


'''simple docstring'''

import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     DataaVecVisionForImageClassification,
     DataaVecVisionForSemanticSegmentation,
     DataaVecVisionModel,
 )
 from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor
class 						__lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self					:					Any     ,		UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Optional[int]=100     ,		UpperCamelCase					:					Tuple=13     ,		UpperCamelCase					:					Union[str, Any]=30     ,		UpperCamelCase					:					int=2     ,		UpperCamelCase					:					Union[str, Any]=3     ,		UpperCamelCase					:					Union[str, Any]=True     ,		UpperCamelCase					:					str=True     ,		UpperCamelCase					:					Union[str, Any]=32     ,		UpperCamelCase					:					Tuple=4     ,		UpperCamelCase					:					Optional[Any]=4     ,		UpperCamelCase					:					Tuple=37     ,		UpperCamelCase					:					Tuple="gelu"     ,		UpperCamelCase					:					List[str]=0.1     ,		UpperCamelCase					:					Optional[int]=0.1     ,		UpperCamelCase					:					List[Any]=10     ,		UpperCamelCase					:					Optional[Any]=0.02     ,		UpperCamelCase					:					Any=3     ,		UpperCamelCase					:					List[str]=None     ,		UpperCamelCase					:					Optional[int]=[0, 1, 2, 3]     ,		):

             '''simple docstring'''







             lowercase__       =				parent
             lowercase__       =				100
             lowercase__       =				batch_size
             lowercase__       =				image_size
             lowercase__       =				patch_size
             lowercase__       =				num_channels
             lowercase__       =				is_training
             lowercase__       =				use_labels
             lowercase__       =				hidden_size
             lowercase__       =				num_hidden_layers
             lowercase__       =				num_attention_heads
             lowercase__       =				intermediate_size
             lowercase__       =				hidden_act
             lowercase__       =				hidden_dropout_prob
             lowercase__       =				attention_probs_dropout_prob
             lowercase__       =				type_sequence_label_size
             lowercase__       =				initializer_range
             lowercase__       =				scope
             lowercase__       =				out_indices
             lowercase__       =				num_labels

             # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
             lowercase__       =				(image_size // patch_size) ** 2
             lowercase__       =				num_patches + 1



      def    UpperCamelCase__	(self					:					Any				):

             '''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.type_sequence_label_size				)
                    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					:					List[str]				):

             '''simple docstring'''







             return DataaVecVisionConfig(
                 vocab_size=self.vocab_size     ,		image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		hidden_size=self.hidden_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		is_decoder=UpperCamelCase     ,		initializer_range=self.initializer_range     ,		out_indices=self.out_indices     ,		)



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

             '''simple docstring'''







             lowercase__       =				DataaVecVisionModel(config=UpperCamelCase				)
             model.to(UpperCamelCase				)
             model.eval()
             lowercase__       =				model(UpperCamelCase				)
             # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
             lowercase__       =				(self.image_size // self.patch_size) ** 2
             self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, num_patches + 1, self.hidden_size)				)



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

             '''simple docstring'''







             lowercase__       =				self.type_sequence_label_size
             lowercase__       =				DataaVecVisionForImageClassification(UpperCamelCase				)
             model.to(UpperCamelCase				)
             model.eval()
             lowercase__       =				model(UpperCamelCase     ,		labels=UpperCamelCase				)
             self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)				)



      def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					Union[str, Any]     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Tuple				):

             '''simple docstring'''







             lowercase__       =				self.num_labels
             lowercase__       =				DataaVecVisionForSemanticSegmentation(UpperCamelCase				)
             model.to(UpperCamelCase				)
             model.eval()
             lowercase__       =				model(UpperCamelCase				)
             self.parent.assertEqual(
                 result.logits.shape     ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)
             lowercase__       =				model(UpperCamelCase     ,		labels=UpperCamelCase				)
             self.parent.assertEqual(
                 result.logits.shape     ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)






      def    UpperCamelCase__	(self					:					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		(lowercase_						,  lowercase_						,  unittest.TestCase  ):





      '''simple docstring'''






      lowerCAmelCase__      :  str						      =							(
          (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__      :  Union[str, Any]						      =							(
          {
              """feature-extraction""": DataaVecVisionModel,
              """image-classification""": DataaVecVisionForImageClassification,
              """image-segmentation""": DataaVecVisionForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__      :  Tuple						      =							False
      lowerCAmelCase__      :  Union[str, Any]						      =							False
      lowerCAmelCase__      :  Dict						      =							False



      def    UpperCamelCase__	(self					:					Optional[Any]				):

             '''simple docstring'''







             lowercase__       =				DataaVecVisionModelTester(self				)
             lowercase__       =				ConfigTester(
                 self     ,		config_class=UpperCamelCase     ,		has_text_modality=UpperCamelCase     ,		hidden_size=37				)



      def    UpperCamelCase__	(self					:					List[Any]				):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







             pass



      @require_torch_multi_gpu
      @unittest.skip(
          reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''				)
      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







             pass



      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__       =				model_class(UpperCamelCase				)
                    self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module)				)
                    lowercase__       =				model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(UpperCamelCase     ,		nn.Linear				)				)



      def    UpperCamelCase__	(self					:					int				):

             '''simple docstring'''







             lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__       =				model_class(UpperCamelCase				)
                    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]     ,		UpperCamelCase				)



      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







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



      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







             lowercase__       =				self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_segmentation(*UpperCamelCase				)



      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             if not self.model_tester.is_training:
                    return

             lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__       =				True

             for model_class in self.all_model_classes:
                    if model_class in [*get_values(UpperCamelCase				)]:
                           continue

                    lowercase__       =				model_class(UpperCamelCase				)
                    model.to(UpperCamelCase				)
                    model.train()
                    lowercase__       =				self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase     ,		return_labels=UpperCamelCase				)
                    lowercase__       =				model(**UpperCamelCase				).loss
                    loss.backward()



      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                    return

             lowercase__       =				False
             lowercase__       =				True

             for model_class in self.all_model_classes:
                    if model_class in [*get_values(UpperCamelCase				)] or not model_class.supports_gradient_checkpointing:
                           continue
                    # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
                    # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                    elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                           lowercase__    ,lowercase__    ,lowercase__    ,lowercase__       =				inputs_dict['''pixel_values'''].shape
                           lowercase__       =				torch.zeros(
                               [self.model_tester.batch_size, height, width]     ,		device=UpperCamelCase				).long()
                    lowercase__       =				model_class(UpperCamelCase				)
                    model.gradient_checkpointing_enable()
                    model.to(UpperCamelCase				)
                    model.train()
                    lowercase__       =				self._prepare_for_class(UpperCamelCase     ,		UpperCamelCase     ,		return_labels=UpperCamelCase				)
                    lowercase__       =				model(**UpperCamelCase				).loss
                    loss.backward()



      def    UpperCamelCase__	(self					:					Optional[Any]				):

             '''simple docstring'''







             lowercase__    ,lowercase__       =				self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__       =				_config_zero_init(UpperCamelCase				)
             for model_class in self.all_model_classes:
                    lowercase__       =				model_class(config=UpperCamelCase				)
                    for name, param in model.named_parameters():
                           # we skip lambda parameters as these require special initial values
                           # determined by config.layer_scale_init_value
                           if "lambda" in name:
                                  continue
                           if param.requires_grad:
                                  self.assertIn(
                                      ((param.data.mean() * 1E9).round() / 1E9).item()     ,		[0.0, 1.0]     ,		msg=f"Parameter {name} of model {model_class} seems not properly initialized"     ,		)



      def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					Optional[Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					List[str]=2E-4     ,		UpperCamelCase					:					Optional[Any]="outputs"     ,		UpperCamelCase					:					Any=None				):

             '''simple docstring'''







             super().check_pt_tf_outputs(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)



      def    UpperCamelCase__	(self					:					Optional[Any]				):

             '''simple docstring'''







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






      @slow
      def    UpperCamelCase__	(self					:					Dict				):

             '''simple docstring'''







             for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__       =				DataaVecVisionModel.from_pretrained(UpperCamelCase				)
                    self.assertIsNotNone(UpperCamelCase				)






def _SCREAMING_SNAKE_CASE   () -> Dict:






       """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					:					List[str]				):

             '''simple docstring'''







             return (
                 BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k'''				) if is_vision_available() else None
             )






      @slow
      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             lowercase__       =				DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k'''				).to(
                 UpperCamelCase				)

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

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

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

             lowercase__       =				torch.tensor([0.32_77, -0.13_95, 0.09_11]				).to(UpperCamelCase				)

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

             lowercase__       =				[model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
             self.assertEqual(logits[0].topk(2				).indices.cpu().tolist()     ,		UpperCamelCase				)








'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


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





      '''simple docstring'''






      lowerCAmelCase__      :  List[str]						      =							["""pixel_values"""]



      def __init__(self					:					Optional[int]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[int, float] = 1 / 255     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = True     ,		**UpperCamelCase					:					Optional[int]     ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase				)
             lowercase__       =				size if size is not None else {'''height''': 346, '''width''': 346}
             lowercase__       =				get_size_dict(UpperCamelCase				)
             lowercase__       =				crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
             lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)

             lowercase__       =				do_resize
             lowercase__       =				size
             lowercase__       =				resample
             lowercase__       =				do_center_crop
             lowercase__       =				crop_size
             lowercase__       =				do_rescale
             lowercase__       =				rescale_factor
             lowercase__       =				rescale_offset
             lowercase__       =				do_normalize
             lowercase__       =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             lowercase__       =				image_std if image_std is not None else IMAGENET_STANDARD_STD
             lowercase__       =				include_top



      def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Dict     ,		):

             '''simple docstring'''







             lowercase__       =				get_size_dict(UpperCamelCase				)
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
             return resize(
                 UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		resample=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)



      def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Union[str, Any]     ,		):

             '''simple docstring'''







             lowercase__       =				get_size_dict(UpperCamelCase				)
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
             return center_crop(UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)



      def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[int, float]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[str]     ,		):

             '''simple docstring'''







             if offset:
                    lowercase__       =				(image - 1_27.5) * scale
                    if data_format is not None:
                           lowercase__       =				to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				)
                    lowercase__       =				rescaled_image.astype(np.floataa				)
             else:
                    lowercase__       =				rescale(UpperCamelCase     ,		scale=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
             return rescaled_image



      def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[Any]     ,		):

             '''simple docstring'''







             return normalize(UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)






      def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					ImageInput     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[str, Any]=None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					float = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[str, TensorType]] = None     ,		UpperCamelCase					:					ChannelDimension = ChannelDimension.FIRST     ,		**UpperCamelCase					:					List[Any]     ,		):

             '''simple docstring'''







             lowercase__       =				do_resize if do_resize is not None else self.do_resize
             lowercase__       =				resample if resample is not None else self.resample
             lowercase__       =				do_center_crop if do_center_crop is not None else self.do_center_crop
             lowercase__       =				do_rescale if do_rescale is not None else self.do_rescale
             lowercase__       =				rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__       =				rescale_offset if rescale_offset is not None else self.rescale_offset
             lowercase__       =				do_normalize if do_normalize is not None else self.do_normalize
             lowercase__       =				image_mean if image_mean is not None else self.image_mean
             lowercase__       =				image_std if image_std is not None else self.image_std
             lowercase__       =				include_top if include_top is not None else self.include_top

             lowercase__       =				size if size is not None else self.size
             lowercase__       =				get_size_dict(UpperCamelCase				)
             lowercase__       =				crop_size if crop_size is not None else self.crop_size
             lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)

             lowercase__       =				make_list_of_images(UpperCamelCase				)

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

             if do_resize and size is None or resample is None:
                    raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

             if do_center_crop and crop_size is None:
                    raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)

             if do_rescale and rescale_factor is None:
                    raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

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

             if do_resize:
                    lowercase__       =				[self.resize(image=UpperCamelCase     ,		size=UpperCamelCase     ,		resample=UpperCamelCase				) for image in images]

             if do_center_crop:
                    lowercase__       =				[self.center_crop(image=UpperCamelCase     ,		size=UpperCamelCase				) for image in images]

             if do_rescale:
                    lowercase__       =				[self.rescale(image=UpperCamelCase     ,		scale=UpperCamelCase     ,		offset=UpperCamelCase				) for image in images]

             if do_normalize:
                    lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase				) for image in images]

             if include_top:
                    lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=[0, 0, 0]     ,		std=UpperCamelCase				) for image in images]

             lowercase__       =				[to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				) for image in images]

             lowercase__       =				{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase     ,		tensor_type=UpperCamelCase				)




