

"""simple docstring"""




import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, 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, _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 DinatBackbone, DinatForImageClassification, DinatModel
      from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor







class        __lowercase     :

  def __init__(					self						:     str   ,A						:     Dict   ,A						:     Optional[Any]=13   ,A						:     str=64   ,A						:     List[str]=4   ,A						:     List[Any]=3   ,A						:     Union[str, Any]=16   ,A						:     Union[str, Any]=[1, 2, 1]   ,A						:     List[str]=[2, 4, 8]   ,A						:     int=3   ,A						:     Tuple=[[3], [1, 2], [1]]   ,A						:     Optional[Any]=2.0   ,A						:     str=True   ,A						:     Union[str, Any]=0.0   ,A						:     Optional[Any]=0.0   ,A						:     Tuple=0.1   ,A						:     List[Any]="gelu"   ,A						:     Any=True   ,A						:     int=0.0_2   ,A						:     Optional[int]=1e-5   ,A						:     Any=True   ,A						:     str=None   ,A						:     Union[str, Any]=True   ,A						:     Union[str, Any]=10   ,A						:     Tuple=["stage1", "stage2"]   ,A						:     List[str]=[1, 2]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   parent
   UpperCAmelCase__   :					Optional[int]   				=   batch_size
   UpperCAmelCase__   :					Optional[int]   				=   image_size
   UpperCAmelCase__   :					Any   				=   patch_size
   UpperCAmelCase__   :					List[Any]   				=   num_channels
   UpperCAmelCase__   :					List[Any]   				=   embed_dim
   UpperCAmelCase__   :					Union[str, Any]   				=   depths
   UpperCAmelCase__   :					int   				=   num_heads
   UpperCAmelCase__   :					Optional[Any]   				=   kernel_size
   UpperCAmelCase__   :					str   				=   dilations
   UpperCAmelCase__   :					List[str]   				=   mlp_ratio
   UpperCAmelCase__   :					int   				=   qkv_bias
   UpperCAmelCase__   :					str   				=   hidden_dropout_prob
   UpperCAmelCase__   :					Union[str, Any]   				=   attention_probs_dropout_prob
   UpperCAmelCase__   :					Union[str, Any]   				=   drop_path_rate
   UpperCAmelCase__   :					int   				=   hidden_act
   UpperCAmelCase__   :					Union[str, Any]   				=   patch_norm
   UpperCAmelCase__   :					Union[str, Any]   				=   layer_norm_eps
   UpperCAmelCase__   :					Dict   				=   initializer_range
   UpperCAmelCase__   :					Optional[int]   				=   is_training
   UpperCAmelCase__   :					Tuple   				=   scope
   UpperCAmelCase__   :					Dict   				=   use_labels
   UpperCAmelCase__   :					List[Any]   				=   num_labels
   UpperCAmelCase__   :					Optional[int]   				=   out_features
   UpperCAmelCase__   :					Optional[int]   				=   out_indices

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






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

   UpperCAmelCase__   :					Tuple   				=   None
   if self.use_labels:
    UpperCAmelCase__   :					Optional[int]   				=   ids_tensor([self.batch_size]   ,self.num_labels      )

   UpperCAmelCase__   :					Optional[Any]   				=   self.get_config()

   return config, pixel_values, labels

  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   return DinatConfig(
       num_labels=self.num_labels   ,image_size=self.image_size   ,patch_size=self.patch_size   ,num_channels=self.num_channels   ,embed_dim=self.embed_dim   ,depths=self.depths   ,num_heads=self.num_heads   ,kernel_size=self.kernel_size   ,dilations=self.dilations   ,mlp_ratio=self.mlp_ratio   ,qkv_bias=self.qkv_bias   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,drop_path_rate=self.drop_path_rate   ,hidden_act=self.hidden_act   ,patch_norm=self.patch_norm   ,layer_norm_eps=self.layer_norm_eps   ,initializer_range=self.initializer_range   ,out_features=self.out_features   ,out_indices=self.out_indices   ,)

  def 				__lowercase    (					self						:     Union[str, Any]   ,A						:     int   ,A						:     Union[str, Any]   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   DinatModel(config=_UpperCAmelCase      )
   model.to(_UpperCAmelCase      )
   model.eval()
   UpperCAmelCase__   :					List[Any]   				=   model(_UpperCAmelCase      )

   UpperCAmelCase__   :					Union[str, Any]   				=   (config.image_size // config.patch_size) // (2 ** (len(config.depths      ) - 1))
   UpperCAmelCase__   :					Any   				=   int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

   self.parent.assertEqual(
       result.last_hidden_state.shape   ,(self.batch_size, expected_height, expected_width, expected_dim)      )

  def 				__lowercase    (					self						:     Any   ,A						:     Any   ,A						:     int   ,A						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   DinatForImageClassification(_UpperCAmelCase      )
   model.to(_UpperCAmelCase      )
   model.eval()
   UpperCAmelCase__   :					Union[str, Any]   				=   model(_UpperCAmelCase   ,labels=_UpperCAmelCase      )
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)      )

   # test greyscale images
   UpperCAmelCase__   :					List[Any]   				=   1
   UpperCAmelCase__   :					str   				=   DinatForImageClassification(_UpperCAmelCase      )
   model.to(_UpperCAmelCase      )
   model.eval()

   UpperCAmelCase__   :					Union[str, Any]   				=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
   UpperCAmelCase__   :					Dict   				=   model(_UpperCAmelCase      )
   self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)      )

  def 				__lowercase    (					self						:     Dict   ,A						:     Dict   ,A						:     Tuple   ,A						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   DinatBackbone(config=_UpperCAmelCase      )
   model.to(_UpperCAmelCase      )
   model.eval()
   UpperCAmelCase__   :					Tuple   				=   model(_UpperCAmelCase      )

   # verify hidden states
   self.parent.assertEqual(len(result.feature_maps      )   ,len(config.out_features      )      )
   self.parent.assertListEqual(list(result.feature_maps[0].shape      )   ,[self.batch_size, model.channels[0], 16, 16]      )

   # verify channels
   self.parent.assertEqual(len(model.channels      )   ,len(config.out_features      )      )

   # verify backbone works with out_features=None
   UpperCAmelCase__   :					str   				=   None
   UpperCAmelCase__   :					List[str]   				=   DinatBackbone(config=_UpperCAmelCase      )
   model.to(_UpperCAmelCase      )
   model.eval()
   UpperCAmelCase__   :					int   				=   model(_UpperCAmelCase      )

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps      )   ,1      )
   self.parent.assertListEqual(list(result.feature_maps[0].shape      )   ,[self.batch_size, model.channels[-1], 4, 4]      )

   # verify channels
   self.parent.assertEqual(len(model.channels      )   ,1      )




  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   self.prepare_config_and_inputs()
   UpperCAmelCase__   :					List[str]   				=   config_and_inputs
   UpperCAmelCase__   :					List[str]   				=   {"pixel_values": pixel_values}
   return config, inputs_dict







@require_natten
@require_torch
class        __lowercase     (      SCREAMING_SNAKE_CASE__		,     SCREAMING_SNAKE_CASE__		,     unittest.TestCase   ):
  snake_case_			    =    (
      (
          DinatModel,
          DinatForImageClassification,
          DinatBackbone,
      )
      if is_torch_available()
      else ()
  )
  snake_case_			    =    (
      {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
      if is_torch_available()
      else {}
  )
  snake_case_			    =    False

  snake_case_			    =    False
  snake_case_			    =    False
  snake_case_			    =    False
  snake_case_			    =    False

  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   DinatModelTester(self      )
   UpperCAmelCase__   :					int   				=   ConfigTester(self   ,config_class=_UpperCAmelCase   ,embed_dim=37      )

  def 				__lowercase    (					self						:     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 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   return

  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase      )

  def 				__lowercase    (					self						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )

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






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_backbone(*_UpperCAmelCase      )

  @unittest.skip(reason="""Dinat does not use inputs_embeds"""      )
  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   pass

  @unittest.skip(reason="""Dinat does not use feedforward chunking"""      )
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   pass

  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__   :					Dict   				=   model_class(_UpperCAmelCase      )
    self.assertIsInstance(model.get_input_embeddings()   ,(nn.Module)      )
    UpperCAmelCase__   :					Optional[int]   				=   model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(_UpperCAmelCase   ,nn.Linear      )      )

  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   self.model_tester.prepare_config_and_inputs_for_common()

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

    UpperCAmelCase__   :					Optional[Any]   				=   ["pixel_values"]
    self.assertListEqual(arg_names[:1]   ,_UpperCAmelCase      )

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






   '''simple docstring'''
   self.skipTest("""Dinat's attention operation is handled entirely by NATTEN."""      )

  def 				__lowercase    (					self						:     Any   ,A						:     Union[str, Any]   ,A						:     str   ,A						:     List[Any]   ,A						:     int      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   model_class(_UpperCAmelCase      )
   model.to(_UpperCAmelCase      )
   model.eval()

   with torch.no_grad():
    UpperCAmelCase__   :					Optional[int]   				=   model(**self._prepare_for_class(_UpperCAmelCase   ,_UpperCAmelCase      )      )

   UpperCAmelCase__   :					List[Any]   				=   outputs.hidden_states

   UpperCAmelCase__   :					Dict   				=   getattr(
       self.model_tester   ,"""expected_num_hidden_layers"""   ,len(self.model_tester.depths      ) + 1      )
   self.assertEqual(len(_UpperCAmelCase      )   ,_UpperCAmelCase      )

   # Dinat has a different seq_length
   UpperCAmelCase__   :					List[str]   				=   (
       config.patch_size
       if isinstance(config.patch_size   ,collections.abc.Iterable      )
       else (config.patch_size, config.patch_size)
   )

   UpperCAmelCase__   :					str   				=   image_size[0] // patch_size[0]
   UpperCAmelCase__   :					Dict   				=   image_size[1] // patch_size[1]

   self.assertListEqual(
       list(hidden_states[0].shape[-3:]      )   ,[height, width, self.model_tester.embed_dim]   ,)

   if model_class.__name__ != "DinatBackbone":
    UpperCAmelCase__   :					Union[str, Any]   				=   outputs.reshaped_hidden_states
    self.assertEqual(len(_UpperCAmelCase      )   ,_UpperCAmelCase      )

    UpperCAmelCase__   :					Tuple   				=   reshaped_hidden_states[0].shape
    UpperCAmelCase__   :					Dict   				=   (
        reshaped_hidden_states[0].view(_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase      ).permute(0   ,2   ,3   ,1      )
    )
    self.assertListEqual(
        list(reshaped_hidden_states.shape[-3:]      )   ,[height, width, self.model_tester.embed_dim]   ,)

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






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__   :					Optional[Any]   				=   (
       self.model_tester.image_size
       if isinstance(self.model_tester.image_size   ,collections.abc.Iterable      )
       else (self.model_tester.image_size, self.model_tester.image_size)
   )

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

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    UpperCAmelCase__   :					List[str]   				=   True

    self.check_hidden_states_output(_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase      )

  @slow
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__   :					Optional[Any]   				=   DinatModel.from_pretrained(_UpperCAmelCase      )
    self.assertIsNotNone(_UpperCAmelCase      )




  def 				__lowercase    (					self						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__   :					Union[str, Any]   				=   _config_zero_init(_UpperCAmelCase      )
   for model_class in self.all_model_classes:
    UpperCAmelCase__   :					List[str]   				=   model_class(config=_UpperCAmelCase      )
    for name, param in model.named_parameters():
     if "embeddings" not in name and 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"   ,)







@require_natten
@require_vision
@require_torch
class        __lowercase     (      unittest.TestCase   ):

  @cached_property
  def 				__lowercase    (					self						:     Optional[int]      ):






   '''simple docstring'''
   return AutoImageProcessor.from_pretrained("""shi-labs/dinat-mini-in1k-224"""      ) if is_vision_available() else None




  @slow
  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   DinatForImageClassification.from_pretrained("""shi-labs/dinat-mini-in1k-224"""      ).to(_UpperCAmelCase      )
   UpperCAmelCase__   :					Union[str, Any]   				=   self.default_image_processor

   UpperCAmelCase__   :					Dict   				=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
   UpperCAmelCase__   :					Tuple   				=   image_processor(images=_UpperCAmelCase   ,return_tensors="""pt"""      ).to(_UpperCAmelCase      )

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__   :					Any   				=   model(**_UpperCAmelCase      )

   # verify the logits
   UpperCAmelCase__   :					Optional[int]   				=   torch.Size((1, 1_000)      )
   self.assertEqual(outputs.logits.shape   ,_UpperCAmelCase      )
   UpperCAmelCase__   :					Optional[Any]   				=   torch.tensor([-0.1_5_4_5, -0.7_6_6_7, 0.4_6_4_2]      ).to(_UpperCAmelCase      )
   self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,_UpperCAmelCase   ,atol=1e-4      )      )







@require_torch
@require_natten
class        __lowercase     (      unittest.TestCase		,     SCREAMING_SNAKE_CASE__   ):
  snake_case_			    =    (DinatBackbone,) if is_torch_available() else ()
  snake_case_			    =    DinatConfig

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






   '''simple docstring'''
   UpperCAmelCase__   :					Union[str, Any]   				=   DinatModelTester(self      )








"""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


__UpperCAmelCase	=			logging.get_logger(__name__)







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    ["""pixel_values"""]

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






   '''simple docstring'''
   super().__init__(**A      )
   UpperCAmelCase__   :					Optional[Any]   				=   size if size is not None else {"""height""": 346, """width""": 346}
   UpperCAmelCase__   :					Union[str, Any]   				=   get_size_dict(A      )
   UpperCAmelCase__   :					Union[str, Any]   				=   crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
   UpperCAmelCase__   :					Optional[Any]   				=   get_size_dict(A   ,param_name="""crop_size"""      )

   UpperCAmelCase__   :					Optional[int]   				=   do_resize
   UpperCAmelCase__   :					Optional[int]   				=   size
   UpperCAmelCase__   :					Optional[int]   				=   resample
   UpperCAmelCase__   :					List[str]   				=   do_center_crop
   UpperCAmelCase__   :					int   				=   crop_size
   UpperCAmelCase__   :					List[Any]   				=   do_rescale
   UpperCAmelCase__   :					List[str]   				=   rescale_factor
   UpperCAmelCase__   :					List[str]   				=   rescale_offset
   UpperCAmelCase__   :					Any   				=   do_normalize
   UpperCAmelCase__   :					Tuple   				=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   UpperCAmelCase__   :					int   				=   image_std if image_std is not None else IMAGENET_STANDARD_STD
   UpperCAmelCase__   :					Optional[int]   				=   include_top

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






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   get_size_dict(A      )
   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(
       A   ,size=(size["""height"""], size["""width"""])   ,resample=A   ,data_format=A   ,**A      )

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






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   get_size_dict(A      )
   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(A   ,size=(size["""height"""], size["""width"""])   ,data_format=A   ,**A      )

  def 				__lowercase    (					self						:     List[Any]   ,A						:     np.ndarray   ,A						:     Union[int, float]   ,A						:     bool = True   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Any   ,):






   '''simple docstring'''
   if offset:
    UpperCAmelCase__   :					Tuple   				=   (image - 1_2_7.5) * scale
    if data_format is not None:
     UpperCAmelCase__   :					Any   				=   to_channel_dimension_format(A   ,A      )
    UpperCAmelCase__   :					Union[str, Any]   				=   rescaled_image.astype(np.floataa      )
   else:
    UpperCAmelCase__   :					List[str]   				=   rescale(A   ,scale=A   ,data_format=A   ,**A      )
   return rescaled_image

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






   '''simple docstring'''
   return normalize(A   ,mean=A   ,std=A   ,data_format=A   ,**A      )




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






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase__   :					Optional[int]   				=   resample if resample is not None else self.resample
   UpperCAmelCase__   :					List[str]   				=   do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase__   :					List[str]   				=   do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase__   :					Any   				=   rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase__   :					str   				=   rescale_offset if rescale_offset is not None else self.rescale_offset
   UpperCAmelCase__   :					Union[str, Any]   				=   do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase__   :					int   				=   image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase__   :					Any   				=   image_std if image_std is not None else self.image_std
   UpperCAmelCase__   :					List[Any]   				=   include_top if include_top is not None else self.include_top

   UpperCAmelCase__   :					List[Any]   				=   size if size is not None else self.size
   UpperCAmelCase__   :					int   				=   get_size_dict(A      )
   UpperCAmelCase__   :					Any   				=   crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase__   :					str   				=   get_size_dict(A   ,param_name="""crop_size"""      )

   UpperCAmelCase__   :					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 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.
   UpperCAmelCase__   :					List[str]   				=   [to_numpy_array(A      ) for image in images]

   if do_resize:
    UpperCAmelCase__   :					str   				=   [self.resize(image=A   ,size=A   ,resample=A      ) for image in images]

   if do_center_crop:
    UpperCAmelCase__   :					Tuple   				=   [self.center_crop(image=A   ,size=A      ) for image in images]

   if do_rescale:
    UpperCAmelCase__   :					int   				=   [self.rescale(image=A   ,scale=A   ,offset=A      ) for image in images]

   if do_normalize:
    UpperCAmelCase__   :					int   				=   [self.normalize(image=A   ,mean=A   ,std=A      ) for image in images]

   if include_top:
    UpperCAmelCase__   :					Dict   				=   [self.normalize(image=A   ,mean=[0, 0, 0]   ,std=A      ) for image in images]

   UpperCAmelCase__   :					Optional[Any]   				=   [to_channel_dimension_format(A   ,A      ) for image in images]

   UpperCAmelCase__   :					Dict   				=   {"""pixel_values""": images}
   return BatchFeature(data=A   ,tensor_type=A      )





