


'''simple docstring'''

import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
 from transformers.models.mobilevitva.modeling_mobilevitva import (
     MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
     make_divisible,
 )


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor
class     __lowerCAmelCase		(A__	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.config_class(**self.inputs_dict					)
             self.parent.assertTrue(hasattr(snake_case_  ,		'''width_multiplier'''					)					)






class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Optional[int]  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       Tuple=13  ,		UpperCamelCase       :       Optional[Any]=64  ,		UpperCamelCase       :       int=2  ,		UpperCamelCase       :       Union[str, Any]=3  ,		UpperCamelCase       :       Any="swish"  ,		UpperCamelCase       :       Union[str, Any]=3  ,		UpperCamelCase       :       Optional[Any]=32  ,		UpperCamelCase       :       str=0.1  ,		UpperCamelCase       :       Union[str, Any]=0.02  ,		UpperCamelCase       :       str=True  ,		UpperCamelCase       :       List[Any]=True  ,		UpperCamelCase       :       List[Any]=10  ,		UpperCamelCase       :       Optional[int]=None  ,		UpperCamelCase       :       List[str]=0.25  ,		UpperCamelCase       :       Optional[int]=0.0  ,		UpperCamelCase       :       Union[str, Any]=0.0  ,		):

             '''simple docstring'''







             lowercase__      =					parent
             lowercase__      =					batch_size
             lowercase__      =					image_size
             lowercase__      =					patch_size
             lowercase__      =					num_channels
             lowercase__      =					make_divisible(512 * width_multiplier  ,		divisor=8					)
             lowercase__      =					hidden_act
             lowercase__      =					conv_kernel_size
             lowercase__      =					output_stride
             lowercase__      =					classifier_dropout_prob
             lowercase__      =					use_labels
             lowercase__      =					is_training
             lowercase__      =					num_labels
             lowercase__      =					initializer_range
             lowercase__      =					scope
             lowercase__      =					width_multiplier
             lowercase__      =					ffn_dropout
             lowercase__      =					attn_dropout



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







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

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

             lowercase__      =					self.get_config()

             return config, pixel_values, labels, pixel_labels



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             return MobileViTVaConfig(
                 image_size=self.image_size  ,		patch_size=self.patch_size  ,		num_channels=self.num_channels  ,		hidden_act=self.hidden_act  ,		conv_kernel_size=self.conv_kernel_size  ,		output_stride=self.output_stride  ,		classifier_dropout_prob=self.classifier_dropout_prob  ,		initializer_range=self.initializer_range  ,		width_multiplier=self.width_multiplier  ,		ffn_dropout=self.ffn_dropout_prob  ,		attn_dropout=self.attn_dropout_prob  ,		)



      def  UpperCamelCase__						(self       :       int  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					MobileViTVaModel(config=snake_case_					)
             model.to(snake_case_					)
             model.eval()
             lowercase__      =					model(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  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Any					):

             '''simple docstring'''







             lowercase__      =					self.num_labels
             lowercase__      =					MobileViTVaForImageClassification(snake_case_					)
             model.to(snake_case_					)
             model.eval()
             lowercase__      =					model(snake_case_  ,		labels=snake_case_					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels)					)



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             lowercase__      =					self.num_labels
             lowercase__      =					MobileViTVaForSemanticSegmentation(snake_case_					)
             model.to(snake_case_					)
             model.eval()
             lowercase__      =					model(snake_case_					)
             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__      =					model(snake_case_  ,		labels=snake_case_					)
             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  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					self.prepare_config_and_inputs()
             lowercase__     ,lowercase__     ,lowercase__     ,lowercase__      =					config_and_inputs
             lowercase__      =					{'''pixel_values''': pixel_values}
             return config, inputs_dict






@require_torch
class     __lowerCAmelCase		(A__    ,	A__    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	Any       =				(
          (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )

      lowerCAmelCase__   :	List[str]       =				(
          {
              """feature-extraction""": MobileViTVaModel,
              """image-classification""": MobileViTVaForImageClassification,
              """image-segmentation""": MobileViTVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__   :	List[Any]       =				False
      lowerCAmelCase__   :	Optional[Any]       =				False
      lowerCAmelCase__   :	Optional[int]       =				False
      lowerCAmelCase__   :	List[str]       =				False



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					MobileViTVaModelTester(self					)
             lowercase__      =					MobileViTVaConfigTester(self  ,		config_class=snake_case_  ,		has_text_modality=snake_case_					)



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      @unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''					)
      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''					)
      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''MobileViTV2 does not output attentions'''					)
      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      @require_torch_multi_gpu
      @unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''					)
      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             pass



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             lowercase__     ,lowercase__      =					self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__      =					['''pixel_values''']
                    self.assertListEqual(arg_names[:1]  ,		snake_case_					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*snake_case_					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             def check_hidden_states_output(UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       int					):
                    lowercase__      =					model_class(snake_case_					)
                    model.to(snake_case_					)
                    model.eval()

                    with torch.no_grad():
                           lowercase__      =					model(**self._prepare_for_class(snake_case_  ,		snake_case_					)					)

                    lowercase__      =					outputs.hidden_states

                    lowercase__      =					5
                    self.assertEqual(len(snake_case_					)  ,		snake_case_					)

                    # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                    # with the width and height being successively divided by 2.
                    lowercase__      =					2
                    for i in range(len(snake_case_					)					):
                           self.assertListEqual(
                               list(hidden_states[i].shape[-2:]					)  ,		[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]  ,		)
                           divisor *= 2

                    self.assertEqual(self.model_tester.output_stride  ,		divisor // 2					)

             lowercase__     ,lowercase__      =					self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__      =					True
                    check_hidden_states_output(snake_case_  ,		snake_case_  ,		snake_case_					)

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

                    check_hidden_states_output(snake_case_  ,		snake_case_  ,		snake_case_					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*snake_case_					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_					)






      @slow
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      =					MobileViTVaModel.from_pretrained(snake_case_					)
                    self.assertIsNotNone(snake_case_					)






def _SCREAMING_SNAKE_CASE ()			->			Optional[int]:






       """simple docstring"""







       lowercase__      =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
       return image

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





      '''simple docstring'''






      @cached_property
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             return (
                 MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''					)
                 if is_vision_available()
                 else None
             )



      @slow
      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''					).to(
                 snake_case_					)

             lowercase__      =					self.default_image_processor
             lowercase__      =					prepare_img()
             lowercase__      =					image_processor(images=snake_case_  ,		return_tensors='''pt'''					).to(snake_case_					)

             # forward pass
             with torch.no_grad():
                    lowercase__      =					model(**snake_case_					)

             # verify the logits
             lowercase__      =					torch.Size((1, 1000)					)
             self.assertEqual(outputs.logits.shape  ,		snake_case_					)

             lowercase__      =					torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]					).to(snake_case_					)

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



      @slow
      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''					)
             lowercase__      =					model.to(snake_case_					)

             lowercase__      =					MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''					)

             lowercase__      =					prepare_img()
             lowercase__      =					image_processor(images=snake_case_  ,		return_tensors='''pt'''					).to(snake_case_					)

             # forward pass
             with torch.no_grad():
                    lowercase__      =					model(**snake_case_					)
             lowercase__      =					outputs.logits

             # verify the logits
             lowercase__      =					torch.Size((1, 21, 32, 32)					)
             self.assertEqual(logits.shape  ,		snake_case_					)

             lowercase__      =					torch.tensor(
                 [
                     [[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
                     [[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
                     [[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
                 ]  ,		device=snake_case_  ,		)

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






      @slow
      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''					)
             lowercase__      =					model.to(snake_case_					)

             lowercase__      =					MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''					)

             lowercase__      =					prepare_img()
             lowercase__      =					image_processor(images=snake_case_  ,		return_tensors='''pt'''					).to(snake_case_					)

             # forward pass
             with torch.no_grad():
                    lowercase__      =					model(**snake_case_					)

             lowercase__      =					outputs.logits.detach().cpu()

             lowercase__      =					image_processor.post_process_semantic_segmentation(outputs=snake_case_  ,		target_sizes=[(50, 60)]					)
             lowercase__      =					torch.Size((50, 60)					)
             self.assertEqual(segmentation[0].shape  ,		snake_case_					)

             lowercase__      =					image_processor.post_process_semantic_segmentation(outputs=snake_case_					)
             lowercase__      =					torch.Size((32, 32)					)
             self.assertEqual(segmentation[0].shape  ,		snake_case_					)






"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
			import textwrap

			from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
			import torch

			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			SCREAMING_SNAKE_CASE_					     =	False


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_					     =	'''ybelkada/fonts'''

def  lowercase     ():
						if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
												raise ImportError(
												    f"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
												    """Pix2StructImageProcessor. Please upgrade torch."""						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						requires_backends(_lowerCAmelCase ,    ["""torch"""]						)
						_check_torch_version()

						__lowerCAmelCase     =  image_tensor.unsqueeze(0						)
						__lowerCAmelCase     =  torch.nn.functional.unfold(_lowerCAmelCase ,    (patch_height, patch_width) ,    stride=(patch_height, patch_width)						)
						__lowerCAmelCase     =  patches.reshape(image_tensor.size(0						) ,    image_tensor.size(1						) ,    _lowerCAmelCase ,    _lowerCAmelCase ,    -1						)
						__lowerCAmelCase     =  patches.permute(0 ,    4 ,    2 ,    3 ,    1						).reshape(
						    image_tensor.size(2						) // patch_height ,    image_tensor.size(3						) // patch_width ,    image_tensor.size(1						) * patch_height * patch_width ,    )
						return patches.unsqueeze(0						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = 36 ,    _lowerCAmelCase = "black" ,    _lowerCAmelCase = "white" ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    ):
						requires_backends(_lowerCAmelCase ,    """vision"""						)
						# Add new lines so that each line is no more than 80 characters.

						__lowerCAmelCase     =  textwrap.TextWrapper(width=80						)
						__lowerCAmelCase     =  wrapper.wrap(text=_lowerCAmelCase						)
						__lowerCAmelCase     =  """\n""".join(_lowerCAmelCase						)

						if font_bytes is not None and font_path is None:
												__lowerCAmelCase     =  io.BytesIO(_lowerCAmelCase						)
						elif font_path is not None:
												__lowerCAmelCase     =  font_path
						else:
												__lowerCAmelCase     =  hf_hub_download(_lowerCAmelCase ,    """Arial.TTF"""						)
						__lowerCAmelCase     =  ImageFont.truetype(_lowerCAmelCase ,    encoding="""UTF-8""" ,    size=_lowerCAmelCase						)

						# Use a temporary canvas to determine the width and height in pixels when
						# rendering the text.
						__lowerCAmelCase     =  ImageDraw.Draw(Image.new("""RGB""" ,    (1, 1) ,    _lowerCAmelCase						)						)
						__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  temp_draw.textbbox((0, 0) ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						# Create the actual image with a bit of padding around the text.
						__lowerCAmelCase     =  text_width + left_padding + right_padding
						__lowerCAmelCase     =  text_height + top_padding + bottom_padding
						__lowerCAmelCase     =  Image.new("""RGB""" ,    (image_width, image_height) ,    _lowerCAmelCase						)
						__lowerCAmelCase     =  ImageDraw.Draw(_lowerCAmelCase						)
						draw.text(xy=(left_padding, top_padding) ,    text=_lowerCAmelCase ,    fill=_lowerCAmelCase ,    font=_lowerCAmelCase						)
						return image

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    **_lowerCAmelCase						):
						requires_backends(_lowerCAmelCase ,    """vision"""						)

						# Convert to PIL image if necessary
						__lowerCAmelCase     =  to_pil_image(_lowerCAmelCase						)

						__lowerCAmelCase     =  render_text(_lowerCAmelCase ,    **_lowerCAmelCase						)
						__lowerCAmelCase     =  max(header_image.width ,    image.width						)

						__lowerCAmelCase     =  int(image.height * (new_width / image.width)						)
						__lowerCAmelCase     =  int(header_image.height * (new_width / header_image.width)						)

						__lowerCAmelCase     =  Image.new("""RGB""" ,    (new_width, new_height + new_header_height) ,    """white"""						)
						new_image.paste(header_image.resize((new_width, new_header_height)						) ,    (0, 0)						)
						new_image.paste(image.resize((new_width, new_height)						) ,    (0, new_header_height)						)

						# Convert back to the original framework if necessary
						__lowerCAmelCase     =  to_numpy_array(_lowerCAmelCase						)

						if infer_channel_dimension_format(_lowerCAmelCase						) == ChannelDimension.LAST:
												__lowerCAmelCase     =  to_channel_dimension_format(_lowerCAmelCase ,    ChannelDimension.LAST						)

						return new_image



class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		['''flattened_patches''']




							def __init__( self    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = None    ,				snake_case_ = 2_048    ,				snake_case_ = False    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)
													__lowerCAmelCase     =  patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
													__lowerCAmelCase     =  do_normalize
													__lowerCAmelCase     =  do_convert_rgb
													__lowerCAmelCase     =  max_patches
													__lowerCAmelCase     =  is_vqa




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				**snake_case_				)       ->  np.ndarray:
													requires_backends(self.extract_flattened_patches    ,				"""torch"""				)
													_check_torch_version()

													# convert to torch
													__lowerCAmelCase     =  to_channel_dimension_format(snake_case_    ,				ChannelDimension.FIRST				)
													__lowerCAmelCase     =  torch.from_numpy(snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  patch_size["""height"""], patch_size["""width"""]
													__lowerCAmelCase   ,     __lowerCAmelCase     =  get_image_size(snake_case_				)

													# maximize scale s.t.
													__lowerCAmelCase     =  math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)				)
													__lowerCAmelCase     =  max(min(math.floor(scale * image_height / patch_height				)    ,				snake_case_				)    ,				1				)
													__lowerCAmelCase     =  max(min(math.floor(scale * image_width / patch_width				)    ,				snake_case_				)    ,				1				)
													__lowerCAmelCase     =  max(num_feasible_rows * patch_height    ,				1				)
													__lowerCAmelCase     =  max(num_feasible_cols * patch_width    ,				1				)

													__lowerCAmelCase     =  torch.nn.functional.interpolate(
													    image.unsqueeze(0				)    ,				size=(resized_height, resized_width)    ,				mode="""bilinear"""    ,				align_corners=snake_case_    ,				antialias=snake_case_    ,				).squeeze(0				)

													# [1, rows, columns, patch_height * patch_width * image_channels]
													__lowerCAmelCase     =  torch_extract_patches(snake_case_    ,				snake_case_    ,				snake_case_				)

													__lowerCAmelCase     =  patches.shape
													__lowerCAmelCase     =  patches_shape[1]
													__lowerCAmelCase     =  patches_shape[2]
													__lowerCAmelCase     =  patches_shape[3]

													# [rows * columns, patch_height * patch_width * image_channels]
													__lowerCAmelCase     =  patches.reshape([rows * columns, depth]				)

													# [rows * columns, 1]
													__lowerCAmelCase     =  torch.arange(snake_case_				).reshape([rows, 1]				).repeat(1    ,				snake_case_				).reshape([rows * columns, 1]				)
													__lowerCAmelCase     =  torch.arange(snake_case_				).reshape([1, columns]				).repeat(snake_case_    ,				1				).reshape([rows * columns, 1]				)

													# Offset by 1 so the ids do not contain zeros, which represent padding.
													row_ids += 1
													col_ids += 1

													# Prepare additional patch features.
													# [rows * columns, 1]
													__lowerCAmelCase     =  row_ids.to(torch.floataa				)
													__lowerCAmelCase     =  col_ids.to(torch.floataa				)

													# [rows * columns, 2 + patch_height * patch_width * image_channels]
													__lowerCAmelCase     =  torch.cat([row_ids, col_ids, patches]    ,				-1				)

													# [max_patches, 2 + patch_height * patch_width * image_channels]
													__lowerCAmelCase     =  torch.nn.functional.pad(snake_case_    ,				[0, 0, 0, max_patches - (rows * columns)]				).float()

													__lowerCAmelCase     =  to_numpy_array(snake_case_				)

													return result




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				**snake_case_				)       ->  np.ndarray:
													if image.dtype == np.uinta:
																			__lowerCAmelCase     =  image.astype(np.floataa				)

													# take mean across the whole `image`
													__lowerCAmelCase     =  np.mean(snake_case_				)
													__lowerCAmelCase     =  np.std(snake_case_				)
													__lowerCAmelCase     =  max(snake_case_    ,				1.0 / math.sqrt(np.prod(image.shape				)				)				)

													return normalize(snake_case_    ,				mean=snake_case_    ,				std=snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = ChannelDimension.FIRST    ,				**snake_case_    ,				)       ->  ImageInput:
													__lowerCAmelCase     =  do_normalize if do_normalize is not None else self.do_normalize
													__lowerCAmelCase     =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
													__lowerCAmelCase     =  patch_size if patch_size is not None else self.patch_size
													__lowerCAmelCase     =  max_patches if max_patches is not None else self.max_patches
													__lowerCAmelCase     =  self.is_vqa

													if kwargs.get("""data_format"""    ,				snake_case_				) is not None:
																			raise ValueError("""data_format is not an accepted input as the outputs are """				)

													__lowerCAmelCase     =  make_list_of_images(snake_case_				)

													if not valid_images(snake_case_				):
																			raise ValueError(
																			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																			    """torch.Tensor, tf.Tensor or jax.ndarray."""				)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																			__lowerCAmelCase     =  [convert_to_rgb(snake_case_				) for image in images]

													# All transformations expect numpy arrays.
													__lowerCAmelCase     =  [to_numpy_array(snake_case_				) for image in images]

													if is_vqa:
																			if header_text is None:
																									raise ValueError("""A header text must be provided for VQA models."""				)
																			__lowerCAmelCase     =  kwargs.pop("""font_bytes"""    ,				snake_case_				)
																			__lowerCAmelCase     =  kwargs.pop("""font_path"""    ,				snake_case_				)

																			if isinstance(snake_case_    ,				snake_case_				):
																									__lowerCAmelCase     =  [header_text] * len(snake_case_				)

																			__lowerCAmelCase     =  [
																			    render_header(snake_case_    ,				header_text[i]    ,				font_bytes=snake_case_    ,				font_path=snake_case_				)
																			    for i, image in enumerate(snake_case_				)
																			]

													if do_normalize:
																			__lowerCAmelCase     =  [self.normalize(image=snake_case_				) for image in images]

													# convert to torch tensor and permute
													__lowerCAmelCase     =  [
													    self.extract_flattened_patches(image=snake_case_    ,				max_patches=snake_case_    ,				patch_size=snake_case_				)
													    for image in images
													]

													# create attention mask in numpy
													__lowerCAmelCase     =  [(image.sum(axis=-1				) != 0).astype(np.floataa				) for image in images]

													__lowerCAmelCase     =  BatchFeature(
													    data={"""flattened_patches""": images, """attention_mask""": attention_masks}    ,				tensor_type=snake_case_				)

													return encoded_outputs
