"""simple docstring"""
import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

					if is_vision_available():
										from transformers import MaskaFormerImageProcessor

if is_vision_available():
					from PIL import Image



class      lowerCAmelCase_			:



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase						,			lowerCAmelCase=2						,			lowerCAmelCase=True						,			lowerCAmelCase=False						,			lowerCAmelCase=10						,			lowerCAmelCase=3						,			lowerCAmelCase=32 * 8						,			lowerCAmelCase=32 * 8						,			lowerCAmelCase=4						,			lowerCAmelCase=64						,			):






										"""simple docstring"""

										snake_case	    =	parent
										snake_case	    =	batch_size
										snake_case	    =	is_training
										snake_case	    =	use_auxiliary_loss
										snake_case	    =	num_queries
										snake_case	    =	num_channels
										snake_case	    =	min_size
										snake_case	    =	max_size
										snake_case	    =	num_labels
										snake_case	    =	hidden_dim
										snake_case	    =	hidden_dim

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
										    __UpperCamelCase			)

										snake_case	    =	torch.ones([self.batch_size, self.min_size, self.max_size]						,			device=__UpperCamelCase			)

										snake_case	    =	(
										    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]						,			device=__UpperCamelCase			) > 0.5
										).float()
										snake_case	    =	(torch.rand((self.batch_size, self.num_labels)						,			device=__UpperCamelCase			) > 0.5).long()

										snake_case	    =	self.get_config()
										return config, pixel_values, pixel_mask, mask_labels, class_labels

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	MaskaFormerConfig(
										    hidden_size=self.hidden_dim						,			)
										snake_case	    =	self.num_queries
										snake_case	    =	self.num_labels

										snake_case	    =	[1, 1, 1, 1]
										snake_case	    =	self.num_channels

										snake_case	    =	64
										snake_case	    =	1_28
										snake_case	    =	self.hidden_dim
										snake_case	    =	self.hidden_dim
										snake_case	    =	self.hidden_dim
										return config

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.prepare_config_and_inputs()
										snake_case	    =	{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
										return config, inputs_dict

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	output.encoder_hidden_states
										snake_case	    =	output.pixel_decoder_hidden_states
										snake_case	    =	output.transformer_decoder_hidden_states

										self.parent.assertTrue(len(__UpperCamelCase			)						,			len(config.backbone_config.depths			)			)
										self.parent.assertTrue(len(__UpperCamelCase			)						,			len(config.backbone_config.depths			)			)
										self.parent.assertTrue(len(__UpperCamelCase			)						,			config.decoder_layers			)

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase=False			):






										"""simple docstring"""

										with torch.no_grad():
																snake_case	    =	MaskaFormerModel(config=__UpperCamelCase			)
																model.to(__UpperCamelCase			)
																model.eval()

																snake_case	    =	model(pixel_values=__UpperCamelCase						,			pixel_mask=__UpperCamelCase			)
																snake_case	    =	model(__UpperCamelCase						,			output_hidden_states=__UpperCamelCase			)

										self.parent.assertEqual(
										    output.transformer_decoder_last_hidden_state.shape						,			(self.batch_size, self.num_queries, self.hidden_dim)						,			)
										# let's ensure the other two hidden state exists
										self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None			)
										self.parent.assertTrue(output.encoder_last_hidden_state is not None			)

										if output_hidden_states:
																self.check_output_hidden_state(__UpperCamelCase						,			__UpperCamelCase			)

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	MaskaFormerForUniversalSegmentation(config=__UpperCamelCase			)
										model.to(__UpperCamelCase			)
										model.eval()

										def comm_check_on_output(lowerCAmelCase			):
																# let's still check that all the required stuff is there
																self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None			)
																self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None			)
																self.parent.assertTrue(result.encoder_last_hidden_state is not None			)
																# okay, now we need to check the logits shape
																# due to the encoder compression, masks have a //4 spatial size
																self.parent.assertEqual(
																    result.masks_queries_logits.shape						,			(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)						,			)
																# + 1 for null class
																self.parent.assertEqual(
																    result.class_queries_logits.shape						,			(self.batch_size, self.num_queries, self.num_labels + 1)			)

										with torch.no_grad():
																snake_case	    =	model(pixel_values=__UpperCamelCase						,			pixel_mask=__UpperCamelCase			)
																snake_case	    =	model(__UpperCamelCase			)

																comm_check_on_output(__UpperCamelCase			)

																snake_case	    =	model(
																    pixel_values=__UpperCamelCase						,			pixel_mask=__UpperCamelCase						,			mask_labels=__UpperCamelCase						,			class_labels=__UpperCamelCase			)

										comm_check_on_output(__UpperCamelCase			)

										self.parent.assertTrue(result.loss is not None			)
										self.parent.assertEqual(result.loss.shape						,			torch.Size([1]			)			)





@require_torch
class      lowerCAmelCase_			(	lowercase_   ,			lowercase_   ,			unittest.TestCase    ):



				"""simple docstring"""
				_lowerCAmelCase   :			Optional[int]	     =  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				_lowerCAmelCase   :			Optional[int]	     =  {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

				_lowerCAmelCase   :			Dict	     =  False
				_lowerCAmelCase   :			Optional[Any]	     =  False
				_lowerCAmelCase   :			List[Any]	     =  False
				_lowerCAmelCase   :			Any	     =  False

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	MaskaFormerModelTester(self			)
										snake_case	    =	ConfigTester(self						,			config_class=__UpperCamelCase						,			has_text_modality=__UpperCamelCase			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(__UpperCamelCase						,			**__UpperCamelCase						,			output_hidden_states=__UpperCamelCase			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__UpperCamelCase			)

				@unittest.skip(reason='Mask2Former does not use inputs_embeds'			)
				def 						snake_case       ( self			):






										"""simple docstring"""

										pass

				@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'			)
				def 						snake_case       ( self			):






										"""simple docstring"""

										pass

				@unittest.skip(reason='Mask2Former is not a generative model'			)
				def 						snake_case       ( self			):






										"""simple docstring"""

										pass

				@unittest.skip(reason='Mask2Former does not use token embeddings'			)
				def 						snake_case       ( self			):






										"""simple docstring"""

										pass

				@require_torch_multi_gpu
				@unittest.skip(
				    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'			)
				def 						snake_case       ( self			):






										"""simple docstring"""

										pass

				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
				def 						snake_case       ( self			):






										"""simple docstring"""

										pass

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case	    =	['pixel_values']
																self.assertListEqual(arg_names[:1]						,			__UpperCamelCase			)

				@slow
				def 						snake_case       ( self			):






										"""simple docstring"""

										for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																snake_case	    =	MaskaFormerModel.from_pretrained(__UpperCamelCase			)
																self.assertIsNotNone(__UpperCamelCase			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	(self.model_tester.min_size,) * 2
										snake_case	    =	{
										    'pixel_values': torch.randn((2, 3, *size)						,			device=__UpperCamelCase			),
										    'mask_labels': torch.randn((2, 10, *size)						,			device=__UpperCamelCase			),
										    'class_labels': torch.zeros(2						,			10						,			device=__UpperCamelCase			).long(),
										}
										snake_case	    =	self.model_tester.get_config()

										snake_case	    =	MaskaFormerForUniversalSegmentation(__UpperCamelCase			).to(__UpperCamelCase			)
										snake_case	    =	model(**__UpperCamelCase			)
										self.assertTrue(outputs.loss is not None			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskaformer_model(__UpperCamelCase						,			**__UpperCamelCase						,			output_hidden_states=__UpperCamelCase			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case	    =	model_class(__UpperCamelCase			).to(__UpperCamelCase			)
																snake_case	    =	model(**__UpperCamelCase						,			output_attentions=__UpperCamelCase			)
																self.assertTrue(outputs.attentions is not None			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										if not self.model_tester.is_training:
																return

										snake_case	    =	self.all_model_classes[1]
										snake_case	    =	self.model_tester.prepare_config_and_inputs()

										snake_case	    =	model_class(__UpperCamelCase			)
										model.to(__UpperCamelCase			)
										model.train()

										snake_case	    =	model(__UpperCamelCase						,			mask_labels=__UpperCamelCase						,			class_labels=__UpperCamelCase			).loss
										loss.backward()

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.all_model_classes[1]
										snake_case	    =	self.model_tester.prepare_config_and_inputs()
										snake_case	    =	True
										snake_case	    =	True

										snake_case	    =	model_class(__UpperCamelCase			).to(__UpperCamelCase			)
										model.train()

										snake_case	    =	model(__UpperCamelCase						,			mask_labels=__UpperCamelCase						,			class_labels=__UpperCamelCase			)

										snake_case	    =	outputs.encoder_hidden_states[0]
										encoder_hidden_states.retain_grad()

										snake_case	    =	outputs.pixel_decoder_hidden_states[0]
										pixel_decoder_hidden_states.retain_grad()

										snake_case	    =	outputs.transformer_decoder_hidden_states[0]
										transformer_decoder_hidden_states.retain_grad()

										snake_case	    =	outputs.attentions[0]
										attentions.retain_grad()

										outputs.loss.backward(retain_graph=__UpperCamelCase			)

										self.assertIsNotNone(encoder_hidden_states.grad			)
										self.assertIsNotNone(pixel_decoder_hidden_states.grad			)
										self.assertIsNotNone(transformer_decoder_hidden_states.grad			)
										self.assertIsNotNone(attentions.grad			)





SCREAMING_SNAKE_CASE__					  =     1E-4





def       lowerCAmelCase__			(	)    -> Tuple:





						"""simple docstring"""






						snake_case	    =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
						return image



@require_vision
@slow
class      lowerCAmelCase_			(	unittest.TestCase    ):



				"""simple docstring"""

				@cached_property
				def 						snake_case       ( self			):






										"""simple docstring"""

										return "facebook/mask2former-swin-small-coco-instance"

				@cached_property
				def 						snake_case       ( self			):






										"""simple docstring"""

										return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints			) if is_vision_available() else None

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	MaskaFormerModel.from_pretrained(self.model_checkpoints			).to(__UpperCamelCase			)
										snake_case	    =	self.default_image_processor
										snake_case	    =	prepare_img()
										snake_case	    =	image_processor(__UpperCamelCase						,			return_tensors='pt'			).to(__UpperCamelCase			)
										snake_case	    =	inputs['pixel_values'].shape
										# check size is divisible by 32
										self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
										# check size
										self.assertEqual(__UpperCamelCase						,			(1, 3, 3_84, 3_84)			)

										with torch.no_grad():
																snake_case	    =	model(**__UpperCamelCase			)

										snake_case	    =	torch.tensor(
										    [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]			).to(__UpperCamelCase			)
										self.assertTrue(
										    torch.allclose(
										        outputs.encoder_last_hidden_state[0, 0, :3, :3]						,			__UpperCamelCase						,			atol=__UpperCamelCase			)			)

										snake_case	    =	torch.tensor(
										    [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]			).to(__UpperCamelCase			)
										self.assertTrue(
										    torch.allclose(
										        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,			__UpperCamelCase						,			atol=__UpperCamelCase			)			)

										snake_case	    =	torch.tensor(
										    [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]			).to(__UpperCamelCase			)
										self.assertTrue(
										    torch.allclose(
										        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,			__UpperCamelCase						,			atol=__UpperCamelCase			)			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(__UpperCamelCase			).eval()
										snake_case	    =	self.default_image_processor
										snake_case	    =	prepare_img()
										snake_case	    =	image_processor(__UpperCamelCase						,			return_tensors='pt'			).to(__UpperCamelCase			)
										snake_case	    =	inputs['pixel_values'].shape
										# check size is divisible by 32
										self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
										# check size
										self.assertEqual(__UpperCamelCase						,			(1, 3, 3_84, 3_84)			)

										with torch.no_grad():
																snake_case	    =	model(**__UpperCamelCase			)
										# masks_queries_logits
										snake_case	    =	outputs.masks_queries_logits
										self.assertEqual(
										    masks_queries_logits.shape						,			(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			)
										snake_case	    =	[
										    [-8.78_39, -9.00_56, -8.81_21],
										    [-7.41_04, -7.03_13, -6.54_01],
										    [-6.61_05, -6.34_27, -6.46_75],
										]
										snake_case	    =	torch.tensor(__UpperCamelCase			).to(__UpperCamelCase			)
										self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,			__UpperCamelCase						,			atol=__UpperCamelCase			)			)
										# class_queries_logits
										snake_case	    =	outputs.class_queries_logits
										self.assertEqual(class_queries_logits.shape						,			(1, model.config.num_queries, model.config.num_labels + 1)			)
										snake_case	    =	torch.tensor(
										    [
										        [1.83_24, -8.08_35, -4.19_22],
										        [0.84_50, -9.00_50, -3.60_53],
										        [0.30_45, -7.72_93, -3.02_75],
										    ]			).to(__UpperCamelCase			)
										self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,			__UpperCamelCase						,			atol=__UpperCamelCase			)			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(__UpperCamelCase			).eval()
										snake_case	    =	self.default_image_processor

										snake_case	    =	image_processor(
										    [np.zeros((3, 8_00, 13_33)			), np.zeros((3, 8_00, 13_33)			)]						,			segmentation_maps=[np.zeros((3_84, 3_84)			).astype(np.floataa			), np.zeros((3_84, 3_84)			).astype(np.floataa			)]						,			return_tensors='pt'						,			)

										snake_case	    =	inputs['pixel_values'].to(__UpperCamelCase			)
										snake_case	    =	[el.to(__UpperCamelCase			) for el in inputs['mask_labels']]
										snake_case	    =	[el.to(__UpperCamelCase			) for el in inputs['class_labels']]

										with torch.no_grad():
																snake_case	    =	model(**__UpperCamelCase			)

										self.assertTrue(outputs.loss is not None			)



"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  ['pixel_values']
   def __init__(			self     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 0.9     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 1 / 255     ,__UpperCamelCase = True     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      None:


        '''simple docstring'''


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

        lowercase_	:				List[str]													=	do_resize
        lowercase_	:				List[Any]													=	size
        lowercase_	:				int													=	crop_pct
        lowercase_	:				Dict													=	resample
        lowercase_	:				List[str]													=	do_center_crop
        lowercase_	:				Union[str, Any]													=	crop_size
        lowercase_	:				List[Any]													=	do_rescale
        lowercase_	:				Tuple													=	rescale_factor
        lowercase_	:				Tuple													=	do_normalize
        lowercase_	:				Union[str, Any]													=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_	:				int													=	image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				Any													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''			)
        if crop_pct is not None:
             if "shortest_edge" in size:
                  lowercase_	:				Union[str, Any]													=	int(size['shortest_edge'] / crop_pct			)
             elif "height" in size and "width" in size:
                  if size["height"] == size["width"]:
                       lowercase_	:				Tuple													=	int(size['height'] / crop_pct			)
                  else:
                       lowercase_	:				Dict													=	(int(size['height'] / crop_pct			), int(size['width'] / crop_pct			))
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

             lowercase_	:				int													=	get_resize_output_image_size(__UpperCamelCase     ,size=__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        else:
             if "shortest_edge" in size:
                  lowercase_	:				Optional[int]													=	get_resize_output_image_size(__UpperCamelCase     ,size=size['shortest_edge']     ,default_to_square=__UpperCamelCase			)
             elif "height" in size and "width" in size:
                  lowercase_	:				Dict													=	(size['height'], size['width'])
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

        return resize(__UpperCamelCase     ,size=__UpperCamelCase     ,resample=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	get_size_dict(__UpperCamelCase			)
        if "height" not in size or "width" not in size:
             raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''			)
        return center_crop(__UpperCamelCase     ,size=(size['height'], size['width'])     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      str:


        '''simple docstring'''


        return rescale(__UpperCamelCase     ,scale=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


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







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = ChannelDimension.FIRST     ,**__UpperCamelCase     ,)	->      PIL.Image.Image:


        '''simple docstring'''


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

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

        lowercase_	:				str													=	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 or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'			)

        if do_center_crop and crop_pct is None:
             raise ValueError('Crop_pct 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.
        lowercase_	:				Optional[Any]													=	[to_numpy_array(__UpperCamelCase			) for image in images]

        if do_resize:
             lowercase_	:				str													=	[self.resize(image=__UpperCamelCase     ,size=__UpperCamelCase     ,crop_pct=__UpperCamelCase     ,resample=__UpperCamelCase			) for image in images]

        if do_center_crop:
             lowercase_	:				str													=	[self.center_crop(image=__UpperCamelCase     ,size=__UpperCamelCase			) for image in images]

        if do_rescale:
             lowercase_	:				Any													=	[self.rescale(image=__UpperCamelCase     ,scale=__UpperCamelCase			) for image in images]

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

        lowercase_	:				Dict													=	[to_channel_dimension_format(__UpperCamelCase     ,__UpperCamelCase			) for image in images]

        lowercase_	:				Any													=	{'pixel_values': images}
        return BatchFeature(data=__UpperCamelCase     ,tensor_type=__UpperCamelCase			)

