
"""simple docstring"""







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase__   					=		logging.get_logger(__name__)




class  A__       (	_lowerCamelCase):
		A_						:       int				   =	['pixel_values']

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE = True       ,		_SCREAMING_SNAKE_CASE = 1 / 2_55       ,		_SCREAMING_SNAKE_CASE = True       ,		_SCREAMING_SNAKE_CASE = 8       ,		**_SCREAMING_SNAKE_CASE       ,		):
			super().__init__(**_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						int									=      do_rescale
			__lowerCAmelCase			:						int									=      rescale_factor
			__lowerCAmelCase			:						Tuple									=      do_pad
			__lowerCAmelCase			:						List[Any]									=      pad_size

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		**_SCREAMING_SNAKE_CASE ):
			return rescale(_SCREAMING_SNAKE_CASE       ,		scale=_SCREAMING_SNAKE_CASE       ,		data_format=_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						Dict									=      get_image_size(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      (old_height // size + 1) * size - old_height
			__lowerCAmelCase			:						List[Any]									=      (old_width // size + 1) * size - old_width

			return pad(_SCREAMING_SNAKE_CASE       ,		((0, pad_height), (0, pad_width))       ,		mode='symmetric'       ,		data_format=_SCREAMING_SNAKE_CASE )







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,		**_SCREAMING_SNAKE_CASE       ,		):
			__lowerCAmelCase			:						List[Any]									=      do_rescale if do_rescale is not None else self.do_rescale
			__lowerCAmelCase			:						Tuple									=      rescale_factor if rescale_factor is not None else self.rescale_factor
			__lowerCAmelCase			:						Any									=      do_pad if do_pad is not None else self.do_pad
			__lowerCAmelCase			:						int									=      pad_size if pad_size is not None else self.pad_size

			__lowerCAmelCase			:						List[str]									=      make_list_of_images(_SCREAMING_SNAKE_CASE )

			if not valid_images(_SCREAMING_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_rescale and rescale_factor is None:
				raise ValueError('Rescale factor must be specified if do_rescale is True.' )

			# All transformations expect numpy arrays.
			__lowerCAmelCase			:						int									=      [to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]

			if do_rescale:
				__lowerCAmelCase			:						Union[str, Any]									=      [self.rescale(image=_SCREAMING_SNAKE_CASE       ,		scale=_SCREAMING_SNAKE_CASE ) for image in images]

			if do_pad:
				__lowerCAmelCase			:						Tuple									=      [self.pad(_SCREAMING_SNAKE_CASE       ,		size=_SCREAMING_SNAKE_CASE ) for image in images]

			__lowerCAmelCase			:						Optional[int]									=      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) for image in images]

			__lowerCAmelCase			:						Optional[Any]									=      {'pixel_values': images}
			return BatchFeature(data=_SCREAMING_SNAKE_CASE       ,		tensor_type=_SCREAMING_SNAKE_CASE )




"""simple docstring"""
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 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Any:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_,       snake_case_ :List[str]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-canny"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :Union[str, Any]						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :Union[str, Any]						=			controlnet_params

											snake_case_ :Union[str, Any]						=			"""bird"""
											snake_case_ :List[Any]						=			jax.device_count()
											snake_case_ :List[Any]						=			pipe.prepare_text_inputs([prompts] * num_samples						)

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

											snake_case_ :Any						=			jax.random.PRNGKey(0						)
											snake_case_ :List[str]						=			jax.random.split(snake_case						,     jax.device_count()						)

											snake_case_ :List[Any]						=			replicate(snake_case						)
											snake_case_ :List[str]						=			shard(snake_case						)
											snake_case_ :str						=			shard(snake_case						)

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

											snake_case_ :str						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Dict						=			jnp.array(
											    [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]						)
											print(f"""output_slice: {output_slice}"""						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1E-2






					def    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											snake_case_,       snake_case_ :List[Any]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-openpose"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :int						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :str						=			controlnet_params

											snake_case_ :Optional[int]						=			"""Chef in the kitchen"""
											snake_case_ :Union[str, Any]						=			jax.device_count()
											snake_case_ :Any						=			pipe.prepare_text_inputs([prompts] * num_samples						)

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

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

											snake_case_ :Tuple						=			replicate(snake_case						)
											snake_case_ :str						=			shard(snake_case						)
											snake_case_ :int						=			shard(snake_case						)

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

											snake_case_ :Dict						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Optional[int]						=			jnp.array(
											    [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]						)
											print(f"""output_slice: {output_slice}"""						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1E-2
