE__: List[str]=   LevitModel(config=lowerCAmelCase )
     model.to(lowerCAmelCase )
     model.eval()
     SCREAMING_SNAKE_CASE__: int=   model(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: List[Any]=   (self.image_size, self.image_size)
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Dict=   image_size[0], image_size[1]
     for _ in range(4 ):
      SCREAMING_SNAKE_CASE__: int=   floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
      SCREAMING_SNAKE_CASE__: Optional[int]=   floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
     self.parent.assertEqual(
         result.last_hidden_state.shape			,			(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1])			,			)
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       List[str]:
     SCREAMING_SNAKE_CASE__: str=   self.num_labels
     SCREAMING_SNAKE_CASE__: int=   LevitForImageClassification(lowerCAmelCase )
     model.to(lowerCAmelCase )
     model.eval()
     SCREAMING_SNAKE_CASE__: List[str]=   model(lowerCAmelCase			,			labels=lowerCAmelCase )
     self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels) )



    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: List[str]=   self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: str=   config_and_inputs
     SCREAMING_SNAKE_CASE__: List[Any]=   {'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   (
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    __a						=   (
        {
            "feature-extraction": LevitModel,
            "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: int=   LevitModelTester(self )
     SCREAMING_SNAKE_CASE__: Any=   ConfigTester(self			,			config_class=lowerCAmelCase			,			has_text_modality=lowerCAmelCase			,			hidden_size=37 )
    def     UpperCamelCase_					(	self )							->       int:
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     return
    @unittest.skip(reason='''Levit does not use inputs_embeds''' )
    def     UpperCamelCase_					(	self )							->       Tuple:
     pass
    @unittest.skip(reason='''Levit does not support input and output embeddings''' )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     pass
    @unittest.skip(reason='''Levit does not output attentions''' )
    def     UpperCamelCase_					(	self )							->       int:
     pass
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Dict=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: Any=   model_class(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Tuple=   inspect.signature(model.forward )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   [*signature.parameters.keys()]

      SCREAMING_SNAKE_CASE__: Union[str, Any]=   ['''pixel_values''']
      self.assertListEqual(arg_names[:1]			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     def check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Optional[int]=   model_class(lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.eval()

      with torch.no_grad():
       SCREAMING_SNAKE_CASE__: Tuple=   model(**self._prepare_for_class(lowerCAmelCase			,			lowerCAmelCase ) )

      SCREAMING_SNAKE_CASE__: Optional[int]=   outputs.hidden_states

      SCREAMING_SNAKE_CASE__: List[str]=   len(self.model_tester.depths ) + 1
      self.assertEqual(len(lowerCAmelCase )			,			lowerCAmelCase )

      SCREAMING_SNAKE_CASE__: Optional[int]=   (self.model_tester.image_size, self.model_tester.image_size)
      SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: int=   image_size[0], image_size[1]
      for _ in range(4 ):
       SCREAMING_SNAKE_CASE__: str=   floor(
           (
               (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
               / self.model_tester.stride
           )
           + 1 )
       SCREAMING_SNAKE_CASE__: Dict=   floor(
           (
               (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
               / self.model_tester.stride
           )
           + 1 )
      # verify the first hidden states (first block)
      self.assertListEqual(
          list(hidden_states[0].shape[-2:] )			,			[
              height * width,
              self.model_tester.hidden_sizes[0],
          ]			,			)

     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Any=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: str=   True
      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      SCREAMING_SNAKE_CASE__: List[str]=   True

      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )
    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
    def     UpperCamelCase_					(	self )							->       Any:
     pass
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase=False )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Tuple=   super()._prepare_for_class(lowerCAmelCase			,			lowerCAmelCase			,			return_labels=lowerCAmelCase )

     if return_labels:
      if model_class.__name__ == "LevitForImageClassificationWithTeacher":
       del inputs_dict["labels"]

     return inputs_dict
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     if not self.model_tester.is_training:
      return

     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: List[str]=   self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE__: int=   True

     for model_class in self.all_model_classes:
      # LevitForImageClassificationWithTeacher supports inference-only
      if (
          model_class in get_values(lowerCAmelCase )
          or model_class.__name__ == "LevitForImageClassificationWithTeacher"
      ):
       continue
      SCREAMING_SNAKE_CASE__: Optional[int]=   model_class(lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.train()
      SCREAMING_SNAKE_CASE__: Optional[int]=   self._prepare_for_class(lowerCAmelCase			,			lowerCAmelCase			,			return_labels=lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   model(**lowerCAmelCase ).loss
      loss.backward()
    def     UpperCamelCase_					(	self )							->       str:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Tuple=   self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
      return

     SCREAMING_SNAKE_CASE__: int=   False
     SCREAMING_SNAKE_CASE__: Dict=   True

     for model_class in self.all_model_classes:
      if model_class in get_values(lowerCAmelCase ) or not model_class.supports_gradient_checkpointing:
       continue
      # LevitForImageClassificationWithTeacher supports inference-only
      if model_class.__name__ == "LevitForImageClassificationWithTeacher":
       continue
      SCREAMING_SNAKE_CASE__: List[str]=   model_class(lowerCAmelCase )
      model.gradient_checkpointing_enable()
      model.to(lowerCAmelCase )
      model.train()
      SCREAMING_SNAKE_CASE__: Optional[int]=   self._prepare_for_class(lowerCAmelCase			,			lowerCAmelCase			,			return_labels=lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: int=   model(**lowerCAmelCase ).loss
      loss.backward()
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Tuple=   self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__: Optional[Any]=   [
         {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
         {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
         {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
     ]

     for model_class in self.all_model_classes:
      if (
          model_class
          not in [
              *get_values(lowerCAmelCase ),
          ]
          or model_class.__name__ == "LevitForImageClassificationWithTeacher"
      ):
       continue

      for problem_type in problem_types:
       with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}' ):
        SCREAMING_SNAKE_CASE__: str=   problem_type['''title''']
        SCREAMING_SNAKE_CASE__: Optional[int]=   problem_type['''num_labels''']

        SCREAMING_SNAKE_CASE__: Optional[int]=   model_class(lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.train()

        SCREAMING_SNAKE_CASE__: Union[str, Any]=   self._prepare_for_class(lowerCAmelCase			,			lowerCAmelCase			,			return_labels=lowerCAmelCase )

        if problem_type["num_labels"] > 1:
         SCREAMING_SNAKE_CASE__: str=   inputs['''labels'''].unsqueeze(1 ).repeat(1			,			problem_type['''num_labels'''] )

        SCREAMING_SNAKE_CASE__: Optional[Any]=   inputs['''labels'''].to(problem_type['''dtype'''] )

        # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
        # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
        # they have the same size." which is a symptom something in wrong for the regression problem.
        # See https://github.com/huggingface/transformers/issues/11780
        with warnings.catch_warnings(record=lowerCAmelCase ) as warning_list:
         SCREAMING_SNAKE_CASE__: Any=   model(**lowerCAmelCase ).loss
        for w in warning_list:
         if "Using a target size that is different to the input size" in str(w.message ):
          raise ValueError(
              f'Something is going wrong in the regression problem: intercepted {w.message}' )

        loss.backward()



    @slow
    def     UpperCamelCase_					(	self )							->       List[str]:
     for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__: str=   LevitModel.from_pretrained(lowerCAmelCase )
      self.assertIsNotNone(lowerCAmelCase )







def       A__   (						):
 SCREAMING_SNAKE_CASE__: Tuple=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
 return image






@require_torch
@require_vision
class 		_lowerCamelCase		( unittest.TestCase     ):
    @cached_property
    def     UpperCamelCase_					(	self )							->       Dict:
     return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )



    @slow
    def     UpperCamelCase_					(	self )							->       Any:
     SCREAMING_SNAKE_CASE__: str=   LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
         lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[str]=   self.default_image_processor
     SCREAMING_SNAKE_CASE__: Any=   prepare_img()
     SCREAMING_SNAKE_CASE__: Optional[Any]=   image_processor(images=lowerCAmelCase			,			return_tensors='''pt''' ).to(lowerCAmelCase )

     # forward pass
     with torch.no_grad():
      SCREAMING_SNAKE_CASE__: List[str]=   model(**lowerCAmelCase )

     # verify the logits
     SCREAMING_SNAKE_CASE__: List[str]=   torch.Size((1, 1000) )
     self.assertEqual(outputs.logits.shape			,			lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Any=   torch.tensor([1.0448, -0.3745, -1.8317] ).to(lowerCAmelCase )

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





'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_pytesseract_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor






class   __snake_case			(  unittest.TestCase):







    """simple docstring"""



    def __init__(  self       :    Union[str, Any]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Union[str, Any]=7       ,		lowerCamelCase       :    Any=3       ,		lowerCamelCase       :    Tuple=18       ,		lowerCamelCase       :    List[Any]=30       ,		lowerCamelCase       :    str=4_00       ,		lowerCamelCase       :    List[str]=True       ,		lowerCamelCase       :    List[Any]=None       ,		lowerCamelCase       :    Any=True       ,		)  ->    Any:
      lowerCAmelCase_     : Optional[int]     =  size if size is not None else {"""height""": 18, """width""": 18}
      lowerCAmelCase_     : Dict     =  parent
      lowerCAmelCase_     : Tuple     =  batch_size
      lowerCAmelCase_     : int     =  num_channels
      lowerCAmelCase_     : List[str]     =  image_size
      lowerCAmelCase_     : Optional[int]     =  min_resolution
      lowerCAmelCase_     : List[str]     =  max_resolution
      lowerCAmelCase_     : List[str]     =  do_resize
      lowerCAmelCase_     : str     =  size
      lowerCAmelCase_     : int     =  apply_ocr





    def __lowercase  (  self       :    Optional[Any]						)  ->    List[Any]:
      return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class   __snake_case			(  _SCREAMING_SNAKE_CASE      ,unittest.TestCase):







    """simple docstring"""



    lowercase  					=   LayoutLMvaImageProcessor if is_pytesseract_available() else None

    def __lowercase  (  self       :    List[Any]						)  ->    Dict:
      lowerCAmelCase_     : Union[str, Any]     =  LayoutLMvaImageProcessingTester(self						)

    @property
    def __lowercase  (  self       :    Optional[Any]						)  ->    Optional[Any]:
      return self.image_processor_tester.prepare_image_processor_dict()

    def __lowercase  (  self       :    Optional[Any]						)  ->    Dict:
      lowerCAmelCase_     : Dict     =  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(lowerCamelCase       ,		"""do_resize"""						)						)
      self.assertTrue(hasattr(lowerCamelCase       ,		"""size"""						)						)
      self.assertTrue(hasattr(lowerCamelCase       ,		"""apply_ocr"""						)						)

    def __lowercase  (  self       :    List[str]						)  ->    int:
      lowerCAmelCase_     : Dict     =  self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size       ,		{"""height""": 18, """width""": 18}						)

      lowerCAmelCase_     : Tuple     =  self.image_processing_class.from_dict(self.image_processor_dict       ,		size=42						)
      self.assertEqual(image_processor.size       ,		{"""height""": 42, """width""": 42}						)

    def __lowercase  (  self       :    str						)  ->    Dict:
      pass

    def __lowercase  (  self       :    str						)  ->    Union[str, Any]:
      # Initialize image_processing
      lowerCAmelCase_     : Union[str, Any]     =  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      lowerCAmelCase_     : Tuple     =  prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=lowerCamelCase						)
      for image in image_inputs:
        self.assertIsInstance(lowerCamelCase       ,		Image.Image						)

      # Test not batched input
      lowerCAmelCase_     : List[str]     =  image_processing(image_inputs[0]       ,		return_tensors="""pt"""						)
      self.assertEqual(
          encoding.pixel_values.shape       ,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )       ,		)

      self.assertIsInstance(encoding.words       ,		lowerCamelCase						)
      self.assertIsInstance(encoding.boxes       ,		lowerCamelCase						)

      # Test batched
      lowerCAmelCase_     : Union[str, Any]     =  image_processing(lowerCamelCase       ,		return_tensors="""pt"""						).pixel_values
      self.assertEqual(
          encoded_images.shape       ,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )       ,		)

    def __lowercase  (  self       :    Any						)  ->    Any:
      # Initialize image_processing
      lowerCAmelCase_     : Any     =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      lowerCAmelCase_     : List[Any]     =  prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=lowerCamelCase       ,		numpify=lowerCamelCase						)
      for image in image_inputs:
        self.assertIsInstance(lowerCamelCase       ,		np.ndarray						)

      # Test not batched input
      lowerCAmelCase_     : List[Any]     =  image_processing(image_inputs[0]       ,		return_tensors="""pt"""						).pixel_values
      self.assertEqual(
          encoded_images.shape       ,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )       ,		)

      # Test batched
      lowerCAmelCase_     : Optional[int]     =  image_processing(lowerCamelCase       ,		return_tensors="""pt"""						).pixel_values
      self.assertEqual(
          encoded_images.shape       ,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )       ,		)

    def __lowercase  (  self       :    List[str]						)  ->    int:
      # Initialize image_processing
      lowerCAmelCase_     : Dict     =  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      lowerCAmelCase_     : Union[str, Any]     =  prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=lowerCamelCase       ,		torchify=lowerCamelCase						)
      for image in image_inputs:
        self.assertIsInstance(lowerCamelCase       ,		torch.Tensor						)

      # Test not batched input
      lowerCAmelCase_     : Optional[int]     =  image_processing(image_inputs[0]       ,		return_tensors="""pt"""						).pixel_values
      self.assertEqual(
          encoded_images.shape       ,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )       ,		)

      # Test batched
      lowerCAmelCase_     : Tuple     =  image_processing(lowerCamelCase       ,		return_tensors="""pt"""						).pixel_values
      self.assertEqual(
          encoded_images.shape       ,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )       ,		)





    def __lowercase  (  self       :    int						)  ->    Optional[Any]:
      # with apply_OCR = True
      lowerCAmelCase_     : Optional[int]     =  LayoutLMvaImageProcessor()

      from datasets import load_dataset

      lowerCAmelCase_     : int     =  load_dataset("""hf-internal-testing/fixtures_docvqa"""       ,		split="""test"""						)

      lowerCAmelCase_     : Any     =  Image.open(ds[0]["""file"""]						).convert("""RGB"""						)

      lowerCAmelCase_     : Dict     =  image_processing(lowerCamelCase       ,		return_tensors="""pt"""						)

      self.assertEqual(encoding.pixel_values.shape       ,		(1, 3, 2_24, 2_24)						)
      self.assertEqual(len(encoding.words						)       ,		len(encoding.boxes						)						)

      # fmt: off
      # the words and boxes were obtained with Tesseract 4.1.1
      lowerCAmelCase_     : Union[str, Any]     =  [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
      lowerCAmelCase_     : Any     =  [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
      # fmt: on

      self.assertListEqual(encoding.words       ,		lowerCamelCase						)
      self.assertListEqual(encoding.boxes       ,		lowerCamelCase						)

      # with apply_OCR = False
      lowerCAmelCase_     : Dict     =  LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  image_processing(lowerCamelCase       ,		return_tensors="""pt"""						)

      self.assertEqual(encoding.pixel_values.shape       ,		(1, 3, 2_24, 2_24)						)



