






import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import YolosForObjectDetection, YolosModel
  from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Dict       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Any=13       ,				lowerCamelCase		:			List[Any]=[30, 30]       ,				lowerCamelCase		:			Optional[Any]=2       ,				lowerCamelCase		:			Tuple=3       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			Dict=True       ,				lowerCamelCase		:			Tuple=32       ,				lowerCamelCase		:			Optional[int]=5       ,				lowerCamelCase		:			Tuple=4       ,				lowerCamelCase		:			Dict=37       ,				lowerCamelCase		:			int="gelu"       ,				lowerCamelCase		:			Dict=0.1       ,				lowerCamelCase		:			str=0.1       ,				lowerCamelCase		:			Dict=10       ,				lowerCamelCase		:			Any=0.02       ,				lowerCamelCase		:			str=3       ,				lowerCamelCase		:			int=None       ,				lowerCamelCase		:			int=8       ,				lowerCamelCase		:			Any=10       ,				)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    patch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    scope
         _UpperCAmelCase							=    n_targets
         _UpperCAmelCase							=    num_detection_tokens
         # we set the expected sequence length (which is used in several tests)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
         _UpperCAmelCase							=    (image_size[1] // patch_size) * (image_size[0] // patch_size)
         _UpperCAmelCase							=    num_patches + 1 + self.num_detection_tokens


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)

         _UpperCAmelCase							=    None
         if self.use_labels:
                # labels is a list of Dict (each Dict being the labels for a given example in the batch)
                _UpperCAmelCase							=    []
                for i in range(self.batch_size							):
                       _UpperCAmelCase							=    {}
                       _UpperCAmelCase							=    torch.randint(
                           high=self.num_labels       ,				size=(self.n_targets,)       ,				device=lowerCamelCase							)
                       _UpperCAmelCase							=    torch.rand(self.n_targets       ,				4       ,				device=lowerCamelCase							)
                       labels.append(lowerCamelCase							)

         _UpperCAmelCase							=    self.get_config()

         return config, pixel_values, labels


  def  lowerCamelCase       (      self		:			List[str]							)						->	Dict:


         """simple docstring"""
         return YolosConfig(
             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=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				num_detection_tokens=self.num_detection_tokens       ,				num_labels=self.num_labels       ,				)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			str							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    YolosModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,				(self.batch_size, self.expected_seq_len, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Tuple							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    YolosForObjectDetection(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    model(pixel_values=lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase							)

         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
         self.parent.assertEqual(result.pred_boxes.shape       ,				(self.batch_size, self.num_detection_tokens, 4)							)

         _UpperCAmelCase							=    model(pixel_values=lowerCamelCase       ,				labels=lowerCamelCase							)

         self.parent.assertEqual(result.loss.shape       ,				()							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
         self.parent.assertEqual(result.pred_boxes.shape       ,				(self.batch_size, self.num_detection_tokens, 4)							)





  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


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



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
  _lowerCamelCase    =  (
      {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
  )

  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Tuple=False							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    super()._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)

         if return_labels:
                if model_class.__name__ == "YolosForObjectDetection":
                       _UpperCAmelCase							=    []
                       for i in range(self.model_tester.batch_size							):
                              _UpperCAmelCase							=    {}
                              _UpperCAmelCase							=    torch.ones(
                                  size=(self.model_tester.n_targets,)       ,				device=lowerCamelCase       ,				dtype=torch.long							)
                              _UpperCAmelCase							=    torch.ones(
                                  self.model_tester.n_targets       ,				4       ,				device=lowerCamelCase       ,				dtype=torch.float							)
                              labels.append(lowerCamelCase							)
                       _UpperCAmelCase							=    labels

         return inputs_dict


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    YolosModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


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


         """simple docstring"""
         self.config_tester.run_common_tests()


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


         """simple docstring"""
         # YOLOS does not use inputs_embeds
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

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


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase							=    ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			str							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    True

         # in YOLOS, the seq_len is different
         _UpperCAmelCase							=    self.model_tester.expected_seq_len
         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    False
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()
                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)
                _UpperCAmelCase							=    outputs.attentions
                self.assertEqual(len(lowerCamelCase							)       ,				self.model_tester.num_hidden_layers							)

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()
                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)
                _UpperCAmelCase							=    outputs.attentions
                self.assertEqual(len(lowerCamelCase							)       ,				self.model_tester.num_hidden_layers							)

                self.assertListEqual(
                    list(attentions[0].shape[-3:]							)       ,				[self.model_tester.num_attention_heads, seq_len, seq_len]       ,				)
                _UpperCAmelCase							=    len(lowerCamelCase							)

                # Check attention is always last and order is fine
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()
                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)

                _UpperCAmelCase							=    1
                self.assertEqual(out_len + added_hidden_states       ,				len(lowerCamelCase							)							)

                _UpperCAmelCase							=    outputs.attentions

                self.assertEqual(len(lowerCamelCase							)       ,				self.model_tester.num_hidden_layers							)
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]							)       ,				[self.model_tester.num_attention_heads, seq_len, seq_len]       ,				)


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


         """simple docstring"""
         def check_hidden_states_output(lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Dict							):
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()

                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)

                _UpperCAmelCase							=    outputs.hidden_states

                _UpperCAmelCase							=    getattr(
                    self.model_tester       ,				"""expected_num_hidden_layers"""       ,				self.model_tester.num_hidden_layers + 1							)
                self.assertEqual(len(lowerCamelCase							)       ,				lowerCamelCase							)

                # YOLOS has a different seq_length
                _UpperCAmelCase							=    self.model_tester.expected_seq_len

                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]							)       ,				[seq_length, self.model_tester.hidden_size]       ,				)

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    True
                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

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

                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_object_detection(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			str							)						->	Dict:


         """simple docstring"""
         for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    YolosModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						Union[str, Any]:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image





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

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""							) if is_vision_available() else None





  @slow
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""							).to(lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(inputs.pixel_values							)

         # verify outputs
         _UpperCAmelCase							=    torch.Size((1, 100, 92)							)
         self.assertEqual(outputs.logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor(
             [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]       ,				device=lowerCamelCase       ,				)
         _UpperCAmelCase							=    torch.tensor(
             [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]       ,				device=lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)
         self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)

         # verify postprocessing
         _UpperCAmelCase							=    image_processor.post_process_object_detection(
             lowerCamelCase       ,				threshold=0.3       ,				target_sizes=[image.size[::-1]]							)[0]
         _UpperCAmelCase							=    torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]							).to(lowerCamelCase							)
         _UpperCAmelCase							=    [75, 75, 17, 63, 17]
         _UpperCAmelCase							=    torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]							).to(lowerCamelCase							)

         self.assertEqual(len(results["""scores"""]							)       ,				5							)
         self.assertTrue(torch.allclose(results["""scores"""]       ,				lowerCamelCase       ,				atol=1E-4							)							)
         self.assertSequenceEqual(results["""labels"""].tolist()       ,				lowerCamelCase							)
         self.assertTrue(torch.allclose(results["""boxes"""][0, :]       ,				lowerCamelCase							)							)







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


_UpperCamelCase			:	Any								=logging.get_logger(__name__)


if is_vision_available():
	import PIL






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Union[str, Any]     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BICUBIC       ,A__ = True       ,A__ = None       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = None       ,A__ = None       ,A__ = True       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				List[str]   =      size if size is not None else {'''shortest_edge''': 224}
						_A	:				int   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				Optional[int]   =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
						_A	:				Dict   =      get_size_dict(A__       ,default_to_square=A__       ,param_name='''crop_size'''		)

						_A	:				List[str]   =      do_resize
						_A	:				List[str]   =      size
						_A	:				Tuple   =      resample
						_A	:				List[Any]   =      do_center_crop
						_A	:				Dict   =      crop_size
						_A	:				Any   =      do_rescale
						_A	:				Optional[Any]   =      rescale_factor
						_A	:				Dict   =      do_normalize
						_A	:				Optional[int]   =      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	:				Any   =      do_convert_rgb







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BICUBIC       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      get_size_dict(A__       ,default_to_square=A__		)
						if "shortest_edge" not in size:
										raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
						_A	:				Any   =      get_resize_output_image_size(A__       ,size=size['''shortest_edge''']       ,default_to_square=A__		)
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						_A	:				Union[str, Any]   =      get_size_dict(A__		)
						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(A__       ,size=(size['''height'''], size['''width'''])       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				str   =      do_resize if do_resize is not None else self.do_resize
						_A	:				List[str]   =      size if size is not None else self.size
						_A	:				List[str]   =      get_size_dict(A__       ,param_name='''size'''       ,default_to_square=A__		)
						_A	:				Optional[Any]   =      resample if resample is not None else self.resample
						_A	:				int   =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A	:				Any   =      crop_size if crop_size is not None else self.crop_size
						_A	:				int   =      get_size_dict(A__       ,param_name='''crop_size'''       ,default_to_square=A__		)
						_A	:				int   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				List[str]   =      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	:				Union[str, Any]   =      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	:				Optional[Any]   =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						_A	:				Optional[Any]   =      make_list_of_images(A__		)

						if not valid_images(A__		):
										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	:				int   =      [convert_to_rgb(A__		) for image in images]

						# All transformations expect numpy arrays.
						_A	:				int   =      [to_numpy_array(A__		) for image in images]

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

						if do_center_crop:
										_A	:				Union[str, Any]   =      [self.center_crop(image=A__       ,size=A__		) for image in images]

						if do_rescale:
										_A	:				Tuple   =      [self.rescale(image=A__       ,scale=A__		) for image in images]

						if do_normalize:
										_A	:				Dict   =      [self.normalize(image=A__       ,mean=A__       ,std=A__		) for image in images]

						_A	:				Dict   =      [to_channel_dimension_format(A__       ,A__		) for image in images]

						_A	:				Dict   =      {'''pixel_values''': images}
						return BatchFeature(data=A__       ,tensor_type=A__		)



