


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class      lowercase			(							A__			,	A__			,	A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       StableDiffusionInstructPixaPixPipeline
		_a									=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
		_a									=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_a									=       IMAGE_TO_IMAGE_IMAGE_PARAMS
		_a									=       IMAGE_TO_IMAGE_IMAGE_PARAMS




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__     :Union[str, Any]							      =				UNetaDConditionModel(
							    block_out_channels=(32, 64)					,     layers_per_block=2					,     sample_size=32					,     in_channels=8					,     out_channels=4					,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,     cross_attention_dim=32					,     )
							UpperCamelCase__     :Union[str, Any]							      =				PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							torch.manual_seed(0							)
							UpperCamelCase__     :List[str]							      =				AutoencoderKL(
							    block_out_channels=[32, 64]					,     in_channels=3					,     out_channels=3					,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,     latent_channels=4					,     )
							torch.manual_seed(0							)
							UpperCamelCase__     :int							      =				CLIPTextConfig(
							    bos_token_id=0					,     eos_token_id=2					,     hidden_size=32					,     intermediate_size=37					,     layer_norm_eps=1e-05					,     num_attention_heads=4					,     num_hidden_layers=5					,     pad_token_id=1					,     vocab_size=1000					,     )
							UpperCamelCase__     :Any							      =				CLIPTextModel(UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__     :List[Any]							      =				{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=0							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				floats_tensor((1, 3, 32, 32)					,     rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				image.cpu().permute(0					,     2					,     3					,     1							)[0]
							UpperCamelCase__     :Tuple							      =				Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							)
							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__     :int							      =				torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__     :Any							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''image_guidance_scale''': 1,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :Optional[int]							      =				self.get_dummy_components()
							UpperCamelCase__     :Tuple							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				sd_pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :str							      =				image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__     :Union[str, Any]							      =				np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :str							      =				self.get_dummy_components()
							UpperCamelCase__     :List[Any]							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				'''french fries'''
							UpperCamelCase__     :Union[str, Any]							      =				sd_pipe(**UpperCamelCase_					,     negative_prompt=UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				output.images
							UpperCamelCase__     :Optional[Any]							      =				image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__     :int							      =				np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :str							      =				self.get_dummy_components()
							UpperCamelCase__     :List[str]							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :int							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				[inputs['''prompt''']] * 2

							UpperCamelCase__     :Dict							      =				np.array(inputs['''image''']							).astype(np.floataa							) / 255.0
							UpperCamelCase__     :Dict							      =				torch.from_numpy(UpperCamelCase_							).unsqueeze(0							).to(UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				image / 2 + 0.5
							UpperCamelCase__     :Dict							      =				image.permute(0					,     3					,     1					,     2							)
							UpperCamelCase__     :List[str]							      =				image.repeat(2					,     1					,     1					,     1							)

							UpperCamelCase__     :Dict							      =				sd_pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :Optional[Any]							      =				image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							UpperCamelCase__     :int							      =				np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :Optional[int]							      =				self.get_dummy_components()
							UpperCamelCase__     :Optional[Any]							      =				EulerAncestralDiscreteScheduler(
							    beta_start=0.00085					,     beta_end=0.012					,     beta_schedule='''scaled_linear'''							)
							UpperCamelCase__     :List[Any]							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Any							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				sd_pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :List[Any]							      =				image[0, -3:, -3:, -1]

							UpperCamelCase__     :Dict							      =				[round(UpperCamelCase_					,     4							) for x in image_slice.flatten().tolist()]
							print(''','''.join([str(UpperCamelCase_							) for x in slice]							)							)

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__     :Optional[Any]							      =				np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.get_dummy_components()
							UpperCamelCase__     :Any							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				VaeImageProcessor(do_resize=UpperCamelCase_					,     do_normalize=UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_					,     input_image_type='''pt'''							)							)[0]

							UpperCamelCase__     :Dict							      =				components['''vae''']
							UpperCamelCase__     :int							      =				self.get_dummy_inputs_by_type(UpperCamelCase_					,     input_image_type='''pt'''							)

							for image_param in self.image_latents_params:
												if image_param in inputs.keys():
																	UpperCamelCase__     :Optional[int]							      =				vae.encode(inputs[image_param]							).latent_dist.mode()

							UpperCamelCase__     :Any							      =				pipe(**UpperCamelCase_							)[0]

							UpperCamelCase__     :str							      =				np.abs(out - out_latents_inputs							).max()
							self.assertLess(UpperCamelCase_					,     1e-4					,     '''passing latents as image input generate different result from passing image'''							)



@slow
@require_torch_gpu
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=0							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				torch.manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''							)
							UpperCamelCase__     :List[str]							      =				{
							    '''prompt''': '''turn him into a cyborg''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''image_guidance_scale''': 1.0,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :Dict							      =				self.get_inputs()
							UpperCamelCase__     :Any							      =				pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :int							      =				image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__     :List[Any]							      =				np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]							)

							assert np.abs(expected_slice - image_slice							).max() < 1e-3




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :List[Any]							      =				self.get_inputs()
							UpperCamelCase__     :Union[str, Any]							      =				pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :str							      =				image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__     :Any							      =				np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]							)

							assert np.abs(expected_slice - image_slice							).max() < 1e-3




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				DDIMScheduler.from_config(pipe.scheduler.config							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :Dict							      =				self.get_inputs()
							UpperCamelCase__     :Any							      =				pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :str							      =				image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__     :Optional[int]							      =				np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]							)

							assert np.abs(expected_slice - image_slice							).max() < 1e-3




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				0

							def callback_fn(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							) -> None:
												UpperCamelCase__     :str							      =				True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																	UpperCamelCase__     :Optional[int]							      =				latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__     :Tuple							      =				latents[0, -3:, -3:, -1]
																	UpperCamelCase__     :Tuple							      =				np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]							)

																	assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2
												elif step == 2:
																	UpperCamelCase__     :List[Any]							      =				latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__     :Any							      =				latents[0, -3:, -3:, -1]
																	UpperCamelCase__     :List[str]							      =				np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]							)

																	assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2

							UpperCamelCase__     :Dict							      =				False

							UpperCamelCase__     :Tuple							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_					,     torch_dtype=torch.floataa							)
							UpperCamelCase__     :List[str]							      =				pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :List[str]							      =				self.get_inputs()
							pipe(**UpperCamelCase_					,     callback=UpperCamelCase_					,     callback_steps=1							)
							assert callback_fn.has_been_called
							assert number_of_steps == 3




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							UpperCamelCase__     :Optional[Any]							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_					,     torch_dtype=torch.floataa							)
							UpperCamelCase__     :Dict							      =				pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing(1							)
							pipe.enable_sequential_cpu_offload()

							UpperCamelCase__     :str							      =				self.get_inputs()
							UpperCamelCase__     :Dict							      =				pipe(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				torch.cuda.max_memory_allocated()
							# make sure that less than 2.2 GB is allocated
							assert mem_bytes < 2.2 * 10**9




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							UpperCamelCase__     :Any							      =				inputs['''image'''].resize((504, 504)							)

							UpperCamelCase__     :List[str]							      =				'''timbrooks/instruct-pix2pix'''
							UpperCamelCase__     :Dict							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    UpperCamelCase_					,     safety_checker=UpperCamelCase_					,     )
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :Optional[Any]							      =				pipe(**UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				output.images[0]

							UpperCamelCase__     :List[Any]							      =				image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							UpperCamelCase__     :Dict							      =				np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 5e-3



'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


__snake_case							   =  logging.get_logger(__name__)


def     a   (    __a				,      __a				,      __a				,      __a     )						->			Tuple[int, int]:



					'''simple docstring'''






					def constraint_to_multiple_of(__a				,      __a				,      __a=0				,      __a=None     ):
										UpperCamelCase__     :Union[str, Any]							      =				round(val / multiple     ) * multiple

										if max_val is not None and x > max_val:
															UpperCamelCase__     :List[str]							      =				math.floor(val / multiple     ) * multiple

										if x < min_val:
															UpperCamelCase__     :Union[str, Any]							      =				math.ceil(val / multiple     ) * multiple

										return x

					UpperCamelCase__     :str							      =				(output_size, output_size) if isinstance(__a				,      __a     ) else output_size

					UpperCamelCase__      ,       UpperCamelCase__     :int							      =				get_image_size(__a     )
					UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				output_size

					# determine new height and width
					UpperCamelCase__     :List[str]							      =				output_height / input_height
					UpperCamelCase__     :str							      =				output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width     ) < abs(1 - scale_height     ):
															# fit width
															UpperCamelCase__     :str							      =				scale_width
										else:
															# fit height
															UpperCamelCase__     :int							      =				scale_height

					UpperCamelCase__     :Optional[int]							      =				constraint_to_multiple_of(scale_height * input_height				,      multiple=__a     )
					UpperCamelCase__     :List[Any]							      =				constraint_to_multiple_of(scale_width * input_width				,      multiple=__a     )

					return (new_height, new_width)







class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['pixel_values']




		def __init__(	self					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = PILImageResampling.BILINEAR					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = True					,     UpperCamelCase_ = 1 / 255					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				size if size is not None else {'''height''': 384, '''width''': 384}
							UpperCamelCase__     :Dict							      =				get_size_dict(UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				do_resize
							UpperCamelCase__     :Union[str, Any]							      =				size
							UpperCamelCase__     :List[Any]							      =				keep_aspect_ratio
							UpperCamelCase__     :Optional[int]							      =				ensure_multiple_of
							UpperCamelCase__     :Union[str, Any]							      =				resample
							UpperCamelCase__     :str							      =				do_rescale
							UpperCamelCase__     :Union[str, Any]							      =				rescale_factor
							UpperCamelCase__     :List[str]							      =				do_normalize
							UpperCamelCase__     :List[str]							      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase__     :Dict							      =				image_std if image_std is not None else IMAGENET_STANDARD_STD




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = PILImageResampling.BICUBIC					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				get_size_dict(UpperCamelCase_							)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''							)
							UpperCamelCase__     :int							      =				get_resize_output_image_size(
							    UpperCamelCase_					,     output_size=(size['''height'''], size['''width'''])					,     keep_aspect_ratio=UpperCamelCase_					,     multiple=UpperCamelCase_					,     )
							return resize(UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	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_ = ChannelDimension.FIRST					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__     :Any							      =				size if size is not None else self.size
							UpperCamelCase__     :List[str]							      =				get_size_dict(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							UpperCamelCase__     :str							      =				ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							UpperCamelCase__     :Union[str, Any]							      =				resample if resample is not None else self.resample
							UpperCamelCase__     :str							      =				do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__     :int							      =				rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__     :Optional[Any]							      =				do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__     :Optional[int]							      =				image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__     :List[str]							      =				image_std if image_std is not None else self.image_std

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

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

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

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

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

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

							UpperCamelCase__     :Optional[int]							      =				[to_channel_dimension_format(UpperCamelCase_					,     UpperCamelCase_							) for image in images]

							UpperCamelCase__     :List[str]							      =				{'''pixel_values''': images}
							return BatchFeature(data=UpperCamelCase_					,     tensor_type=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
												if len(UpperCamelCase_							) != len(UpperCamelCase_							):
																	raise ValueError(
																	    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''							)

												if is_torch_tensor(UpperCamelCase_							):
																	UpperCamelCase__     :Union[str, Any]							      =				target_sizes.numpy()

												UpperCamelCase__     :int							      =				[]

												for idx in range(len(UpperCamelCase_							)							):
																	UpperCamelCase__     :str							      =				torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0							)					,     size=target_sizes[idx]					,     mode='''bilinear'''					,     align_corners=UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				resized_logits[0].argmax(dim=0							)
																	semantic_segmentation.append(UpperCamelCase_							)
							else:
												UpperCamelCase__     :Any							      =				logits.argmax(dim=1							)
												UpperCamelCase__     :Optional[Any]							      =				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

							return semantic_segmentation