# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()

class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  StableDiffusionControlNetImgaImgPipeline
  lowercase_								=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
  lowercase_								=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowercase_								=  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}    )
  lowercase_								=  IMAGE_TO_IMAGE_IMAGE_PARAMS




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




       """simple docstring"""
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		UNetaDConditionModel(
           block_out_channels=(32, 64)  ,			layers_per_block=2  ,			sample_size=32  ,			in_channels=4  ,			out_channels=4  ,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,			up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,			cross_attention_dim=32  ,			)
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Any      =		ControlNetModel(
           block_out_channels=(32, 64)  ,			layers_per_block=2  ,			in_channels=4  ,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,			cross_attention_dim=32  ,			conditioning_embedding_out_channels=(16, 32)  ,			)
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		DDIMScheduler(
           beta_start=0.0_0085  ,			beta_end=0.012  ,			beta_schedule='scaled_linear'  ,			clip_sample=a_  ,			set_alpha_to_one=a_  ,			)
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		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					)
       SCREAMING_SNAKE_CASE__					:					int      =		CLIPTextConfig(
           bos_token_id=0  ,			eos_token_id=2  ,			hidden_size=32  ,			intermediate_size=37  ,			layer_norm_eps=1e-0_5  ,			num_attention_heads=4  ,			num_hidden_layers=5  ,			pad_token_id=1  ,			vocab_size=1000  ,			)
       SCREAMING_SNAKE_CASE__					:					Any      =		CLIPTextModel(a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		{
           'unet': unet,
           'controlnet': controlnet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components




  def 						__lowercase(   self  :						str  ,			a_  :						Optional[int]  ,			a_  :						Optional[int]=0					)->							Any:




       """simple docstring"""
       if str(a_					).startswith('mps'					):
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.manual_seed(a_					)
       else:
            SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.Generator(device=a_					).manual_seed(a_					)

       SCREAMING_SNAKE_CASE__					:					Any      =		2
       SCREAMING_SNAKE_CASE__					:					Tuple      =		randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,			generator=a_  ,			device=torch.device(a_					)  ,			)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		floats_tensor(control_image.shape  ,			rng=random.Random(a_					)					).to(a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		image.cpu().permute(0  ,			2  ,			3  ,			1					)[0]
       SCREAMING_SNAKE_CASE__					:					int      =		Image.fromarray(np.uinta(a_					)					).convert('RGB'					).resize((64, 64)					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		{
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
           'image': image,
           'control_image': control_image,
       }

       return inputs




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




       """simple docstring"""
       return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3					)




  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()  ,			reason='XFormers attention is only available with CUDA and `xformers` installed'  ,			)
  def 						__lowercase(   self  :						List[Any]					)->							int:




       """simple docstring"""
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)




  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       self._test_inference_batch_single_identical(expected_max_diff=2e-3					)




class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  StableDiffusionControlNetImgaImgPipeline
  lowercase_								=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
  lowercase_								=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowercase_								=  frozenset([]    )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




  def 						__lowercase(   self  :						str					)->							Any:




       """simple docstring"""
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		UNetaDConditionModel(
           block_out_channels=(32, 64)  ,			layers_per_block=2  ,			sample_size=32  ,			in_channels=4  ,			out_channels=4  ,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,			up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,			cross_attention_dim=32  ,			)
       torch.manual_seed(0					)

       def init_weights(a_  :						Tuple					):
            if isinstance(a_  ,			torch.nn.Convad					):
                 torch.nn.init.normal(m.weight					)
                 m.bias.data.fill_(1.0					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		ControlNetModel(
           block_out_channels=(32, 64)  ,			layers_per_block=2  ,			in_channels=4  ,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,			cross_attention_dim=32  ,			conditioning_embedding_out_channels=(16, 32)  ,			)
       controlneta.controlnet_down_blocks.apply(a_					)

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					str      =		ControlNetModel(
           block_out_channels=(32, 64)  ,			layers_per_block=2  ,			in_channels=4  ,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,			cross_attention_dim=32  ,			conditioning_embedding_out_channels=(16, 32)  ,			)
       controlneta.controlnet_down_blocks.apply(a_					)

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		DDIMScheduler(
           beta_start=0.0_0085  ,			beta_end=0.012  ,			beta_schedule='scaled_linear'  ,			clip_sample=a_  ,			set_alpha_to_one=a_  ,			)
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		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					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		CLIPTextConfig(
           bos_token_id=0  ,			eos_token_id=2  ,			hidden_size=32  ,			intermediate_size=37  ,			layer_norm_eps=1e-0_5  ,			num_attention_heads=4  ,			num_hidden_layers=5  ,			pad_token_id=1  ,			vocab_size=1000  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		CLIPTextModel(a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		MultiControlNetModel([controlneta, controlneta]					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		{
           'unet': unet,
           'controlnet': controlnet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components




  def 						__lowercase(   self  :						Tuple  ,			a_  :						Any  ,			a_  :						Union[str, Any]=0					)->							Optional[Any]:




       """simple docstring"""
       if str(a_					).startswith('mps'					):
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.manual_seed(a_					)
       else:
            SCREAMING_SNAKE_CASE__					:					Any      =		torch.Generator(device=a_					).manual_seed(a_					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		2

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		[
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,			generator=a_  ,			device=torch.device(a_					)  ,			),
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,			generator=a_  ,			device=torch.device(a_					)  ,			),
       ]

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		floats_tensor(control_image[0].shape  ,			rng=random.Random(a_					)					).to(a_					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		image.cpu().permute(0  ,			2  ,			3  ,			1					)[0]
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		Image.fromarray(np.uinta(a_					)					).convert('RGB'					).resize((64, 64)					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		{
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
           'image': image,
           'control_image': control_image,
       }

       return inputs




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					:					str      =		self.pipeline_class(**a_					)
       pipe.to(a_					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		10.0
       SCREAMING_SNAKE_CASE__					:					Any      =		4

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					Any      =		steps
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		scale
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		pipe(**a_					)[0]

       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		steps
       SCREAMING_SNAKE_CASE__					:					int      =		scale
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		pipe(**a_  ,			control_guidance_start=0.1  ,			control_guidance_end=0.2					)[0]

       SCREAMING_SNAKE_CASE__					:					Any      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		steps
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		scale
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		pipe(**a_  ,			control_guidance_start=[0.1, 0.3]  ,			control_guidance_end=[0.2, 0.7]					)[0]

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		steps
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		scale
       SCREAMING_SNAKE_CASE__					:					str      =		pipe(**a_  ,			control_guidance_start=0.4  ,			control_guidance_end=[0.5, 0.8]					)[0]

       # make sure that all outputs are different
       assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
       assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
       assert np.sum(np.abs(output_a - output_a					)					) > 1e-3




  def 						__lowercase(   self  :						Any					)->							int:




       """simple docstring"""
       return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3					)




  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()  ,			reason='XFormers attention is only available with CUDA and `xformers` installed'  ,			)
  def 						__lowercase(   self  :						List[str]					)->							int:




       """simple docstring"""
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)




  def 						__lowercase(   self  :						int					)->							List[Any]:




       """simple docstring"""
       self._test_inference_batch_single_identical(expected_max_diff=2e-3					)




  def 						__lowercase(   self  :						Optional[int]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					:					str      =		self.pipeline_class(**a_					)
       pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)
       with tempfile.TemporaryDirectory() as tmpdir:
            try:
                 # save_pretrained is not implemented for Multi-ControlNet
                 pipe.save_pretrained(a_					)
            except NotImplementedError:
                 pass




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




  def 						__lowercase(   self  :						Dict					)->							Dict:




       """simple docstring"""
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		StableDiffusionControlNetImgaImgPipeline.from_pretrained(
           'runwayml/stable-diffusion-v1-5'  ,			safety_checker=a_  ,			controlnet=a_					)
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=a_					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.Generator(device='cpu'					).manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		'evil space-punk bird'
       SCREAMING_SNAKE_CASE__					:					int      =		load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'					).resize((512, 512)					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		load_image(
           'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'					).resize((512, 512)					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		pipe(
           a_  ,			a_  ,			control_image=a_  ,			generator=a_  ,			output_type='np'  ,			num_inference_steps=50  ,			strength=0.6  ,			)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		output.images[0]

       assert image.shape == (512, 512, 3)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'					)

       assert np.abs(expected_image - image					).max() < 9e-2








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


_lowerCamelCase    :		List[str]		      =    logging.get_logger(__name__)


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		List[str]		=		['pixel_values']







						def __init__(  self    :				List[Any]       ,	snake_case    :				bool = True       ,	snake_case    :				Optional[Dict[str, int]] = None       ,	snake_case    :				PILImageResampling = PILImageResampling.BICUBIC       ,	snake_case    :				bool = True       ,	snake_case    :				bool = True       ,	snake_case    :				Union[int, float] = 1 / 255       ,	snake_case    :				Dict[str, int] = None       ,	snake_case    :				bool = True       ,	snake_case    :				Optional[Union[float, List[float]]] = None       ,	snake_case    :				Optional[Union[float, List[float]]] = None       ,	**snake_case    :				Optional[int]       ,	):



											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				do_resize
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				do_rescale
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				do_normalize
											SCREAMING_SNAKE_CASE							:     Dict						=				do_center_crop
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				crop_size
											SCREAMING_SNAKE_CASE							:     str						=				size
											SCREAMING_SNAKE_CASE							:     List[str]						=				resample
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				rescale_factor
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def       lowerCamelCase_				(  self    :				int       ,	snake_case    :				np.ndarray       ,	snake_case    :				Dict[str, int]       ,	snake_case    :				PILImageResampling = PILImageResampling.BILINEAR       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				Any       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				get_size_dict(snake_case	)

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







						def       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				np.ndarray       ,	snake_case    :				Dict[str, int]       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				int       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				get_size_dict(snake_case	)
											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(snake_case       ,	size=(size['height'], size['width'])       ,	data_format=snake_case       ,	**snake_case	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				np.ndarray       ,	snake_case    :				float       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				Dict	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				np.ndarray       ,	snake_case    :				Union[float, List[float]]       ,	snake_case    :				Union[float, List[float]]       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				List[Any]       ,	):



											'''simple docstring'''


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







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



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE							:     int						=				do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE							:     List[str]						=				do_center_crop if do_center_crop is not None else self.do_center_crop
											SCREAMING_SNAKE_CASE							:     List[str]						=				crop_size if crop_size is not None else self.crop_size
											SCREAMING_SNAKE_CASE							:     List[Any]						=				get_size_dict(snake_case       ,	param_name='crop_size'       ,	default_to_square=snake_case	)
											SCREAMING_SNAKE_CASE							:     Any						=				resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE							:     Any						=				rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE							:     str						=				image_mean if image_mean is not None else self.image_mean
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				image_std if image_std is not None else self.image_std

											SCREAMING_SNAKE_CASE							:     List[str]						=				size if size is not None else self.size
											SCREAMING_SNAKE_CASE							:     str						=				get_size_dict(snake_case	)

											if not is_batched(snake_case	):
																SCREAMING_SNAKE_CASE							:     str						=				[images]

											if not valid_images(snake_case	):
																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.
											SCREAMING_SNAKE_CASE							:     Dict						=				[to_numpy_array(snake_case	) for image in images]

											if do_resize:
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				[self.resize(image=snake_case       ,	size=snake_case       ,	resample=snake_case	) for image in images]

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

											if do_rescale:
																SCREAMING_SNAKE_CASE							:     Tuple						=				[self.rescale(image=snake_case       ,	scale=snake_case	) for image in images]

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

											SCREAMING_SNAKE_CASE							:     List[str]						=				[to_channel_dimension_format(snake_case       ,	snake_case	) for image in images]

											SCREAMING_SNAKE_CASE							:     Any						=				{'pixel_values': images}
											return BatchFeature(data=snake_case       ,	tensor_type=snake_case	)