






"""simple docstring"""



# This model implementation is heavily based on:

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,
    StableDiffusionControlNetInpaintPipeline,
    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 (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class        lowerCAmelCase__       (  __magic_name__				,		__magic_name__				,		__magic_name__				,		unittest.TestCase					):
   SCREAMING_SNAKE_CASE_					      =StableDiffusionControlNetInpaintPipeline
   SCREAMING_SNAKE_CASE_					      =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   SCREAMING_SNAKE_CASE_					      =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   SCREAMING_SNAKE_CASE_					      =frozenset({'''control_image'''}					)  # skip `image` and `mask` for now, only test for control_image
   SCREAMING_SNAKE_CASE_					      =TEXT_TO_IMAGE_IMAGE_PARAMS
   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Any				=				UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=9				,       out_channels=4				,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				,       cross_attention_dim=3_2				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Optional[int]				=				ControlNetModel(
            block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       in_channels=4				,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,       cross_attention_dim=3_2				,       conditioning_embedding_out_channels=(1_6, 3_2)				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  List[str]				=				DDIMScheduler(
            beta_start=0.0_0085				,       beta_end=0.012				,       beta_schedule="scaled_linear"				,       clip_sample=snake_case__				,       set_alpha_to_one=snake_case__				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  List[Any]				=				AutoencoderKL(
            block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,       latent_channels=4				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Dict				=				CLIPTextConfig(
            bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       )
        UpperCAmelCase__		:  Dict				=				CLIPTextModel(snake_case__				)
        UpperCAmelCase__		:  List[str]				=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

        UpperCAmelCase__		:  List[str]				=				{
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components
   def 	__a				(						self	:     Dict				,       snake_case__	:     Optional[int]				,       snake_case__	:     Optional[Any]=0				):


        '''simple docstring'''
        if str(snake_case__				).startswith("mps"				):
             UpperCAmelCase__		:  Dict				=				torch.manual_seed(snake_case__				)
        else:
             UpperCAmelCase__		:  Optional[Any]				=				torch.Generator(device=snake_case__				).manual_seed(snake_case__				)

        UpperCAmelCase__		:  Optional[Any]				=				2
        UpperCAmelCase__		:  Dict				=				randn_tensor(
            (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)				,       generator=snake_case__				,       device=torch.device(snake_case__				)				,       )
        UpperCAmelCase__		:  str				=				floats_tensor((1, 3, 3_2, 3_2)				,       rng=random.Random(snake_case__				)				).to(snake_case__				)
        UpperCAmelCase__		:  int				=				init_image.cpu().permute(0				,       2				,       3				,       1				)[0]

        UpperCAmelCase__		:  List[Any]				=				Image.fromarray(np.uinta(snake_case__				)				).convert("RGB"				).resize((6_4, 6_4)				)
        UpperCAmelCase__		:  int				=				Image.fromarray(np.uinta(init_image + 4				)				).convert("RGB"				).resize((6_4, 6_4)				)

        UpperCAmelCase__		:  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,
            "mask_image": mask_image,
            "control_image": control_image,
        }

        return inputs
   def 	__a				(						self	:     Tuple				):


        '''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 	__a				(						self	:     Optional[int]				):


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




   def 	__a				(						self	:     List[str]				):


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




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =StableDiffusionControlNetInpaintPipeline
   SCREAMING_SNAKE_CASE_					      =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   SCREAMING_SNAKE_CASE_					      =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   SCREAMING_SNAKE_CASE_					      =frozenset([]					)
   def 	__a				(						self	:     str				):


        '''simple docstring'''
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Any				=				UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=4				,       out_channels=4				,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				,       cross_attention_dim=3_2				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Any				=				ControlNetModel(
            block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       in_channels=4				,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,       cross_attention_dim=3_2				,       conditioning_embedding_out_channels=(1_6, 3_2)				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  int				=				DDIMScheduler(
            beta_start=0.0_0085				,       beta_end=0.012				,       beta_schedule="scaled_linear"				,       clip_sample=snake_case__				,       set_alpha_to_one=snake_case__				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Optional[int]				=				AutoencoderKL(
            block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,       latent_channels=4				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  List[Any]				=				CLIPTextConfig(
            bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       )
        UpperCAmelCase__		:  Tuple				=				CLIPTextModel(snake_case__				)
        UpperCAmelCase__		:  int				=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

        UpperCAmelCase__		:  List[Any]				=				{
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components




class        lowerCAmelCase__       (  __magic_name__				,		__magic_name__				,		unittest.TestCase					):
   SCREAMING_SNAKE_CASE_					      =StableDiffusionControlNetInpaintPipeline
   SCREAMING_SNAKE_CASE_					      =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   SCREAMING_SNAKE_CASE_					      =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   def 	__a				(						self	:     Optional[Any]				):


        '''simple docstring'''
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Union[str, Any]				=				UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=9				,       out_channels=4				,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				,       cross_attention_dim=3_2				,       )
        torch.manual_seed(0				)

        def init_weights(snake_case__	:     List[str]				):
             if isinstance(snake_case__				,       torch.nn.Convad				):
                  torch.nn.init.normal(m.weight				)
                  m.bias.data.fill_(1.0				)

        UpperCAmelCase__		:  List[Any]				=				ControlNetModel(
            block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       in_channels=4				,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,       cross_attention_dim=3_2				,       conditioning_embedding_out_channels=(1_6, 3_2)				,       )
        controlneta.controlnet_down_blocks.apply(snake_case__				)

        torch.manual_seed(0				)
        UpperCAmelCase__		:  List[Any]				=				ControlNetModel(
            block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       in_channels=4				,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,       cross_attention_dim=3_2				,       conditioning_embedding_out_channels=(1_6, 3_2)				,       )
        controlneta.controlnet_down_blocks.apply(snake_case__				)

        torch.manual_seed(0				)
        UpperCAmelCase__		:  Any				=				DDIMScheduler(
            beta_start=0.0_0085				,       beta_end=0.012				,       beta_schedule="scaled_linear"				,       clip_sample=snake_case__				,       set_alpha_to_one=snake_case__				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Optional[int]				=				AutoencoderKL(
            block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,       latent_channels=4				,       )
        torch.manual_seed(0				)
        UpperCAmelCase__		:  List[Any]				=				CLIPTextConfig(
            bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       )
        UpperCAmelCase__		:  Dict				=				CLIPTextModel(snake_case__				)
        UpperCAmelCase__		:  int				=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

        UpperCAmelCase__		:  Tuple				=				MultiControlNetModel([controlneta, controlneta]				)

        UpperCAmelCase__		:  List[str]				=				{
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components
   def 	__a				(						self	:     Tuple				,       snake_case__	:     str				,       snake_case__	:     int=0				):


        '''simple docstring'''
        if str(snake_case__				).startswith("mps"				):
             UpperCAmelCase__		:  List[Any]				=				torch.manual_seed(snake_case__				)
        else:
             UpperCAmelCase__		:  List[str]				=				torch.Generator(device=snake_case__				).manual_seed(snake_case__				)

        UpperCAmelCase__		:  int				=				2

        UpperCAmelCase__		:  Optional[int]				=				[
            randn_tensor(
                (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)				,       generator=snake_case__				,       device=torch.device(snake_case__				)				,       ),
            randn_tensor(
                (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)				,       generator=snake_case__				,       device=torch.device(snake_case__				)				,       ),
        ]
        UpperCAmelCase__		:  int				=				floats_tensor((1, 3, 3_2, 3_2)				,       rng=random.Random(snake_case__				)				).to(snake_case__				)
        UpperCAmelCase__		:  List[str]				=				init_image.cpu().permute(0				,       2				,       3				,       1				)[0]

        UpperCAmelCase__		:  Any				=				Image.fromarray(np.uinta(snake_case__				)				).convert("RGB"				).resize((6_4, 6_4)				)
        UpperCAmelCase__		:  Optional[Any]				=				Image.fromarray(np.uinta(init_image + 4				)				).convert("RGB"				).resize((6_4, 6_4)				)

        UpperCAmelCase__		:  List[str]				=				{
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
            "image": image,
            "mask_image": mask_image,
            "control_image": control_image,
        }

        return inputs
   def 	__a				(						self	:     List[str]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				self.get_dummy_components()
        UpperCAmelCase__		:  Union[str, Any]				=				self.pipeline_class(**snake_case__				)
        pipe.to(snake_case__				)

        UpperCAmelCase__		:  Optional[Any]				=				10.0
        UpperCAmelCase__		:  Optional[Any]				=				4

        UpperCAmelCase__		:  Any				=				self.get_dummy_inputs(snake_case__				)
        UpperCAmelCase__		:  List[Any]				=				steps
        UpperCAmelCase__		:  Optional[int]				=				scale
        UpperCAmelCase__		:  List[str]				=				pipe(**snake_case__				)[0]

        UpperCAmelCase__		:  Union[str, Any]				=				self.get_dummy_inputs(snake_case__				)
        UpperCAmelCase__		:  List[str]				=				steps
        UpperCAmelCase__		:  Union[str, Any]				=				scale
        UpperCAmelCase__		:  Union[str, Any]				=				pipe(**snake_case__				,       control_guidance_start=0.1				,       control_guidance_end=0.2				)[0]

        UpperCAmelCase__		:  Union[str, Any]				=				self.get_dummy_inputs(snake_case__				)
        UpperCAmelCase__		:  List[Any]				=				steps
        UpperCAmelCase__		:  Tuple				=				scale
        UpperCAmelCase__		:  str				=				pipe(**snake_case__				,       control_guidance_start=[0.1, 0.3]				,       control_guidance_end=[0.2, 0.7]				)[0]

        UpperCAmelCase__		:  Dict				=				self.get_dummy_inputs(snake_case__				)
        UpperCAmelCase__		:  Optional[Any]				=				steps
        UpperCAmelCase__		:  Tuple				=				scale
        UpperCAmelCase__		:  Dict				=				pipe(**snake_case__				,       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 	__a				(						self	:     List[Any]				):


        '''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 	__a				(						self	:     Any				):


        '''simple docstring'''
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)
   def 	__a				(						self	:     Optional[int]				):


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




   def 	__a				(						self	:     Tuple				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[Any]				=				self.get_dummy_components()
        UpperCAmelCase__		:  str				=				self.pipeline_class(**snake_case__				)
        pipe.to(snake_case__				)
        pipe.set_progress_bar_config(disable=snake_case__				)
        with tempfile.TemporaryDirectory() as tmpdir:
             try:
                  # save_pretrained is not implemented for Multi-ControlNet
                  pipe.save_pretrained(snake_case__				)
             except NotImplementedError:
                  pass




@slow
@require_torch_gpu
class        lowerCAmelCase__       (  unittest.TestCase					):
   def 	__a				(						self	:     int				):


        '''simple docstring'''
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[Any]				=				ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"				)

        UpperCAmelCase__		:  Optional[int]				=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting"				,       safety_checker=snake_case__				,       controlnet=snake_case__				)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=snake_case__				)

        UpperCAmelCase__		:  str				=				torch.Generator(device="cpu"				).manual_seed(0				)
        UpperCAmelCase__		:  Any				=				load_image(
            "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"				).resize((5_1_2, 5_1_2)				)

        UpperCAmelCase__		:  Optional[Any]				=				load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_mask.png"				).resize((5_1_2, 5_1_2)				)

        UpperCAmelCase__		:  str				=				"pitch black hole"

        UpperCAmelCase__		:  Dict				=				load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"				).resize((5_1_2, 5_1_2)				)

        UpperCAmelCase__		:  int				=				pipe(
            snake_case__				,       image=snake_case__				,       mask_image=snake_case__				,       control_image=snake_case__				,       generator=snake_case__				,       output_type="np"				,       num_inference_steps=3				,       )

        UpperCAmelCase__		:  List[str]				=				output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        UpperCAmelCase__		:  Dict				=				load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"				)

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




   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"				)

        UpperCAmelCase__		:  Dict				=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5"				,       safety_checker=snake_case__				,       controlnet=snake_case__				)
        UpperCAmelCase__		:  Dict				=				DDIMScheduler.from_config(pipe.scheduler.config				)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=snake_case__				)

        UpperCAmelCase__		:  int				=				torch.Generator(device="cpu"				).manual_seed(3_3				)

        UpperCAmelCase__		:  List[str]				=				load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"				)
        UpperCAmelCase__		:  Any				=				init_image.resize((5_1_2, 5_1_2)				)

        UpperCAmelCase__		:  Tuple				=				load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"				)
        UpperCAmelCase__		:  Tuple				=				mask_image.resize((5_1_2, 5_1_2)				)

        UpperCAmelCase__		:  Union[str, Any]				=				"a handsome man with ray-ban sunglasses"

        def make_inpaint_condition(snake_case__	:     int				,       snake_case__	:     Optional[Any]				):
             UpperCAmelCase__		:  str				=				np.array(image.convert("RGB"				)				).astype(np.floataa				) / 255.0
             UpperCAmelCase__		:  List[Any]				=				np.array(image_mask.convert("L"				)				).astype(np.floataa				) / 255.0

             assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
             UpperCAmelCase__		:  List[Any]				=				-1.0  # set as masked pixel
             UpperCAmelCase__		:  Optional[Any]				=				np.expand_dims(snake_case__				,       0				).transpose(0				,       3				,       1				,       2				)
             UpperCAmelCase__		:  Dict				=				torch.from_numpy(snake_case__				)
             return image

        UpperCAmelCase__		:  Optional[Any]				=				make_inpaint_condition(snake_case__				,       snake_case__				)

        UpperCAmelCase__		:  int				=				pipe(
            snake_case__				,       image=snake_case__				,       mask_image=snake_case__				,       control_image=snake_case__				,       guidance_scale=9.0				,       eta=1.0				,       generator=snake_case__				,       num_inference_steps=2_0				,       output_type="np"				,       )
        UpperCAmelCase__		:  Optional[int]				=				output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        UpperCAmelCase__		:  Dict				=				load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"				)

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










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


_lowerCAmelCase     :     Any            =	logging.get_logger(__name__)




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =['''pixel_values''']
   def __init__(						self	:     str				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Dict[str, int]] = None				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     bool = True				,       snake_case__	:     Union[int, float] = 1 / 2_5_5				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        super().__init__(**snake_case__				)
        UpperCAmelCase__		:  Dict				=				size if size is not None else {"height": 2_2_4, "width": 2_2_4}
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)
        UpperCAmelCase__		:  Optional[Any]				=				do_resize
        UpperCAmelCase__		:  Any				=				do_rescale
        UpperCAmelCase__		:  Optional[int]				=				do_normalize
        UpperCAmelCase__		:  Dict				=				size
        UpperCAmelCase__		:  Optional[Any]				=				resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor
        UpperCAmelCase__		:  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 	__a				(						self	:     Optional[Any]				,       snake_case__	:     np.ndarray				,       snake_case__	:     Dict[str, int]				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				get_size_dict(snake_case__				)
        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()}'				)
        return resize(
            snake_case__				,       size=(size["height"], size["width"])				,       resample=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     Any				,       snake_case__	:     np.ndarray				,       snake_case__	:     float				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Any				):


        '''simple docstring'''
        return rescale(snake_case__				,       scale=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     str				,       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 	__a				(						self	:     Union[str, Any]				,       snake_case__	:     ImageInput				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Dict[str, int] = None				,       snake_case__	:     PILImageResampling = 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__	:     List[Any]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Union[str, Any]				=				do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase__		:  Optional[Any]				=				do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase__		:  str				=				do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase__		:  Dict				=				resample if resample is not None else self.resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase__		:  Optional[int]				=				image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase__		:  Optional[int]				=				image_std if image_std is not None else self.image_std

        UpperCAmelCase__		:  Any				=				size if size is not None else self.size
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)

        UpperCAmelCase__		:  List[Any]				=				make_list_of_images(snake_case__				)

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

        # All transformations expect numpy arrays.
        UpperCAmelCase__		:  Optional[int]				=				[to_numpy_array(snake_case__				) for image in images]

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

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

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

        UpperCAmelCase__		:  Any				=				[to_channel_dimension_format(snake_case__				,       snake_case__				) for image in images]

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


