





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,
)








import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()







class   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase		,_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						Optional[int]     		=				StableUnCLIPPipeline
   _UpperCamelCase :						Optional[int]     		=				TEXT_TO_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCamelCase :						List[Any]     		=				TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_IMAGE_PARAMS

   # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
   _UpperCamelCase :						Any     		=				False





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Any      		=  32
      a__	:						int      		=  embedder_hidden_size

      # prior components

      torch.manual_seed(0      )
      a__	:						Any      		=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

      torch.manual_seed(0      )
      a__	:						Optional[Any]      		=  CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			projection_dim=snake_case					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						int      		=  PriorTransformer(
          num_attention_heads=2					,			attention_head_dim=12					,			embedding_dim=snake_case					,			num_layers=1					,			)

      torch.manual_seed(0      )
      a__	:						str      		=  DDPMScheduler(
          variance_type="fixed_small_log"					,			prediction_type="sample"					,			num_train_timesteps=1_000					,			clip_sample=snake_case					,			clip_sample_range=5.0					,			beta_schedule="squaredcos_cap_v2"					,			)

      # regular denoising components

      torch.manual_seed(0      )
      a__	:						Any      		=  StableUnCLIPImageNormalizer(embedding_dim=snake_case      )
      a__	:						int      		=  DDPMScheduler(beta_schedule="squaredcos_cap_v2"      )

      torch.manual_seed(0      )
      a__	:						Optional[Any]      		=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

      torch.manual_seed(0      )
      a__	:						Union[str, Any]      		=  CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			projection_dim=32					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						Any      		=  UNetaDConditionModel(
          sample_size=32					,			in_channels=4					,			out_channels=4					,			down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")					,			up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")					,			block_out_channels=(32, 64)					,			attention_head_dim=(2, 4)					,			class_embed_type="projection"					,			projection_class_embeddings_input_dim=embedder_projection_dim * 2					,			cross_attention_dim=snake_case					,			layers_per_block=1					,			upcast_attention=snake_case					,			use_linear_projection=snake_case					,			)

      torch.manual_seed(0      )
      a__	:						Tuple      		=  DDIMScheduler(
          beta_schedule="scaled_linear"					,			beta_start=0.00_085					,			beta_end=0.012					,			prediction_type="v_prediction"					,			set_alpha_to_one=snake_case					,			steps_offset=1					,			)

      torch.manual_seed(0      )
      a__	:						Optional[int]      		=  AutoencoderKL()

      a__	:						Any      		=  {
          # prior components
          "prior_tokenizer": prior_tokenizer,
          "prior_text_encoder": prior_text_encoder,
          "prior": prior,
          "prior_scheduler": prior_scheduler,
          # image noising components
          "image_normalizer": image_normalizer,
          "image_noising_scheduler": image_noising_scheduler,
          # regular denoising components
          "tokenizer": tokenizer,
          "text_encoder": text_encoder,
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
      }

      return components





   def 	_snake_case ( self					,			snake_case					,			snake_case=0      )				->  Dict:
      """simple docstring"""





      if str(snake_case      ).startswith("mps"      ):
         a__	:						Union[str, Any]      		=  torch.manual_seed(snake_case      )
      else:
         a__	:						List[str]      		=  torch.Generator(device=snake_case      ).manual_seed(snake_case      )
      a__	:						Any      		=  {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "prior_num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Dict      		=  torch_device == "cpu"

      self._test_attention_slicing_forward_pass(test_max_difference=snake_case      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						int      		=  torch_device in ["cpu", "mps"]

      self._test_inference_batch_single_identical(test_max_difference=snake_case      )







@slow
@require_torch_gpu
class   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						int      		=  load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"      )

      a__	:						int      		=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,			torch_dtype=torch.floataa      )
      pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      a__	:						Dict      		=  torch.Generator(device="cpu"      ).manual_seed(0      )
      a__	:						Dict      		=  pipe("anime turle"					,			generator=snake_case					,			output_type="np"      )

      a__	:						List[str]      		=  output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      a__	:						str      		=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,			torch_dtype=torch.floataa      )
      a__	:						Union[str, Any]      		=  pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      a__	:						Union[str, Any]      		=  pipe(
          "anime turtle"					,			prior_num_inference_steps=2					,			num_inference_steps=2					,			output_type="np"					,			)

      a__	:						List[Any]      		=  torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9

