





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			       =      logging.get_logger(__name__)







class 			__A       (      lowerCamelCase__  ):




						"""simple docstring"""







						UpperCAmelCase__						=      ["""pixel_values"""]


						def __init__(		self      ,							a__ = True      ,							a__ = None      ,							a__ = PILImageResampling.BICUBIC      ,							a__ = True      ,							a__ = True      ,							a__ = 1 / 255      ,							a__ = None      ,							a__ = True      ,							a__ = None      ,							a__ = None      ,							**a__      ,							):



													"""simple docstring"""





													super().__init__(**a__)
													_lowerCamelCase							:     Dict	=		size if size is not None else {'''height''': 224, '''width''': 224}
													_lowerCamelCase							:     Union[str, Any]	=		get_size_dict(a__)
													_lowerCamelCase							:     str	=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
													_lowerCamelCase							:     str	=		get_size_dict(a__      ,							default_to_square=a__      ,							param_name='''crop_size''')

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


						def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__ = PILImageResampling.BILINEAR      ,							a__ = None      ,							**a__      ,							):



													"""simple docstring"""





													_lowerCamelCase							:     int	=		get_size_dict(a__)

													if "shortest_edge" in size:
																				_lowerCamelCase							:     int	=		get_resize_output_image_size(a__      ,							size=size['''shortest_edge''']      ,							default_to_square=a__)
																				# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
													elif "height" in size and "width" in size:
																				_lowerCamelCase							:     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(a__      ,							size=a__      ,							resample=a__      ,							data_format=a__      ,							**a__)


						def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__ = None      ,							**a__      ,							):



													"""simple docstring"""





													_lowerCamelCase							:     str	=		get_size_dict(a__)
													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(a__      ,							size=(size['''height'''], size['''width'''])      ,							data_format=a__      ,							**a__)


						def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__ = None      ,							**a__):



													"""simple docstring"""





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


						def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__      ,							a__ = None      ,							**a__      ,							):



													"""simple docstring"""





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



						def 				__snake_case			(		self      ,							a__      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = ChannelDimension.FIRST      ,							**a__      ,							):



													"""simple docstring"""





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

													_lowerCamelCase							:     List[str]	=		size if size is not None else self.size
													_lowerCamelCase							:     Tuple	=		get_size_dict(a__)

													if not is_batched(a__):
																				_lowerCamelCase							:     int	=		[images]

													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:
																				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.
													_lowerCamelCase							:     Optional[int]	=		[to_numpy_array(a__) for image in images]

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

													if do_center_crop:
																				_lowerCamelCase							:     Optional[int]	=		[self.center_crop(image=a__      ,							size=a__) for image in images]

													if do_rescale:
																				_lowerCamelCase							:     Any	=		[self.rescale(image=a__      ,							scale=a__) for image in images]

													if do_normalize:
																				_lowerCamelCase							:     Optional[int]	=		[self.normalize(image=a__      ,							mean=a__      ,							std=a__) for image in images]

													_lowerCamelCase							:     List[Any]	=		[to_channel_dimension_format(a__      ,							a__) for image in images]

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








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 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             super().tearDown()
             gc.collect()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-canny'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Union[str, Any]	=		controlnet_params

             _lowerCamelCase							:     str	=		'''bird'''
             _lowerCamelCase							:     Union[str, Any]	=		jax.device_count()
             _lowerCamelCase							:     Dict	=		pipe.prepare_text_inputs([prompts] * num_samples)

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

             _lowerCamelCase							:     Union[str, Any]	=		jax.random.PRNGKey(0)
             _lowerCamelCase							:     Tuple	=		jax.random.split(a__      ,							jax.device_count())

             _lowerCamelCase							:     Dict	=		replicate(a__)
             _lowerCamelCase							:     Tuple	=		shard(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)

             _lowerCamelCase							:     Optional[Any]	=		pipe(
                 prompt_ids=a__      ,							image=a__      ,							params=a__      ,							prng_seed=a__      ,							num_inference_steps=50      ,							jit=a__      ,							).images
             assert images.shape == (jax.device_count(), 1, 768, 512, 3)

             _lowerCamelCase							:     Optional[int]	=		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
             _lowerCamelCase							:     List[Any]	=		images[0, 253:256, 253:256, -1]

             _lowerCamelCase							:     Union[str, Any]	=		jnp.asarray(jax.device_get(image_slice.flatten()))
             _lowerCamelCase							:     Optional[int]	=		jnp.array(
                 [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
             print(F"""output_slice: {output_slice}""")
             assert jnp.abs(output_slice - expected_slice).max() < 1e-2



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Any	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-openpose'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase,  _lowerCamelCase							:     Any	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Optional[Any]	=		controlnet_params

             _lowerCamelCase							:     Optional[Any]	=		'''Chef in the kitchen'''
             _lowerCamelCase							:     int	=		jax.device_count()
             _lowerCamelCase							:     Dict	=		pipe.prepare_text_inputs([prompts] * num_samples)

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

             _lowerCamelCase							:     Optional[Any]	=		jax.random.PRNGKey(0)
             _lowerCamelCase							:     List[str]	=		jax.random.split(a__      ,							jax.device_count())

             _lowerCamelCase							:     str	=		replicate(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)
             _lowerCamelCase							:     List[Any]	=		shard(a__)

             _lowerCamelCase							:     Any	=		pipe(
                 prompt_ids=a__      ,							image=a__      ,							params=a__      ,							prng_seed=a__      ,							num_inference_steps=50      ,							jit=a__      ,							).images
             assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

             _lowerCamelCase							:     Optional[int]	=		jnp.asarray(jax.device_get(image_slice.flatten()))
             _lowerCamelCase							:     Optional[int]	=		jnp.array(
                 [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
             print(F"""output_slice: {output_slice}""")
             assert jnp.abs(output_slice - expected_slice).max() < 1e-2

