




import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

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


if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     self.config_class(**self.inputs_dict		)
       self.parent.assertTrue(hasattr(_lowerCAmelCase							,							'tf_padding'		)		)
       self.parent.assertTrue(hasattr(_lowerCAmelCase							,							'depth_multiplier'		)		)





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						List[Any]=13							,							_lowerCAmelCase    :						Optional[Any]=3							,							_lowerCAmelCase    :						Optional[int]=32							,							_lowerCAmelCase    :						Dict=0.25							,							_lowerCAmelCase    :						str=8							,							_lowerCAmelCase    :						Dict=8							,							_lowerCAmelCase    :						Union[str, Any]=6							,							_lowerCAmelCase    :						str=32							,							_lowerCAmelCase    :						int=True							,							_lowerCAmelCase    :						Dict=True							,							_lowerCAmelCase    :						Optional[int]=True							,							_lowerCAmelCase    :						Tuple="relu6"							,							_lowerCAmelCase    :						Optional[int]=1_280							,							_lowerCAmelCase    :						Union[str, Any]=0.1							,							_lowerCAmelCase    :						Any=0.02							,							_lowerCAmelCase    :						int=True							,							_lowerCAmelCase    :						Dict=True							,							_lowerCAmelCase    :						Union[str, Any]=10							,							_lowerCAmelCase    :						Optional[Any]=None							,							):
       SCREAMING_SNAKE_CASE_      		=     parent
       SCREAMING_SNAKE_CASE_      		=     batch_size
       SCREAMING_SNAKE_CASE_      		=     num_channels
       SCREAMING_SNAKE_CASE_      		=     image_size
       SCREAMING_SNAKE_CASE_      		=     depth_multiplier
       SCREAMING_SNAKE_CASE_      		=     depth_divisible_by
       SCREAMING_SNAKE_CASE_      		=     min_depth
       SCREAMING_SNAKE_CASE_      		=     expand_ratio
       SCREAMING_SNAKE_CASE_      		=     tf_padding
       SCREAMING_SNAKE_CASE_      		=     output_stride
       SCREAMING_SNAKE_CASE_      		=     first_layer_is_expansion
       SCREAMING_SNAKE_CASE_      		=     finegrained_output
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier		)
       SCREAMING_SNAKE_CASE_      		=     classifier_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     use_labels
       SCREAMING_SNAKE_CASE_      		=     is_training
       SCREAMING_SNAKE_CASE_      		=     num_labels
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     scope



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

       SCREAMING_SNAKE_CASE_      		=     self.get_config()

       return config, pixel_values, labels, pixel_labels



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



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						Optional[int]							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     MobileNetVaModel(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)
       self.parent.assertEqual(
           result.last_hidden_state.shape							,							(
               self.batch_size,
               self.last_hidden_size,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )							,							)
       self.parent.assertEqual(
           result.pooler_output.shape							,							(self.batch_size, self.last_hidden_size)							,							)



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Optional[int]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.num_labels
       SCREAMING_SNAKE_CASE_      		=     MobileNetVaForImageClassification(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							labels=_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)		)



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




 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     config_and_inputs
       SCREAMING_SNAKE_CASE_      		=     {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       (
     (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 lowercase_							       =       (
     {
         "feature-extraction": MobileNetVaModel,
         "image-classification": MobileNetVaForImageClassification,
         "image-segmentation": MobileNetVaForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     MobileNetVaModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     MobileNetVaConfigTester(self							,							config_class=_lowerCAmelCase							,							has_text_modality=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       self.config_tester.run_common_tests()



 @unittest.skip(reason='MobileNetV2 does not use inputs_embeds'		)
 def  lowerCAmelCase_    (				self    :						Any		):
       pass



 @unittest.skip(reason='MobileNetV2 does not support input and output embeddings'		)
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       pass



 @unittest.skip(reason='MobileNetV2 does not output attentions'		)
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       pass



 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_      		=     ['pixel_values']
             self.assertListEqual(arg_names[:1]							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						int		):
       def check_hidden_states_output(_lowerCAmelCase    :						List[str]							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Tuple		):
             SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
             model.to(_lowerCAmelCase		)
             model.eval()

             with torch.no_grad():
                   SCREAMING_SNAKE_CASE_      		=     model(**self._prepare_for_class(_lowerCAmelCase							,							_lowerCAmelCase		)		)

             SCREAMING_SNAKE_CASE_      		=     outputs.hidden_states

             SCREAMING_SNAKE_CASE_      		=     16
             self.assertEqual(len(_lowerCAmelCase		)							,							_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_      		=     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_      		=     True

             check_hidden_states_output(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase		)




 @slow
 def  lowerCAmelCase_    (				self    :						Any		):
       for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_      		=     MobileNetVaModel.from_pretrained(_lowerCAmelCase		)
             self.assertIsNotNone(_lowerCAmelCase		)


def    UpperCAmelCase_				( )   ->					Dict:
      SCREAMING_SNAKE_CASE_      		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      return image





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


 '''simple docstring'''







 @cached_property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       return (
           MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'		) if is_vision_available() else None
       )



 @slow
 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'		).to(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor
       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(images=_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       # verify the logits
       SCREAMING_SNAKE_CASE_      		=     torch.Size((1, 1_001)		)
       self.assertEqual(outputs.logits.shape							,							_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor([0.2445, -1.1993, 0.1905]		).to(_lowerCAmelCase		)

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




 @slow
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'		)
       SCREAMING_SNAKE_CASE_      		=     model.to(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'		)

       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(images=_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE_      		=     torch.Size((1, 21, 65, 65)		)
       self.assertEqual(logits.shape							,							_lowerCAmelCase		)

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

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






"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a_					=     logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	["""pixel_values"""]

 def __init__(       self   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		__lowerCamelCase = 1 / 255   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase    )
     __A       :		Optional[int]							=	size if size is not None else {'''shortest_edge''': 224}
     __A       :		Optional[int]							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase    )
     __A       :		Dict							=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     __A       :		str							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase   ,		param_name='''crop_size'''    )

     __A       :		Dict							=	do_resize
     __A       :		Optional[Any]							=	size
     __A       :		Optional[Any]							=	resample
     __A       :		Dict							=	do_center_crop
     __A       :		Any							=	crop_size
     __A       :		Optional[Any]							=	do_rescale
     __A       :		List[Any]							=	rescale_factor
     __A       :		Union[str, Any]							=	do_normalize
     __A       :		Any							=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     __A       :		Optional[int]							=	image_std if image_std is not None else OPENAI_CLIP_STD
     __A       :		Dict							=	do_convert_rgb

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase    )
     if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
     __A       :		Optional[int]							=	get_resize_output_image_size(__lowerCamelCase   ,		size=size['''shortest_edge''']   ,		default_to_square=__lowerCamelCase    )
     return resize(__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	get_size_dict(__lowerCamelCase    )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
     return center_crop(__lowerCamelCase   ,		size=(size['''height'''], size['''width'''])   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     return rescale(__lowerCamelCase   ,		scale=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




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







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = ChannelDimension.FIRST   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Tuple							=	do_resize if do_resize is not None else self.do_resize
     __A       :		Any							=	size if size is not None else self.size
     __A       :		List[str]							=	get_size_dict(__lowerCamelCase   ,		param_name='''size'''   ,		default_to_square=__lowerCamelCase    )
     __A       :		List[Any]							=	resample if resample is not None else self.resample
     __A       :		Any							=	do_center_crop if do_center_crop is not None else self.do_center_crop
     __A       :		Dict							=	crop_size if crop_size is not None else self.crop_size
     __A       :		List[Any]							=	get_size_dict(__lowerCamelCase   ,		param_name='''crop_size'''   ,		default_to_square=__lowerCamelCase    )
     __A       :		List[str]							=	do_rescale if do_rescale is not None else self.do_rescale
     __A       :		List[Any]							=	rescale_factor if rescale_factor is not None else self.rescale_factor
     __A       :		Tuple							=	do_normalize if do_normalize is not None else self.do_normalize
     __A       :		str							=	image_mean if image_mean is not None else self.image_mean
     __A       :		int							=	image_std if image_std is not None else self.image_std
     __A       :		Union[str, Any]							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     __A       :		int							=	make_list_of_images(__lowerCamelCase    )

     if not valid_images(__lowerCamelCase    ):
         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:
         raise ValueError('''Size 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.'''    )

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
         __A       :		List[str]							=	[convert_to_rgb(__lowerCamelCase    ) for image in images]

     # All transformations expect numpy arrays.
     __A       :		Optional[int]							=	[to_numpy_array(__lowerCamelCase    ) for image in images]

     if do_resize:
         __A       :		List[str]							=	[self.resize(image=__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase    ) for image in images]

     if do_center_crop:
         __A       :		Dict							=	[self.center_crop(image=__lowerCamelCase   ,		size=__lowerCamelCase    ) for image in images]

     if do_rescale:
         __A       :		str							=	[self.rescale(image=__lowerCamelCase   ,		scale=__lowerCamelCase    ) for image in images]

     if do_normalize:
         __A       :		Optional[Any]							=	[self.normalize(image=__lowerCamelCase   ,		mean=__lowerCamelCase   ,		std=__lowerCamelCase    ) for image in images]

     __A       :		str							=	[to_channel_dimension_format(__lowerCamelCase   ,		__lowerCamelCase    ) for image in images]

     __A       :		Union[str, Any]							=	{'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase   ,		tensor_type=__lowerCamelCase    )




