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    ):
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      FlaxControlNetModel.from_pretrained(
         '''lllyasviel/sd-controlnet-canny''' , from_pt=lowerCAmelCase , dtype=jnp.bfloataa   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   List[Any]=      FlaxStableDiffusionControlNetPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5''' , controlnet=lowerCAmelCase , from_pt=lowerCAmelCase , dtype=jnp.bfloataa   )
     SCREAMING_SNAKE_CASE__:   int=      controlnet_params

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

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

     SCREAMING_SNAKE_CASE__:   int=      jax.random.PRNGKey(0   )
     SCREAMING_SNAKE_CASE__:   Dict=      jax.random.split(lowerCAmelCase , jax.device_count()   )

     SCREAMING_SNAKE_CASE__:   Tuple=      replicate(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      shard(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      shard(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      pipe(
         prompt_ids=lowerCAmelCase , image=lowerCAmelCase , params=lowerCAmelCase , prng_seed=lowerCAmelCase , num_inference_steps=50 , jit=lowerCAmelCase , ).images
     assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

     SCREAMING_SNAKE_CASE__:   List[Any]=      jnp.asarray(jax.device_get(image_slice.flatten()   )   )
     SCREAMING_SNAKE_CASE__:   int=      jnp.array(
         [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]   )
     print(f'output_slice: {output_slice}'   )
     assert jnp.abs(output_slice - expected_slice   ).max() < 1e-2



    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Dict=      FlaxControlNetModel.from_pretrained(
         '''lllyasviel/sd-controlnet-openpose''' , from_pt=lowerCAmelCase , dtype=jnp.bfloataa   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[Any]=      FlaxStableDiffusionControlNetPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5''' , controlnet=lowerCAmelCase , from_pt=lowerCAmelCase , dtype=jnp.bfloataa   )
     SCREAMING_SNAKE_CASE__:   Any=      controlnet_params

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      '''Chef in the kitchen'''
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      jax.device_count()
     SCREAMING_SNAKE_CASE__:   str=      pipe.prepare_text_inputs([prompts] * num_samples   )

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

     SCREAMING_SNAKE_CASE__:   List[Any]=      jax.random.PRNGKey(0   )
     SCREAMING_SNAKE_CASE__:   Any=      jax.random.split(lowerCAmelCase , jax.device_count()   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      replicate(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      shard(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      shard(lowerCAmelCase   )

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

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

     SCREAMING_SNAKE_CASE__:   Tuple=      jnp.asarray(jax.device_get(image_slice.flatten()   )   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      jnp.array(
         [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]   )
     print(f'output_slice: {output_slice}'   )
     assert jnp.abs(output_slice - expected_slice   ).max() < 1e-2


from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

