
'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


_snake_case         =  logging.get_logger(__name__)


class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      warnings.warn(
          "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use BeitImageProcessor instead."      ,    _UpperCamelCase      ,    )
      super().__init__(*_UpperCamelCase      ,    **_UpperCamelCase  )









'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class        a__  (							lowerCamelCase_  ,				lowerCamelCase_  ,				lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					Dict     	=							StableUnCLIPImgaImgPipeline
   _SCREAMING_SNAKE_CASE   :					int     	=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _SCREAMING_SNAKE_CASE   :					Any     	=							frozenset(
       []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							frozenset([]  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       32
      _lowercase      :				Any      					=       embedder_hidden_size

      # image encoding components

      _lowercase      :				Optional[int]      					=       CLIPImageProcessor(crop_size=32      ,    size=32  )

      torch.manual_seed(0  )
      _lowercase      :				Dict      					=       CLIPVisionModelWithProjection(
          CLIPVisionConfig(
              hidden_size=_UpperCamelCase      ,    projection_dim=_UpperCamelCase      ,    num_hidden_layers=5      ,    num_attention_heads=4      ,    image_size=32      ,    intermediate_size=37      ,    patch_size=1      ,    )  )

      # regular denoising components

      torch.manual_seed(0  )
      _lowercase      :				str      					=       StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase  )
      _lowercase      :				Dict      					=       DDPMScheduler(beta_schedule="squaredcos_cap_v2"  )

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

      torch.manual_seed(0  )
      _lowercase      :				Optional[Any]      					=       CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=_UpperCamelCase      ,    projection_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1E-0_5      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    pad_token_id=1      ,    vocab_size=1000      ,    )  )

      torch.manual_seed(0  )
      _lowercase      :				List[str]      					=       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=_UpperCamelCase      ,    layers_per_block=1      ,    upcast_attention=_UpperCamelCase      ,    use_linear_projection=_UpperCamelCase      ,    )

      torch.manual_seed(0  )
      _lowercase      :				int      					=       DDIMScheduler(
          beta_schedule="scaled_linear"      ,    beta_start=0.0_0_0_8_5      ,    beta_end=0.0_1_2      ,    prediction_type="v_prediction"      ,    set_alpha_to_one=_UpperCamelCase      ,    steps_offset=1      ,    )

      torch.manual_seed(0  )
      _lowercase      :				Dict      					=       AutoencoderKL()

      _lowercase      :				int      					=       {
          # image encoding components
          "feature_extractor": feature_extractor,
          "image_encoder": image_encoder.eval(),
          # image noising components
          "image_normalizer": image_normalizer.eval(),
          "image_noising_scheduler": image_noising_scheduler,
          # regular denoising components
          "tokenizer": tokenizer,
          "text_encoder": text_encoder.eval(),
          "unet": unet.eval(),
          "scheduler": scheduler,
          "vae": vae.eval(),
      }

      return components


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=0      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      if str(_UpperCamelCase  ).startswith("mps"  ):
         _lowercase      :				List[Any]      					=       torch.manual_seed(_UpperCamelCase  )
      else:
         _lowercase      :				List[str]      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )

      _lowercase      :				List[str]      					=       floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )

      if pil_image:
         _lowercase      :				Optional[Any]      					=       input_image * 0.5 + 0.5
         _lowercase      :				Optional[int]      					=       input_image.clamp(0      ,    1  )
         _lowercase      :				str      					=       input_image.cpu().permute(0      ,    2      ,    3      ,    1  ).float().numpy()
         _lowercase      :				int      					=       DiffusionPipeline.numpy_to_pil(_UpperCamelCase  )[0]

      return {
          "prompt": "An anime racoon running a marathon",
          "image": input_image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "np",
      }


   @skip_mps
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       "cpu"  # ensure determinism for the device-dependent torch.Generator
      _lowercase      :				List[str]      					=       self.get_dummy_components()
      _lowercase      :				str      					=       StableUnCLIPImgaImgPipeline(**_UpperCamelCase  )
      _lowercase      :				int      					=       sd_pipe.to(_UpperCamelCase  )
      sd_pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				str      					=       self.get_dummy_inputs(_UpperCamelCase  )
      inputs.update({"image_embeds": None}  )
      _lowercase      :				Tuple      					=       sd_pipe(**_UpperCamelCase  ).images
      _lowercase      :				str      					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _lowercase      :				Tuple      					=       np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       torch_device in ["cpu", "mps"]

      self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       torch_device in ["cpu", "mps"]

      self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase  )






   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()      ,    reason="XFormers attention is only available with CUDA and `xformers` installed"      ,    )
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_UpperCamelCase  )




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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Any      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"  )

      _lowercase      :				Any      					=       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"  )

      _lowercase      :				List[str]      					=       StableUnCLIPImgaImgPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-l-img2img"      ,    torch_dtype=torch.floataa  )
      pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      # 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()

      _lowercase      :				int      					=       torch.Generator(device="cpu"  ).manual_seed(0  )
      _lowercase      :				List[Any]      					=       pipe(_UpperCamelCase      ,    "anime turle"      ,    generator=_UpperCamelCase      ,    output_type="np"  )

      _lowercase      :				str      					=       output.images[0]

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

      assert_mean_pixel_difference(_UpperCamelCase      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"  )

      _lowercase      :				Any      					=       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"  )

      _lowercase      :				Any      					=       StableUnCLIPImgaImgPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-h-img2img"      ,    torch_dtype=torch.floataa  )
      pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      # 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()

      _lowercase      :				Optional[int]      					=       torch.Generator(device="cpu"  ).manual_seed(0  )
      _lowercase      :				Any      					=       pipe(_UpperCamelCase      ,    "anime turle"      ,    generator=_UpperCamelCase      ,    output_type="np"  )

      _lowercase      :				Tuple      					=       output.images[0]

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

      assert_mean_pixel_difference(_UpperCamelCase      ,    _UpperCamelCase  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"  )

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

      _lowercase      :				Optional[Any]      					=       StableUnCLIPImgaImgPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-h-img2img"      ,    torch_dtype=torch.floataa  )
      _lowercase      :				Tuple      					=       pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _lowercase      :				Optional[int]      					=       pipe(
          _UpperCamelCase      ,    "anime turtle"      ,    num_inference_steps=2      ,    output_type="np"      ,    )

      _lowercase      :				str      					=       torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9






