






"""simple docstring"""






import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 torch import nn

  from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
  from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  List[str]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Tuple=3						,	lowercase_     :  Union[str, Any]=32						,	lowercase_     :  Optional[Any]=3						,	lowercase_     :  Tuple=10						,	lowercase_     :  Dict=[8, 16, 32, 64]						,	lowercase_     :  List[Any]=[1, 1, 2, 1]						,	lowercase_     :  int=True						,	lowercase_     :  Any=True						,	lowercase_     :  List[str]="relu"						,	lowercase_     :  Dict=3						,	lowercase_     :  List[str]=None						,	lowercase_     :  Dict=["stage2", "stage3", "stage4"]						,	lowercase_     :  Optional[Any]=[2, 3, 4]						,	lowercase_     :  Any=1						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		parent
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		num_channels
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		embeddings_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		hidden_sizes
          SCREAMING_SNAKE_CASE_      :  str    						=		depths
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		is_training
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		num_labels
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scope
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		len(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		out_features
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		out_indices
          SCREAMING_SNAKE_CASE_      :  str    						=		num_groups
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

          SCREAMING_SNAKE_CASE_      :  str    						=		self.get_config()

          return config, pixel_values, labels
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          return BitConfig(
              num_channels=self.num_channels						,	embeddings_size=self.embeddings_size						,	hidden_sizes=self.hidden_sizes						,	depths=self.depths						,	hidden_act=self.hidden_act						,	num_labels=self.num_labels						,	out_features=self.out_features						,	out_indices=self.out_indices						,	num_groups=self.num_groups						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  List[Any]						,	lowercase_     :  str						,	lowercase_     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		BitModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(lowercase_)
          self.parent.assertEqual(
              result.last_hidden_state.shape						,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.num_labels
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		BitForImageClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model(lowercase_						,	labels=lowercase_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels))
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  List[Any]						,	lowercase_     :  str						,	lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		BitBackbone(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		model(lowercase_)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps)						,	len(config.out_features))
          self.parent.assertListEqual(list(result.feature_maps[0].shape)						,	[self.batch_size, self.hidden_sizes[1], 4, 4])

          # verify channels
          self.parent.assertEqual(len(model.channels)						,	len(config.out_features))
          self.parent.assertListEqual(model.channels						,	config.hidden_sizes[1:])

          # verify backbone works with out_features=None
          SCREAMING_SNAKE_CASE_      :  Dict    						=		None
          SCREAMING_SNAKE_CASE_      :  str    						=		BitBackbone(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Any    						=		model(lowercase_)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps)						,	1)
          self.parent.assertListEqual(list(result.feature_maps[0].shape)						,	[self.batch_size, self.hidden_sizes[-1], 1, 1])

          # verify channels
          self.parent.assertEqual(len(model.channels)						,	1)
          self.parent.assertListEqual(model.channels						,	[config.hidden_sizes[-1]])



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Any    						=		config_and_inputs
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{'''pixel_values''': pixel_values}
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      __UpperCamelCase	 =  (
          {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
          if is_torch_available()
          else {}
      )

      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		BitModelTester(self)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		ConfigTester(self						,	config_class=lowercase_						,	has_text_modality=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          return
      @unittest.skip(reason='''Bit does not output attentions''')
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Bit does not use inputs_embeds''')
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Bit does not support input and output embeddings''')
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE_      :  int    						=		['''pixel_values''']
              self.assertListEqual(arg_names[:1]						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


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

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		model_class(config=lowercase_)
              for name, module in model.named_modules():
                  if isinstance(lowercase_						,	(nn.BatchNormad, nn.GroupNorm)):
                      self.assertTrue(
                          torch.all(module.weight == 1)						,	msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,	)
                      self.assertTrue(
                          torch.all(module.bias == 0)						,	msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          def check_hidden_states_output(lowercase_     :  Tuple						,	lowercase_     :  List[Any]						,	lowercase_     :  List[str]):
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.eval()

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

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.model_tester.num_stages
              self.assertEqual(len(lowercase_)						,	expected_num_stages + 1)

              # Bit's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:])						,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,	)

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_      :  Any    						=		['''preactivation''', '''bottleneck''']
          for model_class in self.all_model_classes:
              for layer_type in layers_type:
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		layer_type
                  SCREAMING_SNAKE_CASE_      :  str    						=		True
                  check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		True

                  check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)
      @unittest.skip(reason='''Bit does not use feedforward chunking''')
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_)



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		BitModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)



def 					_A			() ->       Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
    return image




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






      '''simple docstring'''

      @cached_property
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          return (
              BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
          )



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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase_)

          SCREAMING_SNAKE_CASE_      :  str    						=		self.default_image_processor
          SCREAMING_SNAKE_CASE_      :  Dict    						=		prepare_img()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		image_processor(images=lowercase_						,	return_tensors='''pt''').to(lowercase_)

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

          # verify the logits
          SCREAMING_SNAKE_CASE_      :  int    						=		torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]).to(lowercase_)

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




@require_torch
class 							lowerCAmelCase__				(			UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (BitBackbone,) if is_torch_available() else ()
      __UpperCamelCase	 =  BitConfig

      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		BitModelTester(self)










"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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


if is_vision_available():
  import PIL

UpperCAmelCase_   : int   			= logging.get_logger(__name__)



def 					_A			(__a							) ->       List[List[ImageInput]]:
    """simple docstring"""






    if isinstance(__a    ,				(list, tuple)							) and isinstance(videos[0]    ,				(list, tuple)							) and is_valid_image(videos[0][0]							):
        return videos

    elif isinstance(__a    ,				(list, tuple)							) and is_valid_image(videos[0]							):
        return [videos]

    elif is_valid_image(__a							):
        return [[videos]]

    raise ValueError(f'Could not make batched video from {videos}'							)





class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["pixel_values"]
      def __init__(						self     :  Dict						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = PILImageResampling.BILINEAR						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = True						,	lowercase_     :  Union[int, float] = 1 / 255						,	lowercase_     :  bool = True						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	**lowercase_     :  Dict						,	):




          '''simple docstring'''


          super().__init__(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		size if size is not None else {'''shortest_edge''': 256}
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          SCREAMING_SNAKE_CASE_      :  Dict    						=		get_size_dict(lowercase_						,	param_name='''crop_size''')

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		do_resize
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		do_center_crop
          SCREAMING_SNAKE_CASE_      :  Dict    						=		crop_size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		resample
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		do_rescale
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		rescale_factor
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		offset
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		do_normalize
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		image_std if image_std is not None else IMAGENET_STANDARD_STD
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  PILImageResampling = PILImageResampling.BILINEAR						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Any						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          if "shortest_edge" in size:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		get_resize_output_image_size(lowercase_						,	size['''shortest_edge''']						,	default_to_square=lowercase_)
          elif "height" in size and "width" in size:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(size['''height'''], size['''width'''])
          else:
              raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}')
          return resize(lowercase_						,	size=lowercase_						,	resample=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  List[Any]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		get_size_dict(lowercase_)
          if "height" not in size or "width" not in size:
              raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}')
          return center_crop(lowercase_						,	size=(size['''height'''], size['''width'''])						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[int, float]						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Optional[int]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image.astype(np.floataa)
          if offset:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		image - (scale / 2)
          return rescale(lowercase_						,	scale=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  List[str]						,	):




          '''simple docstring'''


          return normalize(lowercase_						,	mean=lowercase_						,	std=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  ImageInput						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = None						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = None						,	lowercase_     :  float = None						,	lowercase_     :  bool = None						,	lowercase_     :  bool = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[ChannelDimension] = ChannelDimension.FIRST						,	):




          '''simple docstring'''


          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.''')

          if offset and not do_rescale:
              raise ValueError('''For offset, do_rescale must also be set to True.''')

          # All transformations expect numpy arrays.
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		to_numpy_array(lowercase_)

          if do_resize:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.resize(image=lowercase_						,	size=lowercase_						,	resample=lowercase_)

          if do_center_crop:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.center_crop(lowercase_						,	size=lowercase_)

          if do_rescale:
              SCREAMING_SNAKE_CASE_      :  int    						=		self.rescale(image=lowercase_						,	scale=lowercase_						,	offset=lowercase_)

          if do_normalize:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.normalize(image=lowercase_						,	mean=lowercase_						,	std=lowercase_)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		to_channel_dimension_format(lowercase_						,	lowercase_)
          return image



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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		do_resize if do_resize is not None else self.do_resize
          SCREAMING_SNAKE_CASE_      :  int    						=		resample if resample is not None else self.resample
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		do_center_crop if do_center_crop is not None else self.do_center_crop
          SCREAMING_SNAKE_CASE_      :  Dict    						=		do_rescale if do_rescale is not None else self.do_rescale
          SCREAMING_SNAKE_CASE_      :  Dict    						=		rescale_factor if rescale_factor is not None else self.rescale_factor
          SCREAMING_SNAKE_CASE_      :  Dict    						=		offset if offset is not None else self.offset
          SCREAMING_SNAKE_CASE_      :  str    						=		do_normalize if do_normalize is not None else self.do_normalize
          SCREAMING_SNAKE_CASE_      :  Dict    						=		image_mean if image_mean is not None else self.image_mean
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		image_std if image_std is not None else self.image_std

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		size if size is not None else self.size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		crop_size if crop_size is not None else self.crop_size
          SCREAMING_SNAKE_CASE_      :  Dict    						=		get_size_dict(lowercase_						,	param_name='''crop_size''')

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

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		make_batched(lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[
              [
                  self._preprocess_image(
                      image=lowercase_						,	do_resize=lowercase_						,	size=lowercase_						,	resample=lowercase_						,	do_center_crop=lowercase_						,	crop_size=lowercase_						,	do_rescale=lowercase_						,	rescale_factor=lowercase_						,	offset=lowercase_						,	do_normalize=lowercase_						,	image_mean=lowercase_						,	image_std=lowercase_						,	data_format=lowercase_						,	)
                  for img in video
              ]
              for video in videos
          ]

          SCREAMING_SNAKE_CASE_      :  int    						=		{'''pixel_values''': videos}
          return BatchFeature(data=lowercase_						,	tensor_type=lowercase_)


