




'''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      A       :







							def __init__(    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    str=2				,     lowerCAmelCase_			:    int=True				,     lowerCAmelCase_			:    Tuple=False				,     lowerCAmelCase_			:    List[str]=10				,     lowerCAmelCase_			:    Optional[int]=3				,     lowerCAmelCase_			:    Dict=32 * 8				,     lowerCAmelCase_			:    Tuple=32 * 8				,     lowerCAmelCase_			:    Any=4				,     lowerCAmelCase_			:    int=64				,     )     ->				str:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    int   )     ->				Any:


														"""simple docstring"""


														_a      	=							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
														    lowerCAmelCase_   )

														_a      	=							torch.ones([self.batch_size, self.min_size, self.max_size]				,     device=lowerCAmelCase_   )

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

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







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Any:


														"""simple docstring"""


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

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

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







							def   __lowerCAmelCase     (    self			:    str   )     ->				Any:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    List[str]   )     ->				Any:


														"""simple docstring"""


														_a      	=							output.encoder_hidden_states
														_a      	=							output.pixel_decoder_hidden_states
														_a      	=							output.transformer_decoder_hidden_states

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







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    int=False   )     ->				List[str]:


														"""simple docstring"""


														with torch.no_grad():
																					_a      	=							MaskaFormerModel(config=lowerCAmelCase_   )
																					model.to(lowerCAmelCase_   )
																					model.eval()

																					_a      	=							model(pixel_values=lowerCAmelCase_				,     pixel_mask=lowerCAmelCase_   )
																					_a      	=							model(lowerCAmelCase_				,     output_hidden_states=lowerCAmelCase_   )

														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(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Any   )     ->				str:


														"""simple docstring"""


														_a      	=							MaskaFormerForUniversalSegmentation(config=lowerCAmelCase_   )
														model.to(lowerCAmelCase_   )
														model.eval()

														def comm_check_on_output(lowerCAmelCase_			:    List[Any]   ):
																					# 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():
																					_a      	=							model(pixel_values=lowerCAmelCase_				,     pixel_mask=lowerCAmelCase_   )
																					_a      	=							model(lowerCAmelCase_   )

																					comm_check_on_output(lowerCAmelCase_   )

																					_a      	=							model(
																					    pixel_values=lowerCAmelCase_				,     pixel_mask=lowerCAmelCase_				,     mask_labels=lowerCAmelCase_				,     class_labels=lowerCAmelCase_   )

														comm_check_on_output(lowerCAmelCase_   )

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



@require_torch
class      A       (						_a	,_a	,unittest.TestCase       ):
							lowercase_							     =							(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							lowercase_							     =							{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							False







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							MaskaFormerModelTester(self   )
														_a      	=							ConfigTester(self				,     config_class=lowerCAmelCase_				,     has_text_modality=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				Optional[Any]:


														"""simple docstring"""


														self.config_tester.run_common_tests()







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


														"""simple docstring"""


														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase_				,     **lowerCAmelCase_				,     output_hidden_states=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase_   )







							@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''   )
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				str:


														"""simple docstring"""


														pass







							@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''   )
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


														pass







							@unittest.skip(reason='''Mask2Former is not a generative model'''   )
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[Any]:


														"""simple docstring"""


														pass







							@unittest.skip(reason='''Mask2Former does not use token embeddings'''   )
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Dict:


														"""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   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


														pass







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Optional[int]:


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

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

																					_a      	=							['''pixel_values''']
																					self.assertListEqual(arg_names[:1]				,     lowerCAmelCase_   )







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


														"""simple docstring"""


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







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


														"""simple docstring"""


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

														_a      	=							MaskaFormerForUniversalSegmentation(lowerCAmelCase_   ).to(lowerCAmelCase_   )
														_a      	=							model(**lowerCAmelCase_   )
														self.assertTrue(outputs.loss is not None   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase_				,     **lowerCAmelCase_				,     output_hidden_states=lowerCAmelCase_   )







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


														"""simple docstring"""


														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

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







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														if not self.model_tester.is_training:
																					return

														_a      	=							self.all_model_classes[1]
														_a	,				_a	,				_a	,				_a	,				_a      	=							self.model_tester.prepare_config_and_inputs()

														_a      	=							model_class(lowerCAmelCase_   )
														model.to(lowerCAmelCase_   )
														model.train()

														_a      	=							model(lowerCAmelCase_				,     mask_labels=lowerCAmelCase_				,     class_labels=lowerCAmelCase_   ).loss
														loss.backward()







							def   __lowerCAmelCase     (    self			:    Any   )     ->				int:


														"""simple docstring"""


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

														_a      	=							model_class(lowerCAmelCase_   ).to(lowerCAmelCase_   )
														model.train()

														_a      	=							model(lowerCAmelCase_				,     mask_labels=lowerCAmelCase_				,     class_labels=lowerCAmelCase_   )

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

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

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

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

														outputs.loss.backward(retain_graph=lowerCAmelCase_   )

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



_snake_case : List[str]  				=						1E-4
def 						snake_case_       ():





							'''simple docstring'''


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

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







							@cached_property
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				List[Any]:


														"""simple docstring"""


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







							@cached_property
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Dict:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    str   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							MaskaFormerModel.from_pretrained(self.model_checkpoints   ).to(lowerCAmelCase_   )
														_a      	=							self.default_image_processor
														_a      	=							prepare_img()
														_a      	=							image_processor(lowerCAmelCase_				,     return_tensors='''pt'''   ).to(lowerCAmelCase_   )
														_a      	=							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(lowerCAmelCase_				,     (1, 3, 3_84, 3_84)   )

														with torch.no_grad():
																					_a      	=							model(**lowerCAmelCase_   )

														_a      	=							torch.tensor(
														    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]   ).to(lowerCAmelCase_   )
														self.assertTrue(
														    torch.allclose(
														        outputs.encoder_last_hidden_state[0, 0, :3, :3]				,     lowerCAmelCase_				,     atol=lowerCAmelCase_   )   )

														_a      	=							torch.tensor(
														    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]   ).to(lowerCAmelCase_   )
														self.assertTrue(
														    torch.allclose(
														        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,     lowerCAmelCase_				,     atol=lowerCAmelCase_   )   )

														_a      	=							torch.tensor(
														    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]   ).to(lowerCAmelCase_   )
														self.assertTrue(
														    torch.allclose(
														        outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,     lowerCAmelCase_				,     atol=lowerCAmelCase_   )   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				str:


														"""simple docstring"""


														_a      	=							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(lowerCAmelCase_   ).eval()
														_a      	=							self.default_image_processor
														_a      	=							prepare_img()
														_a      	=							image_processor(lowerCAmelCase_				,     return_tensors='''pt'''   ).to(lowerCAmelCase_   )
														_a      	=							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(lowerCAmelCase_				,     (1, 3, 3_84, 3_84)   )

														with torch.no_grad():
																					_a      	=							model(**lowerCAmelCase_   )
														# masks_queries_logits
														_a      	=							outputs.masks_queries_logits
														self.assertEqual(
														    masks_queries_logits.shape				,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   )
														_a      	=							[
														    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
														    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
														    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
														]
														_a      	=							torch.tensor(lowerCAmelCase_   ).to(lowerCAmelCase_   )
														self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,     lowerCAmelCase_				,     atol=lowerCAmelCase_   )   )
														# class_queries_logits
														_a      	=							outputs.class_queries_logits
														self.assertEqual(class_queries_logits.shape				,     (1, model.config.num_queries, model.config.num_labels + 1)   )
														_a      	=							torch.tensor(
														    [
														        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
														        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
														        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
														    ]   ).to(lowerCAmelCase_   )
														self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,     lowerCAmelCase_				,     atol=lowerCAmelCase_   )   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(lowerCAmelCase_   ).eval()
														_a      	=							self.default_image_processor

														_a      	=							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'''				,     )

														_a      	=							inputs['''pixel_values'''].to(lowerCAmelCase_   )
														_a      	=							[el.to(lowerCAmelCase_   ) for el in inputs['''mask_labels''']]
														_a      	=							[el.to(lowerCAmelCase_   ) for el in inputs['''class_labels''']]

														with torch.no_grad():
																					_a      	=							model(**lowerCAmelCase_   )

														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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case : Dict  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    List[Any]				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Dict[str, int]] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_   )
														_a      	=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_				,     param_name='''crop_size'''   )

														_a      	=							do_resize
														_a      	=							do_rescale
														_a      	=							do_normalize
														_a      	=							do_center_crop
														_a      	=							crop_size
														_a      	=							size
														_a      	=							resample
														_a      	=							rescale_factor
														_a      	=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_DEFAULT_STD







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )

														if "shortest_edge" in size:
																					_a      	=							get_resize_output_image_size(lowerCAmelCase_				,     size=size['''shortest_edge''']				,     default_to_square=lowerCAmelCase_   )
																					# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
														elif "height" in size and "width" in size:
																					_a      	=							(size['''height'''], size['''width'''])
														else:
																					raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'   )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    Dict				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
														return center_crop(lowerCAmelCase_				,     size=(size['''height'''], size['''width'''])				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]   )     ->				np.ndarray:


														"""simple docstring"""


														return rescale(lowerCAmelCase_				,     scale=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				np.ndarray:


														"""simple docstring"""


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







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


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							do_center_crop if do_center_crop is not None else self.do_center_crop
														_a      	=							crop_size if crop_size is not None else self.crop_size
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''				,     default_to_square=lowerCAmelCase_   )
														_a      	=							resample if resample is not None else self.resample
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_   )

														if not is_batched(lowerCAmelCase_   ):
																					_a      	=							[images]

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

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

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

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

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

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

														if do_center_crop:
																					_a      	=							[self.center_crop(image=lowerCAmelCase_				,     size=lowerCAmelCase_   ) for image in images]

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

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

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )

