
import collections
import inspect
import unittest

from transformers import SwinConfig
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, _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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
    from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Tuple=13      ,							UpperCamelCase__      :			Any=32      ,							UpperCamelCase__      :			str=2      ,							UpperCamelCase__      :			Optional[int]=3      ,							UpperCamelCase__      :			List[str]=16      ,							UpperCamelCase__      :			Optional[int]=[1, 2, 1]      ,							UpperCamelCase__      :			int=[2, 2, 4]      ,							UpperCamelCase__      :			List[Any]=2      ,							UpperCamelCase__      :			List[Any]=2.0      ,							UpperCamelCase__      :			Dict=True      ,							UpperCamelCase__      :			int=0.0      ,							UpperCamelCase__      :			Any=0.0      ,							UpperCamelCase__      :			List[str]=0.1      ,							UpperCamelCase__      :			List[Any]="gelu"      ,							UpperCamelCase__      :			Optional[Any]=False      ,							UpperCamelCase__      :			List[str]=True      ,							UpperCamelCase__      :			Union[str, Any]=0.02      ,							UpperCamelCase__      :			Optional[Any]=1E-5      ,							UpperCamelCase__      :			Dict=True      ,							UpperCamelCase__      :			Optional[Any]=None      ,							UpperCamelCase__      :			Dict=True      ,							UpperCamelCase__      :			Optional[int]=10      ,							UpperCamelCase__      :			List[str]=8      ,							UpperCamelCase__      :			Tuple=["stage1", "stage2"]      ,							UpperCamelCase__      :			str=[1, 2]      ,							)      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	parent
    __magic_name__    					=	batch_size
    __magic_name__    					=	image_size
    __magic_name__    					=	patch_size
    __magic_name__    					=	num_channels
    __magic_name__    					=	embed_dim
    __magic_name__    					=	depths
    __magic_name__    					=	num_heads
    __magic_name__    					=	window_size
    __magic_name__    					=	mlp_ratio
    __magic_name__    					=	qkv_bias
    __magic_name__    					=	hidden_dropout_prob
    __magic_name__    					=	attention_probs_dropout_prob
    __magic_name__    					=	drop_path_rate
    __magic_name__    					=	hidden_act
    __magic_name__    					=	use_absolute_embeddings
    __magic_name__    					=	patch_norm
    __magic_name__    					=	layer_norm_eps
    __magic_name__    					=	initializer_range
    __magic_name__    					=	is_training
    __magic_name__    					=	scope
    __magic_name__    					=	use_labels
    __magic_name__    					=	type_sequence_label_size
    __magic_name__    					=	encoder_stride
    __magic_name__    					=	out_features
    __magic_name__    					=	out_indices
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

    __magic_name__    					=	None
    if self.use_labels:
      __magic_name__    					=	ids_tensor([self.batch_size]      ,							self.type_sequence_label_size   )

    __magic_name__    					=	self.get_config()

    return config, pixel_values, labels
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Dict:







    """simple docstring"""






    return SwinConfig(
        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      ,							window_size=self.window_size      ,							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      ,							use_absolute_embeddings=self.use_absolute_embeddings      ,							path_norm=self.patch_norm      ,							layer_norm_eps=self.layer_norm_eps      ,							initializer_range=self.initializer_range      ,							encoder_stride=self.encoder_stride      ,							out_features=self.out_features      ,							out_indices=self.out_indices      ,							)
  def 							_lowercase							(			self      :			str      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			List[str]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	SwinModel(config=UpperCamelCase__   )
    model.to(UpperCamelCase__   )
    model.eval()
    __magic_name__    					=	model(UpperCamelCase__   )

    __magic_name__    					=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
    __magic_name__    					=	int(config.embed_dim * 2 ** (len(config.depths   ) - 1)   )

    self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, expected_seq_len, expected_dim)   )
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			List[Any]   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__    					=	SwinBackbone(config=UpperCamelCase__   )
    model.to(UpperCamelCase__   )
    model.eval()
    __magic_name__    					=	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
    __magic_name__    					=	None
    __magic_name__    					=	SwinBackbone(config=UpperCamelCase__   )
    model.to(UpperCamelCase__   )
    model.eval()
    __magic_name__    					=	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      :			List[str]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			Optional[int]   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	SwinForMaskedImageModeling(config=UpperCamelCase__   )
    model.to(UpperCamelCase__   )
    model.eval()
    __magic_name__    					=	model(UpperCamelCase__   )
    self.parent.assertEqual(
        result.logits.shape      ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

    # test greyscale images
    __magic_name__    					=	1
    __magic_name__    					=	SwinForMaskedImageModeling(UpperCamelCase__   )
    model.to(UpperCamelCase__   )
    model.eval()

    __magic_name__    					=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
    __magic_name__    					=	model(UpperCamelCase__   )
    self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, 1, self.image_size, self.image_size)   )
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	self.type_sequence_label_size
    __magic_name__    					=	SwinForImageClassification(UpperCamelCase__   )
    model.to(UpperCamelCase__   )
    model.eval()
    __magic_name__    					=	model(UpperCamelCase__      ,							labels=UpperCamelCase__   )
    self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)   )

    # test greyscale images
    __magic_name__    					=	1
    __magic_name__    					=	SwinForImageClassification(UpperCamelCase__   )
    model.to(UpperCamelCase__   )
    model.eval()

    __magic_name__    					=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
    __magic_name__    					=	model(UpperCamelCase__   )
    self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)   )





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







    """simple docstring"""






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







@require_torch
class 	UpperCAmelCase_      (			_A  ,	_A  ,	unittest.TestCase					):


  '''simple docstring'''







  a__   	=		(
      (
          SwinModel,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  a__   	=		(
      {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
      if is_torch_available()
      else {}
  )
  a__   	=		True

  a__   	=		False
  a__   	=		False
  a__   	=		False
  def 							_lowercase							(			self      :			List[str]   )      ->     Tuple:







    """simple docstring"""






    __magic_name__    					=	SwinModelTester(self   )
    __magic_name__    					=	ConfigTester(self      ,							config_class=UpperCamelCase__      ,							embed_dim=37   )
  def 							_lowercase							(			self      :			str   )      ->     Optional[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      :			List[Any]   )      ->     Dict:







    """simple docstring"""






    return
  def 							_lowercase							(			self      :			List[Any]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__   )
  @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""   )
  def 							_lowercase							(			self      :			List[Any]   )      ->     int:







    """simple docstring"""






    pass
  def 							_lowercase							(			self      :			List[str]   )      ->     int:







    """simple docstring"""






    super().test_training_gradient_checkpointing()
  def 							_lowercase							(			self      :			Optional[int]   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*UpperCamelCase__   )
  def 							_lowercase							(			self      :			int   )      ->     str:







    """simple docstring"""






    __magic_name__    					=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__   )
  def 							_lowercase							(			self      :			str   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__    					=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__   )
  @unittest.skip(reason="""Swin does not use inputs_embeds"""   )
  def 							_lowercase							(			self      :			Dict   )      ->     Optional[Any]:







    """simple docstring"""






    pass
  @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""   )
  def 							_lowercase							(			self      :			str   )      ->     Tuple:







    """simple docstring"""






    pass
  def 							_lowercase							(			self      :			str   )      ->     str:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__    					=	model_class(UpperCamelCase__   )
      self.assertIsInstance(model.get_input_embeddings()      ,							(nn.Module)   )
      __magic_name__    					=	model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(UpperCamelCase__      ,							nn.Linear   )   )
  def 							_lowercase							(			self      :			str   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

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

      __magic_name__    					=	["""pixel_values"""]
      self.assertListEqual(arg_names[:1]      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[str]   )      ->     Dict:







    """simple docstring"""






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

    for model_class in self.all_model_classes:
      __magic_name__    					=	True
      __magic_name__    					=	False
      __magic_name__    					=	True
      __magic_name__    					=	model_class(UpperCamelCase__   )
      model.to(UpperCamelCase__   )
      model.eval()
      with torch.no_grad():
        __magic_name__    					=	model(**self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )   )
      __magic_name__    					=	outputs.attentions
      __magic_name__    					=	len(self.model_tester.depths   )
      self.assertEqual(len(UpperCamelCase__   )      ,							UpperCamelCase__   )

      # check that output_attentions also work using config
      del inputs_dict["output_attentions"]
      __magic_name__    					=	True
      __magic_name__    					=	config.window_size**2
      __magic_name__    					=	model_class(UpperCamelCase__   )
      model.to(UpperCamelCase__   )
      model.eval()
      with torch.no_grad():
        __magic_name__    					=	model(**self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )   )
      __magic_name__    					=	outputs.attentions
      self.assertEqual(len(UpperCamelCase__   )      ,							UpperCamelCase__   )

      self.assertListEqual(
          list(attentions[0].shape[-3:]   )      ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,							)
      __magic_name__    					=	len(UpperCamelCase__   )

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

      # also another +1 for reshaped_hidden_states
      __magic_name__    					=	1 if model_class.__name__ == """SwinBackbone""" else 2
      self.assertEqual(out_len + added_hidden_states      ,							len(UpperCamelCase__   )   )

      __magic_name__    					=	outputs.attentions

      self.assertEqual(len(UpperCamelCase__   )      ,							UpperCamelCase__   )

      self.assertListEqual(
          list(self_attentions[0].shape[-3:]   )      ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,							)
  def 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Union[str, Any]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	model_class(UpperCamelCase__   )
    model.to(UpperCamelCase__   )
    model.eval()

    with torch.no_grad():
      __magic_name__    					=	model(**self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__   )   )

    __magic_name__    					=	outputs.hidden_states

    __magic_name__    					=	getattr(
        self.model_tester      ,							"""expected_num_hidden_layers"""      ,							len(self.model_tester.depths   ) + 1   )
    self.assertEqual(len(UpperCamelCase__   )      ,							UpperCamelCase__   )

    # Swin has a different seq_length
    __magic_name__    					=	(
        config.patch_size
        if isinstance(config.patch_size      ,							collections.abc.Iterable   )
        else (config.patch_size, config.patch_size)
    )

    __magic_name__    					=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

    if not model_class.__name__ == "SwinBackbone":
      __magic_name__    					=	outputs.reshaped_hidden_states
      self.assertEqual(len(UpperCamelCase__   )      ,							UpperCamelCase__   )

      __magic_name__					,   __magic_name__					,   __magic_name__					,   __magic_name__    					=	reshaped_hidden_states[0].shape
      __magic_name__    					=	(
          reshaped_hidden_states[0].view(UpperCamelCase__      ,							UpperCamelCase__      ,							height * width   ).permute(0      ,							2      ,							1   )
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]   )      ,							[num_patches, self.model_tester.embed_dim]      ,							)
  def 							_lowercase							(			self      :			Dict   )      ->     List[str]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

    __magic_name__    					=	(
        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:
      __magic_name__    					=	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"]
      __magic_name__    					=	True

      self.check_hidden_states_output(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[int]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__    					=	3

    __magic_name__    					=	(
        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)
    )
    __magic_name__    					=	(
        config.patch_size
        if isinstance(config.patch_size      ,							collections.abc.Iterable   )
        else (config.patch_size, config.patch_size)
    )

    __magic_name__    					=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    __magic_name__    					=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
      __magic_name__    					=	True
      self.check_hidden_states_output(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							(padded_height, padded_width)   )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      __magic_name__    					=	True
      self.check_hidden_states_output(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							(padded_height, padded_width)   )
  @slow
  def 							_lowercase							(			self      :			Any   )      ->     Optional[int]:







    """simple docstring"""






    for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __magic_name__    					=	SwinModel.from_pretrained(UpperCamelCase__   )
      self.assertIsNotNone(UpperCamelCase__   )





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







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

    __magic_name__    					=	_config_zero_init(UpperCamelCase__   )
    for model_class in self.all_model_classes:
      __magic_name__    					=	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_vision
@require_torch
class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  @cached_property
  def 							_lowercase							(			self      :			Tuple   )      ->     str:







    """simple docstring"""






    return (
        AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""   )
        if is_vision_available()
        else None
    )





  @slow
  def 							_lowercase							(			self      :			List[Any]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""   ).to(UpperCamelCase__   )
    __magic_name__    					=	self.default_image_processor

    __magic_name__    					=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
    __magic_name__    					=	image_processor(images=UpperCamelCase__      ,							return_tensors="""pt"""   ).to(UpperCamelCase__   )

    # forward pass
    with torch.no_grad():
      __magic_name__    					=	model(**UpperCamelCase__   )

    # verify the logits
    __magic_name__    					=	torch.Size((1, 1000)   )
    self.assertEqual(outputs.logits.shape      ,							UpperCamelCase__   )
    __magic_name__    					=	torch.tensor([-0.0948, -0.6454, -0.0921]   ).to(UpperCamelCase__   )
    self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							UpperCamelCase__      ,							atol=1E-4   )   )







@require_torch
class 	UpperCAmelCase_      (			unittest.TestCase  ,	_A					):


  '''simple docstring'''







  a__   	=		(SwinBackbone,) if is_torch_available() else ()
  a__   	=		SwinConfig
  def 							_lowercase							(			self      :			Tuple   )      ->     str:







    """simple docstring"""






    __magic_name__    					=	SwinModelTester(self   )



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 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, logging


__lowerCAmelCase      :					str   	=       logging.get_logger(__name__)




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""pixel_values"""]
  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Dict[str, int]] = None      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[int, float] = 1 / 255      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							**UpperCamelCase__      :			Optional[Any]      ,							)      ->     None:







    """simple docstring"""






    super().__init__(**UpperCamelCase__   )
    __magic_name__    					=	size if size is not None else {"""height""": 224, """width""": 224}
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )
    __magic_name__    					=	do_resize
    __magic_name__    					=	do_rescale
    __magic_name__    					=	do_normalize
    __magic_name__    					=	size
    __magic_name__    					=	resample
    __magic_name__    					=	rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __magic_name__    					=	image_std if image_std is not None else IMAGENET_STANDARD_STD
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			Dict[str, int]      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Optional[int]      ,							)      ->     np.ndarray:







    """simple docstring"""






    __magic_name__    					=	get_size_dict(UpperCamelCase__   )
    if "height" not in size or "width" not in size:
      raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''   )
    return resize(
        UpperCamelCase__      ,							size=(size["""height"""], size["""width"""])      ,							resample=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			float      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Union[str, Any]   )      ->     np.ndarray:







    """simple docstring"""






    return rescale(UpperCamelCase__      ,							scale=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			Union[float, List[float]]      ,							UpperCamelCase__      :			Union[float, List[float]]      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     np.ndarray:







    """simple docstring"""






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





  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			ImageInput      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Dict[str, int] = None      ,							UpperCamelCase__      :			PILImageResampling = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[float] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__      :			Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**UpperCamelCase__      :			List[str]      ,							)      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	do_resize if do_resize is not None else self.do_resize
    __magic_name__    					=	do_rescale if do_rescale is not None else self.do_rescale
    __magic_name__    					=	do_normalize if do_normalize is not None else self.do_normalize
    __magic_name__    					=	resample if resample is not None else self.resample
    __magic_name__    					=	rescale_factor if rescale_factor is not None else self.rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else self.image_mean
    __magic_name__    					=	image_std if image_std is not None else self.image_std

    __magic_name__    					=	size if size is not None else self.size
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )

    __magic_name__    					=	make_list_of_images(UpperCamelCase__   )

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

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

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

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

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

    if do_rescale:
      __magic_name__    					=	[self.rescale(image=UpperCamelCase__      ,							scale=UpperCamelCase__   ) for image in images]

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

    __magic_name__    					=	[to_channel_dimension_format(UpperCamelCase__      ,							UpperCamelCase__   ) for image in images]

    __magic_name__    					=	{"""pixel_values""": images}
    return BatchFeature(data=UpperCamelCase__      ,							tensor_type=UpperCamelCase__   )

