



"""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 SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Optional[int]									=       FlaxControlNetModel.from_pretrained(
													    """lllyasviel/sd-controlnet-canny"""						,     from_pt=_lowercase						,     dtype=jnp.bfloataa							)
													__a							,     __a		:		Any									=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    """runwayml/stable-diffusion-v1-5"""						,     controlnet=_lowercase						,     from_pt=_lowercase						,     dtype=jnp.bfloataa							)
													__a		:		List[str]									=       controlnet_params

													__a		:		Any									=       """bird"""
													__a		:		Tuple									=       jax.device_count()
													__a		:		int									=       pipe.prepare_text_inputs([prompts] * num_samples							)

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

													__a		:		Union[str, Any]									=       jax.random.PRNGKey(0							)
													__a		:		Optional[int]									=       jax.random.split(_lowercase						,     jax.device_count()							)

													__a		:		str									=       replicate(_lowercase							)
													__a		:		Optional[int]									=       shard(_lowercase							)
													__a		:		Optional[int]									=       shard(_lowercase							)

													__a		:		Tuple									=       pipe(
													    prompt_ids=_lowercase						,     image=_lowercase						,     params=_lowercase						,     prng_seed=_lowercase						,     num_inference_steps=50						,     jit=_lowercase						,     ).images
													assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

													__a		:		Optional[int]									=       jnp.asarray(jax.device_get(image_slice.flatten()							)							)
													__a		:		Tuple									=       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     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Any									=       FlaxControlNetModel.from_pretrained(
													    """lllyasviel/sd-controlnet-openpose"""						,     from_pt=_lowercase						,     dtype=jnp.bfloataa							)
													__a							,     __a		:		str									=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    """runwayml/stable-diffusion-v1-5"""						,     controlnet=_lowercase						,     from_pt=_lowercase						,     dtype=jnp.bfloataa							)
													__a		:		List[str]									=       controlnet_params

													__a		:		Union[str, Any]									=       """Chef in the kitchen"""
													__a		:		List[str]									=       jax.device_count()
													__a		:		Dict									=       pipe.prepare_text_inputs([prompts] * num_samples							)

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

													__a		:		Dict									=       jax.random.PRNGKey(0							)
													__a		:		int									=       jax.random.split(_lowercase						,     jax.device_count()							)

													__a		:		int									=       replicate(_lowercase							)
													__a		:		Optional[int]									=       shard(_lowercase							)
													__a		:		str									=       shard(_lowercase							)

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

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

													__a		:		str									=       jnp.asarray(jax.device_get(image_slice.flatten()							)							)
													__a		:		Tuple									=       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






"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

