'''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    a__				(       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       unittest.TestCase			):
     lowerCamelCase  :					List[str]							=StableDiffusionInstructPixaPixPipeline
     lowerCamelCase  :					Optional[Any]							=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
     lowerCamelCase  :					Any							=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     lowerCamelCase  :					int							=IMAGE_TO_IMAGE_IMAGE_PARAMS
     lowerCamelCase  :					Optional[int]							=IMAGE_TO_IMAGE_IMAGE_PARAMS






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         torch.manual_seed(0   )
         __lowerCamelCase          =  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  ,    )
         __lowerCamelCase          =  PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_   )
         torch.manual_seed(0   )
         __lowerCamelCase          =  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   )
         __lowerCamelCase          =  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=10_00  ,    )
         __lowerCamelCase          =  CLIPTextModel(SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

         __lowerCamelCase          =  {
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		int  ,    a		:		int=0   ):





         """simple docstring"""



         __lowerCamelCase          =  floats_tensor((1, 3, 32, 32)  ,    rng=random.Random(SCREAMING_SNAKE_CASE_   )   ).to(SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  image.cpu().permute(0  ,    2  ,    3  ,    1   )[0]
         __lowerCamelCase          =  Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_   )   ).convert('''RGB'''   )
         if str(SCREAMING_SNAKE_CASE_   ).startswith('''mps'''   ):
             __lowerCamelCase          =  torch.manual_seed(SCREAMING_SNAKE_CASE_   )
         else:
             __lowerCamelCase          =  torch.Generator(device=SCREAMING_SNAKE_CASE_   ).manual_seed(SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  {
             """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        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase          =  self.get_dummy_components()
         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  sd_pipe.to(SCREAMING_SNAKE_CASE_   )
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

         __lowerCamelCase          =  self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  sd_pipe(**SCREAMING_SNAKE_CASE_   ).images
         __lowerCamelCase          =  image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase          =  np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase          =  self.get_dummy_components()
         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  sd_pipe.to(SCREAMING_SNAKE_CASE_   )
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

         __lowerCamelCase          =  self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  """french fries"""
         __lowerCamelCase          =  sd_pipe(**SCREAMING_SNAKE_CASE_  ,    negative_prompt=SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  output.images
         __lowerCamelCase          =  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase          =  np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase          =  self.get_dummy_components()
         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  sd_pipe.to(SCREAMING_SNAKE_CASE_   )
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

         __lowerCamelCase          =  self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  [inputs["""prompt"""]] * 2

         __lowerCamelCase          =  np.array(inputs['''image''']   ).astype(np.floataa   ) / 2_55.0
         __lowerCamelCase          =  torch.from_numpy(SCREAMING_SNAKE_CASE_   ).unsqueeze(0   ).to(SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  image / 2 + 0.5
         __lowerCamelCase          =  image.permute(0  ,    3  ,    1  ,    2   )
         __lowerCamelCase          =  image.repeat(2  ,    1  ,    1  ,    1   )

         __lowerCamelCase          =  sd_pipe(**SCREAMING_SNAKE_CASE_   ).images
         __lowerCamelCase          =  image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         __lowerCamelCase          =  np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase          =  self.get_dummy_components()
         __lowerCamelCase          =  EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85  ,    beta_end=0.0_12  ,    beta_schedule='''scaled_linear'''   )
         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  sd_pipe.to(SCREAMING_SNAKE_CASE_   )
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

         __lowerCamelCase          =  self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  sd_pipe(**SCREAMING_SNAKE_CASE_   ).images
         __lowerCamelCase          =  image[0, -3:, -3:, -1]

         __lowerCamelCase          =  [round(SCREAMING_SNAKE_CASE_  ,    4   ) for x in image_slice.flatten().tolist()]
         print(''','''.join([str(SCREAMING_SNAKE_CASE_   ) for x in slice]   )   )

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase          =  np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_dummy_components()
         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_  ,    do_normalize=SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  pipe.to(SCREAMING_SNAKE_CASE_   )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

         __lowerCamelCase          =  pipe(**self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_  ,    input_image_type='''pt'''   )   )[0]

         __lowerCamelCase          =  components["""vae"""]
         __lowerCamelCase          =  self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_  ,    input_image_type='''pt'''   )

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

         __lowerCamelCase          =  pipe(**SCREAMING_SNAKE_CASE_   )[0]

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




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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]  ,    a		:		Union[str, Any]=0   ):





         """simple docstring"""



         __lowerCamelCase          =  torch.manual_seed(SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  load_image(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''   )
         __lowerCamelCase          =  {
             """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        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''  ,    safety_checker=SCREAMING_SNAKE_CASE_   )
         pipe.to(SCREAMING_SNAKE_CASE_   )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
         pipe.enable_attention_slicing()

         __lowerCamelCase          =  self.get_inputs()
         __lowerCamelCase          =  pipe(**SCREAMING_SNAKE_CASE_   ).images
         __lowerCamelCase          =  image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase          =  np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''  ,    safety_checker=SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.to(SCREAMING_SNAKE_CASE_   )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
         pipe.enable_attention_slicing()

         __lowerCamelCase          =  self.get_inputs()
         __lowerCamelCase          =  pipe(**SCREAMING_SNAKE_CASE_   ).images
         __lowerCamelCase          =  image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase          =  np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''  ,    safety_checker=SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  DDIMScheduler.from_config(pipe.scheduler.config   )
         pipe.to(SCREAMING_SNAKE_CASE_   )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
         pipe.enable_attention_slicing()

         __lowerCamelCase          =  self.get_inputs()
         __lowerCamelCase          =  pipe(**SCREAMING_SNAKE_CASE_   ).images
         __lowerCamelCase          =  image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase          =  np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  0

         def callback_fn(a		:		Union[str, Any]  ,    a		:		List[Any]  ,    a		:		Tuple   ) -> None:
             __lowerCamelCase          =  True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 1:
                 __lowerCamelCase          =  latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 64)
                 __lowerCamelCase          =  latents[0, -3:, -3:, -1]
                 __lowerCamelCase          =  np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]   )

                 assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5e-2
             elif step == 2:
                 __lowerCamelCase          =  latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 64)
                 __lowerCamelCase          =  latents[0, -3:, -3:, -1]
                 __lowerCamelCase          =  np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]   )

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

         __lowerCamelCase          =  False

         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''  ,    safety_checker=SCREAMING_SNAKE_CASE_  ,    torch_dtype=torch.floataa   )
         __lowerCamelCase          =  pipe.to(SCREAMING_SNAKE_CASE_   )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
         pipe.enable_attention_slicing()

         __lowerCamelCase          =  self.get_inputs()
         pipe(**SCREAMING_SNAKE_CASE_  ,    callback=SCREAMING_SNAKE_CASE_  ,    callback_steps=1   )
         assert callback_fn.has_been_called
         assert number_of_steps == 3






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



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

         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''  ,    safety_checker=SCREAMING_SNAKE_CASE_  ,    torch_dtype=torch.floataa   )
         __lowerCamelCase          =  pipe.to(SCREAMING_SNAKE_CASE_   )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
         pipe.enable_attention_slicing(1   )
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase          =  self.get_inputs()
         __lowerCamelCase          =  pipe(**SCREAMING_SNAKE_CASE_   )

         __lowerCamelCase          =  torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         __lowerCamelCase          =  inputs["""image"""].resize((5_04, 5_04)   )

         __lowerCamelCase          =  """timbrooks/instruct-pix2pix"""
         __lowerCamelCase          =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
             SCREAMING_SNAKE_CASE_  ,    safety_checker=SCREAMING_SNAKE_CASE_  ,    )
         pipe.to(SCREAMING_SNAKE_CASE_   )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
         pipe.enable_attention_slicing()

         __lowerCamelCase          =  pipe(**SCREAMING_SNAKE_CASE_   )
         __lowerCamelCase          =  output.images[0]

         __lowerCamelCase          =  image[2_55:2_58, 3_83:3_86, -1]

         assert image.shape == (5_04, 5_04, 3)
         __lowerCamelCase          =  np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]   )

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

'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__UpperCAmelCase												=logging.get_logger(__name__)




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=["pixel_values"]






     def __init__(			self		:		List[str]  ,    a		:		bool = True  ,    a		:		Dict[str, int] = None  ,    a		:		int = 0.9  ,    a		:		PILImageResampling = PILImageResampling.BICUBIC  ,    a		:		bool = True  ,    a		:		Dict[str, int] = None  ,    a		:		Union[int, float] = 1 / 2_55  ,    a		:		bool = True  ,    a		:		bool = True  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    **a		:		Dict  ,    ):





         """simple docstring"""



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

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






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





         """simple docstring"""



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

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

         return resize(a  ,    size=a  ,    resample=a  ,    data_format=a  ,    **a   )






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





         """simple docstring"""



         __lowerCamelCase          =  get_size_dict(a   )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""   )
         return center_crop(a  ,    size=(size['''height'''], size['''width'''])  ,    data_format=a  ,    **a   )






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





         """simple docstring"""



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






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





         """simple docstring"""



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






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





         """simple docstring"""



         __lowerCamelCase          =  do_resize if do_resize is not None else self.do_resize
         __lowerCamelCase          =  crop_pct if crop_pct is not None else self.crop_pct
         __lowerCamelCase          =  resample if resample is not None else self.resample
         __lowerCamelCase          =  do_center_crop if do_center_crop is not None else self.do_center_crop
         __lowerCamelCase          =  do_rescale if do_rescale is not None else self.do_rescale
         __lowerCamelCase          =  rescale_factor if rescale_factor is not None else self.rescale_factor
         __lowerCamelCase          =  do_normalize if do_normalize is not None else self.do_normalize
         __lowerCamelCase          =  image_mean if image_mean is not None else self.image_mean
         __lowerCamelCase          =  image_std if image_std is not None else self.image_std

         __lowerCamelCase          =  size if size is not None else self.size
         __lowerCamelCase          =  get_size_dict(a  ,    default_to_square=a   )
         __lowerCamelCase          =  crop_size if crop_size is not None else self.crop_size
         __lowerCamelCase          =  get_size_dict(a  ,    param_name='''crop_size'''   )

         __lowerCamelCase          =  make_list_of_images(a   )

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

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

         if do_center_crop and crop_pct is None:
             raise ValueError('''Crop_pct must be specified if do_center_crop is True.'''   )

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

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

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

         if do_resize:
             __lowerCamelCase          =  [self.resize(image=a  ,    size=a  ,    crop_pct=a  ,    resample=a   ) for image in images]

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

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

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

         __lowerCamelCase          =  [to_channel_dimension_format(a  ,    a   ) for image in images]

         __lowerCamelCase          =  {'''pixel_values''': images}
         return BatchFeature(data=a  ,    tensor_type=a   )
