

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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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		:       Union[str, Any]						    =	logging.get_logger(__name__)







class 			lowercase    (	__lowerCamelCase      ):
    __SCREAMING_SNAKE_CASE :			int	      =      ['pixel_values']




    def __init__( self				,       snake_case = True				,       snake_case = None				,       snake_case = PILImageResampling.BICUBIC				,       snake_case = True				,       snake_case = 1 / 255				,       snake_case = True				,       snake_case = None				,       snake_case = None				,       snake_case = True				,       **snake_case				,       ):
           super().__init__(**UpperCamelCase_	)
           snake_case_          =   size if size is not None else {'height': 384, 'width': 384}
           snake_case_          =   get_size_dict(UpperCamelCase_				,       default_to_square=UpperCamelCase_	)

           snake_case_          =   do_resize
           snake_case_          =   size
           snake_case_          =   resample
           snake_case_          =   do_rescale
           snake_case_          =   rescale_factor
           snake_case_          =   do_normalize
           snake_case_          =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           snake_case_          =   image_std if image_std is not None else OPENAI_CLIP_STD
           snake_case_          =   do_convert_rgb




    def    a							( self				,       snake_case				,       snake_case				,       snake_case = PILImageResampling.BICUBIC				,       snake_case = None				,       **snake_case				,       ):
           snake_case_          =   get_size_dict(UpperCamelCase_				,       default_to_square=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()}'''	)
           snake_case_          =   (size['height'], size['width'])
           return resize(UpperCamelCase_				,       size=UpperCamelCase_				,       resample=UpperCamelCase_				,       data_format=UpperCamelCase_				,       **UpperCamelCase_	)




    def    a							( self				,       snake_case				,       snake_case				,       snake_case = None				,       **snake_case				,       ):
           return rescale(UpperCamelCase_				,       scale=UpperCamelCase_				,       data_format=UpperCamelCase_				,       **UpperCamelCase_	)




    def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case = None				,       **snake_case				,       ):
           return normalize(UpperCamelCase_				,       mean=UpperCamelCase_				,       std=UpperCamelCase_				,       data_format=UpperCamelCase_				,       **UpperCamelCase_	)




    def    a							( self				,       snake_case				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = ChannelDimension.FIRST				,       **snake_case				,       ):
           snake_case_          =   do_resize if do_resize is not None else self.do_resize
           snake_case_          =   resample if resample is not None else self.resample
           snake_case_          =   do_rescale if do_rescale is not None else self.do_rescale
           snake_case_          =   rescale_factor if rescale_factor is not None else self.rescale_factor
           snake_case_          =   do_normalize if do_normalize is not None else self.do_normalize
           snake_case_          =   image_mean if image_mean is not None else self.image_mean
           snake_case_          =   image_std if image_std is not None else self.image_std
           snake_case_          =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           snake_case_          =   size if size is not None else self.size
           snake_case_          =   get_size_dict(UpperCamelCase_				,       default_to_square=UpperCamelCase_	)

           snake_case_          =   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.'	)

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                  snake_case_          =   [convert_to_rgb(UpperCamelCase_	) for image in images]

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

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

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

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

           snake_case_          =   [to_channel_dimension_format(UpperCamelCase_				,       UpperCamelCase_	) for image in images]

           snake_case_          =   BatchFeature(data={'pixel_values': images}				,       tensor_type=UpperCamelCase_	)

           return encoded_outputs







import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







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




				def    a							( self	):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-canny'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_          =   controlnet_params

											snake_case_          =   'bird'
											snake_case_          =   jax.device_count()
											snake_case_          =   pipe.prepare_text_inputs([prompts] * num_samples	)

											snake_case_          =   load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'	)
											snake_case_          =   pipe.prepare_image_inputs([canny_image] * num_samples	)

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_          =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
											snake_case_          =   images[0, 253:256, 253:256, -1]

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   jnp.array(
											    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




				def    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-openpose'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_          =   controlnet_params

											snake_case_          =   'Chef in the kitchen'
											snake_case_          =   jax.device_count()
											snake_case_          =   pipe.prepare_text_inputs([prompts] * num_samples	)

											snake_case_          =   load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'	)
											snake_case_          =   pipe.prepare_image_inputs([pose_image] * num_samples	)

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_          =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
											snake_case_          =   images[0, 253:256, 253:256, -1]

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   jnp.array(
											    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




