



import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__magic_name__       =     logging.get_logger(__name__)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase = True				):
					"""simple docstring"""






					lowercase						   =       {}
					for name in model.attn_processors.keys():
										lowercase						   =       name.endswith('attn2.processor'				) and not (
										    name.startswith('transformer_in'				) or 'temp_attentions' in name.split('.'				)
										)
										lowercase						   =       model.config.cross_attention_dim if has_cross_attention else None
										if name.startswith('mid_block'				):
															lowercase						   =       model.config.block_out_channels[-1]
										elif name.startswith('up_blocks'				):
															lowercase						   =       int(name[len('up_blocks.'				)]				)
															lowercase						   =       list(reversed(model.config.block_out_channels				)				)[block_id]
										elif name.startswith('down_blocks'				):
															lowercase						   =       int(name[len('down_blocks.'				)]				)
															lowercase						   =       model.config.block_out_channels[block_id]
										elif name.startswith('transformer_in'				):
															# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
															lowercase						   =       8 * model.config.attention_head_dim

										lowercase						   =       LoRAAttnProcessor(hidden_size=UpperCAmelCase__       ,			cross_attention_dim=UpperCAmelCase__				)
										lowercase						   =       lora_attn_procs[name].to(model.device				)

										if mock_weights:
															# add 1 to weights to mock trained weights
															with torch.no_grad():
																				lora_attn_procs[name].to_q_lora.up.weight += 1
																				lora_attn_procs[name].to_k_lora.up.weight += 1
																				lora_attn_procs[name].to_v_lora.up.weight += 1
																				lora_attn_procs[name].to_out_lora.up.weight += 1

					return lora_attn_procs




@skip_mps
class 	a__       (       __lowerCamelCase  ,			__lowerCamelCase  ,			unittest.TestCase       ):


					"""simple docstring"""



					A__  :				List[Any]					 =				UNetaDConditionModel
					A__  :				str					 =				'''sample'''

					@property
					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       4
										lowercase						   =       4
										lowercase						   =       4
										lowercase						   =       (32, 32)

										lowercase						   =       floats_tensor((batch_size, num_channels, num_frames) + sizes    ).to(_lowercase    )
										lowercase						   =       torch.tensor([10]    ).to(_lowercase    )
										lowercase						   =       floats_tensor((batch_size, 4, 32)    ).to(_lowercase    )

										return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}

					@property
					def 				__UpperCAmelCase		(		self     :str    ):
										return (4, 4, 32, 32)

					@property
					def 				__UpperCAmelCase		(		self     :int    ):
										return (4, 4, 32, 32)

					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       {
										    'block_out_channels': (32, 64),
										    'down_block_types': (
										        'CrossAttnDownBlock3D',
										        'DownBlock3D',
										    ),
										    'up_block_types': ('UpBlock3D', 'CrossAttnUpBlock3D'),
										    'cross_attention_dim': 32,
										    'attention_head_dim': 8,
										    'out_channels': 4,
										    'in_channels': 4,
										    'layers_per_block': 1,
										    'sample_size': 32,
										}
										lowercase						   =       self.dummy_input
										return init_dict, inputs_dict

					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()      ,  reason='XFormers attention is only available with CUDA and `xformers` installed'      ,  )
					def 				__UpperCAmelCase		(		self     :Tuple    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()
										lowercase						   =       self.model_class(**_lowercase    )

										model.enable_xformers_memory_efficient_attention()

										assert (
										    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
										    == "XFormersAttnProcessor"
										), "xformers is not enabled"

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()

										lowercase						   =       32

										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )
										model.eval()

										with torch.no_grad():
															lowercase						   =       model(**_lowercase    )

															if isinstance(_lowercase      ,  _lowercase    ):
																				lowercase						   =       output.sample

										self.assertIsNotNone(_lowercase    )
										lowercase						   =       inputs_dict['sample'].shape
										self.assertEqual(output.shape      ,  _lowercase      ,  'Input and output shapes do not match'    )

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()
										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )
										model.eval()

										with torch.no_grad():
															# Warmup pass when using mps (see #372)
															if torch_device == "mps" and isinstance(_lowercase      ,  _lowercase    ):
																				model(**self.dummy_input    )

															lowercase						   =       model(**_lowercase    )
															if isinstance(_lowercase      ,  _lowercase    ):
																				lowercase						   =       first.sample

															lowercase						   =       model(**_lowercase    )
															if isinstance(_lowercase      ,  _lowercase    ):
																				lowercase						   =       second.sample

										lowercase						   =       first.cpu().numpy()
										lowercase						   =       second.cpu().numpy()
										lowercase						   =       out_a[~np.isnan(_lowercase    )]
										lowercase						   =       out_a[~np.isnan(_lowercase    )]
										lowercase						   =       np.amax(np.abs(out_a - out_a    )    )
										self.assertLessEqual(_lowercase      ,  1E-5    )

					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()

										lowercase						   =       8

										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )
										model.eval()

										model.set_attention_slice('auto'    )
										with torch.no_grad():
															lowercase						   =       model(**_lowercase    )
										assert output is not None

										model.set_attention_slice('max'    )
										with torch.no_grad():
															lowercase						   =       model(**_lowercase    )
										assert output is not None

										model.set_attention_slice(2    )
										with torch.no_grad():
															lowercase						   =       model(**_lowercase    )
										assert output is not None

					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()

										lowercase						   =       8

										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )

										with torch.no_grad():
															lowercase						   =       model(**_lowercase    ).sample

										lowercase						   =       create_lora_layers(_lowercase    )

										# make sure we can set a list of attention processors
										model.set_attn_processor(_lowercase    )
										model.to(_lowercase    )

										# test that attn processors can be set to itself
										model.set_attn_processor(model.attn_processors    )

										with torch.no_grad():
															lowercase						   =       model(**_lowercase      ,  cross_attention_kwargs={'scale': 0.0}    ).sample
															lowercase						   =       model(**_lowercase      ,  cross_attention_kwargs={'scale': 0.5}    ).sample
															lowercase						   =       model(**_lowercase      ,  cross_attention_kwargs={'scale': 0.5}    ).sample

										assert (samplea - samplea).abs().max() < 3E-3
										assert (samplea - samplea).abs().max() < 3E-3

										# sample 2 and sample 3 should be different
										assert (samplea - samplea).abs().max() > 3E-3

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()

										lowercase						   =       8

										torch.manual_seed(0    )
										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )

										with torch.no_grad():
															lowercase						   =       model(**_lowercase    ).sample

										lowercase						   =       create_lora_layers(_lowercase    )
										model.set_attn_processor(_lowercase    )

										with torch.no_grad():
															lowercase						   =       model(**_lowercase      ,  cross_attention_kwargs={'scale': 0.5}    ).sample

										with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(_lowercase    )
															self.assertTrue(os.path.isfile(os.path.join(_lowercase      ,  'pytorch_lora_weights.bin'    )    )    )
															torch.manual_seed(0    )
															lowercase						   =       self.model_class(**_lowercase    )
															new_model.to(_lowercase    )
															new_model.load_attn_procs(_lowercase    )

										with torch.no_grad():
															lowercase						   =       new_model(**_lowercase      ,  cross_attention_kwargs={'scale': 0.5}    ).sample

										assert (sample - new_sample).abs().max() < 1E-3

										# LoRA and no LoRA should NOT be the same
										assert (sample - old_sample).abs().max() > 1E-4

					def 				__UpperCAmelCase		(		self     :Tuple    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()

										lowercase						   =       8

										torch.manual_seed(0    )
										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )

										with torch.no_grad():
															lowercase						   =       model(**_lowercase    ).sample

										lowercase						   =       create_lora_layers(_lowercase    )
										model.set_attn_processor(_lowercase    )

										with torch.no_grad():
															lowercase						   =       model(**_lowercase      ,  cross_attention_kwargs={'scale': 0.5}    ).sample

										with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(_lowercase      ,  safe_serialization=_lowercase    )
															self.assertTrue(os.path.isfile(os.path.join(_lowercase      ,  'pytorch_lora_weights.safetensors'    )    )    )
															torch.manual_seed(0    )
															lowercase						   =       self.model_class(**_lowercase    )
															new_model.to(_lowercase    )
															new_model.load_attn_procs(_lowercase    )

										with torch.no_grad():
															lowercase						   =       new_model(**_lowercase      ,  cross_attention_kwargs={'scale': 0.5}    ).sample

										assert (sample - new_sample).abs().max() < 3E-3

										# LoRA and no LoRA should NOT be the same
										assert (sample - old_sample).abs().max() > 1E-4

					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()

										lowercase						   =       8

										torch.manual_seed(0    )
										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )

										lowercase						   =       create_lora_layers(_lowercase      ,  mock_weights=_lowercase    )
										model.set_attn_processor(_lowercase    )
										# Saving as torch, properly reloads with directly filename
										with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(_lowercase    )
															self.assertTrue(os.path.isfile(os.path.join(_lowercase      ,  'pytorch_lora_weights.bin'    )    )    )
															torch.manual_seed(0    )
															lowercase						   =       self.model_class(**_lowercase    )
															new_model.to(_lowercase    )
															new_model.load_attn_procs(_lowercase      ,  weight_name='pytorch_lora_weights.bin'    )

					def 				__UpperCAmelCase		(		self     :Optional[int]    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()

										lowercase						   =       8

										torch.manual_seed(0    )
										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )

										lowercase						   =       create_lora_layers(_lowercase      ,  mock_weights=_lowercase    )
										model.set_attn_processor(_lowercase    )
										# Saving as torch, properly reloads with directly filename
										with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(_lowercase    )
															self.assertTrue(os.path.isfile(os.path.join(_lowercase      ,  'pytorch_lora_weights.bin'    )    )    )
															torch.manual_seed(0    )
															lowercase						   =       self.model_class(**_lowercase    )
															new_model.to(_lowercase    )
															with self.assertRaises(_lowercase    ) as e:
																				new_model.load_attn_procs(_lowercase      ,  use_safetensors=_lowercase    )
															self.assertIn('Error no file named pytorch_lora_weights.safetensors'      ,  str(e.exception    )    )

					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()

										lowercase						   =       8

										torch.manual_seed(0    )
										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )

										with torch.no_grad():
															lowercase						   =       model(**_lowercase    ).sample

										lowercase						   =       create_lora_layers(_lowercase    )
										model.set_attn_processor(_lowercase    )

										with torch.no_grad():
															lowercase						   =       model(**_lowercase      ,  cross_attention_kwargs={'scale': 0.0}    ).sample

										model.set_attn_processor(AttnProcessor()    )

										with torch.no_grad():
															lowercase						   =       model(**_lowercase    ).sample

										assert (sample - new_sample).abs().max() < 1E-4
										assert (sample - old_sample).abs().max() < 3E-3

					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()      ,  reason='XFormers attention is only available with CUDA and `xformers` installed'      ,  )
					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()

										lowercase						   =       4

										torch.manual_seed(0    )
										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )
										lowercase						   =       create_lora_layers(_lowercase    )
										model.set_attn_processor(_lowercase    )

										# default
										with torch.no_grad():
															lowercase						   =       model(**_lowercase    ).sample

															model.enable_xformers_memory_efficient_attention()
															lowercase						   =       model(**_lowercase    ).sample

															model.disable_xformers_memory_efficient_attention()
															lowercase						   =       model(**_lowercase    ).sample

										assert (sample - on_sample).abs().max() < 1E-4
										assert (sample - off_sample).abs().max() < 1E-4



					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										lowercase   ,      lowercase						   =       self.prepare_init_args_and_inputs_for_common()
										lowercase						   =       32

										lowercase						   =       self.model_class(**_lowercase    )
										model.to(_lowercase    )
										model.eval()

										with torch.no_grad():
															lowercase						   =       model(**_lowercase    )[0]

										model.enable_forward_chunking()
										with torch.no_grad():
															lowercase						   =       model(**_lowercase    )[0]

										self.assertEqual(output.shape      ,  output_a.shape      ,  'Shape doesn\'t match'    )
										assert np.abs(output.cpu() - output_a.cpu()    ).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.







"""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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCamelCase_							:							Optional[int]				 =		logging.get_logger(__name__)


if is_vision_available():
						import PIL


class       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							['pixel_values']

				def __init__(  self							:     str							,     _lowercase							:     bool = True							,     _lowercase							:     Dict[str, int] = None							,     _lowercase							:     PILImageResampling = PILImageResampling.BILINEAR							,     _lowercase							:     bool = True							,     _lowercase							:     bool = False							,     _lowercase							:     bool = True							,     _lowercase							:     bool = True							,     _lowercase							:     Union[int, float] = 1 / 2_5_5							,     _lowercase							:     bool = True							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     **_lowercase							:     List[str]							,     ):


										'''simple docstring'''
										super().__init__(**_lowercase  )

										UpperCAmelCase__       						=		size if size is not None else {"""height""": 2_5_6_0, """width""": 1_9_2_0}
										if isinstance(_lowercase							,     (tuple, list)  ):
																# The previous feature extractor size parameter was in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )

										UpperCAmelCase__       						=		do_resize
										UpperCAmelCase__       						=		size
										UpperCAmelCase__       						=		resample
										UpperCAmelCase__       						=		do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis
										UpperCAmelCase__       						=		do_pad
										UpperCAmelCase__       						=		do_rescale
										UpperCAmelCase__       						=		rescale_factor
										UpperCAmelCase__       						=		do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										UpperCAmelCase__       						=		image_std if image_std is not None else IMAGENET_STANDARD_STD

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


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
																UpperCAmelCase__       						=		np.rotaa(_lowercase							,     3  )

										if data_format is not None:
																UpperCAmelCase__       						=		to_channel_dimension_format(_lowercase							,     _lowercase  )

										return image

				def 							A			(  self							:     Tuple							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )

										UpperCAmelCase__       						=		output_width - input_width
										UpperCAmelCase__       						=		output_height - input_height

										if random_padding:
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_height + 1  )
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_width + 1  )
										else:
																UpperCAmelCase__       						=		delta_height // 2
																UpperCAmelCase__       						=		delta_width // 2

										UpperCAmelCase__       						=		delta_height - pad_top
										UpperCAmelCase__       						=		delta_width - pad_left

										UpperCAmelCase__       						=		((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(_lowercase							,     _lowercase							,     data_format=_lowercase  )

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


										'''simple docstring'''
										logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""  )
										return self.pad_image(*_lowercase							,     **_lowercase  )

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


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										# We always resize to the smallest of either the input or output size.
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )

										if height == input_height and width == input_width:
																return image

										if input_height > input_width:
																UpperCAmelCase__       						=		int(input_width * height / input_height  )
										elif input_width > input_height:
																UpperCAmelCase__       						=		int(input_height * width / input_width  )

										return resize(
										    _lowercase							,     size=(height, width)							,     resample=_lowercase							,     reducing_gap=2.0							,     data_format=_lowercase							,     **_lowercase  )

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


										'''simple docstring'''
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		min(size["""height"""]							,     size["""width"""]  )
										UpperCAmelCase__       						=		get_resize_output_image_size(_lowercase							,     size=_lowercase							,     default_to_square=_lowercase  )
										UpperCAmelCase__       						=		resize(_lowercase							,     size=_lowercase							,     resample=_lowercase							,     data_format=_lowercase							,     **_lowercase  )
										return resized_image

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


										'''simple docstring'''
										return rescale(_lowercase							,     scale=_lowercase							,     data_format=_lowercase							,     **_lowercase  )

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


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

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


										'''simple docstring'''
										UpperCAmelCase__       						=		do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase__       						=		size if size is not None else self.size
										if isinstance(_lowercase							,     (tuple, list)  ):
																# Previous feature extractor had size in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		resample if resample is not None else self.resample
										UpperCAmelCase__       						=		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										UpperCAmelCase__       						=		do_pad if do_pad is not None else self.do_pad
										UpperCAmelCase__       						=		do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase__       						=		rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase__       						=		do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase__       						=		image_std if image_std is not None else self.image_std

										UpperCAmelCase__       						=		make_list_of_images(_lowercase  )

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

										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."""  )

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

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("""Image mean and std must be specified if do_normalize is True."""  )

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

										if do_align_long_axis:
																UpperCAmelCase__       						=		[self.align_long_axis(_lowercase							,     size=_lowercase  ) for image in images]

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

										if do_thumbnail:
																UpperCAmelCase__       						=		[self.thumbnail(image=_lowercase							,     size=_lowercase  ) for image in images]

										if do_pad:
																UpperCAmelCase__       						=		[self.pad_image(image=_lowercase							,     size=_lowercase							,     random_padding=_lowercase  ) for image in images]

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

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

										UpperCAmelCase__       						=		[to_channel_dimension_format(_lowercase							,     _lowercase  ) for image in images]

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

