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

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowerCamelCase							(	lowerCamelCase     ,		lowerCamelCase     ,		lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	StableDiffusionInpaintPipeline
  lowerCAmelCase__										=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  lowerCAmelCase__										=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowerCAmelCase__										=	frozenset(
      []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  lowerCAmelCase__										=	frozenset([]					)






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->List[str]:
        torch.manual_seed(0  )
        UpperCAmelCase_            =  UNetaDConditionModel(
            block_out_channels=(32, 64)  ,							layers_per_block=2  ,							sample_size=32  ,							in_channels=9  ,							out_channels=4  ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,							cross_attention_dim=32  ,							attention_head_dim=(2, 4)  ,							use_linear_projection=UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  PNDMScheduler(skip_prk_steps=UpperCAmelCase__  )
        torch.manual_seed(0  )
        UpperCAmelCase_            =  AutoencoderKL(
            block_out_channels=[32, 64]  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,							latent_channels=4  ,							sample_size=128  ,							)
        torch.manual_seed(0  )
        UpperCAmelCase_            =  CLIPTextConfig(
            bos_token_id=0  ,							eos_token_id=2  ,							hidden_size=32  ,							intermediate_size=37  ,							layer_norm_eps=1e-05  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=1000  ,							hidden_act='''gelu'''  ,							projection_dim=512  ,							)
        UpperCAmelCase_            =  CLIPTextModel(UpperCAmelCase__  )
        UpperCAmelCase_            =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

        UpperCAmelCase_            =  {
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       str=0  )				->List[str]:
        # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
        UpperCAmelCase_            =  floats_tensor((1, 3, 32, 32)  ,							rng=random.Random(UpperCAmelCase__  )  ).to(UpperCAmelCase__  )
        UpperCAmelCase_            =  image.cpu().permute(0  ,							2  ,							3  ,							1  )[0]
        UpperCAmelCase_            =  Image.fromarray(np.uinta(UpperCAmelCase__  )  ).convert('''RGB'''  ).resize((64, 64)  )
        UpperCAmelCase_            =  Image.fromarray(np.uinta(image + 4  )  ).convert('''RGB'''  ).resize((64, 64)  )
        if str(UpperCAmelCase__  ).startswith('''mps'''  ):
              UpperCAmelCase_            =  torch.manual_seed(UpperCAmelCase__  )
        else:
              UpperCAmelCase_            =  torch.Generator(device=UpperCAmelCase__  ).manual_seed(UpperCAmelCase__  )
        UpperCAmelCase_            =  {
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Dict:
        UpperCAmelCase_            =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        UpperCAmelCase_            =  self.get_dummy_components()
        UpperCAmelCase_            =  StableDiffusionInpaintPipeline(**UpperCAmelCase__  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        UpperCAmelCase_            =  self.get_dummy_inputs(UpperCAmelCase__  )
        UpperCAmelCase_            =  sd_pipe(**UpperCAmelCase__  ).images
        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        UpperCAmelCase_            =  np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->List[str]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







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




  '''simple docstring'''



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






  def  lowerCAmelCase__ (      self			:       Any  )				->Any:
        UpperCAmelCase_            =  load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png'''  )
        UpperCAmelCase_            =  load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''  )
        UpperCAmelCase_            =  load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
            '''/yellow_cat_sitting_on_a_park_bench.npy'''  )

        UpperCAmelCase_            =  '''stabilityai/stable-diffusion-2-inpainting'''
        UpperCAmelCase_            =  StableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase__  ,							safety_checker=UpperCAmelCase__  )
        pipe.to(UpperCAmelCase__  )
        pipe.set_progress_bar_config(disable=UpperCAmelCase__  )
        pipe.enable_attention_slicing()

        UpperCAmelCase_            =  '''Face of a yellow cat, high resolution, sitting on a park bench'''

        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  pipe(
            prompt=UpperCAmelCase__  ,							image=UpperCAmelCase__  ,							mask_image=UpperCAmelCase__  ,							generator=UpperCAmelCase__  ,							output_type='''np'''  ,							)
        UpperCAmelCase_            =  output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image  ).max() < 9e-3






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->int:
        UpperCAmelCase_            =  load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png'''  )
        UpperCAmelCase_            =  load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''  )
        UpperCAmelCase_            =  load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
            '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''  )

        UpperCAmelCase_            =  '''stabilityai/stable-diffusion-2-inpainting'''
        UpperCAmelCase_            =  StableDiffusionInpaintPipeline.from_pretrained(
            UpperCAmelCase__  ,							torch_dtype=torch.floataa  ,							safety_checker=UpperCAmelCase__  ,							)
        pipe.to(UpperCAmelCase__  )
        pipe.set_progress_bar_config(disable=UpperCAmelCase__  )
        pipe.enable_attention_slicing()

        UpperCAmelCase_            =  '''Face of a yellow cat, high resolution, sitting on a park bench'''

        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  pipe(
            prompt=UpperCAmelCase__  ,							image=UpperCAmelCase__  ,							mask_image=UpperCAmelCase__  ,							generator=UpperCAmelCase__  ,							output_type='''np'''  ,							)
        UpperCAmelCase_            =  output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image  ).max() < 5e-1






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Optional[Any]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        UpperCAmelCase_            =  load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png'''  )
        UpperCAmelCase_            =  load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''  )

        UpperCAmelCase_            =  '''stabilityai/stable-diffusion-2-inpainting'''
        UpperCAmelCase_            =  PNDMScheduler.from_pretrained(UpperCAmelCase__  ,							subfolder='''scheduler'''  )
        UpperCAmelCase_            =  StableDiffusionInpaintPipeline.from_pretrained(
            UpperCAmelCase__  ,							safety_checker=UpperCAmelCase__  ,							scheduler=UpperCAmelCase__  ,							torch_dtype=torch.floataa  ,							)
        pipe.to(UpperCAmelCase__  )
        pipe.set_progress_bar_config(disable=UpperCAmelCase__  )
        pipe.enable_attention_slicing(1  )
        pipe.enable_sequential_cpu_offload()

        UpperCAmelCase_            =  '''Face of a yellow cat, high resolution, sitting on a park bench'''

        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  pipe(
            prompt=UpperCAmelCase__  ,							image=UpperCAmelCase__  ,							mask_image=UpperCAmelCase__  ,							generator=UpperCAmelCase__  ,							num_inference_steps=2  ,							output_type='''np'''  ,							)

        UpperCAmelCase_            =  torch.cuda.max_memory_allocated()
        # make sure that less than 2.65 GB is allocated
        assert mem_bytes < 2.65 * 10**9






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__  :      Optional[Any]        =					{
    "configuration_encodec": [
        "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EncodecConfig",
    ],
    "feature_extraction_encodec": ["EncodecFeatureExtractor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      List[str]        =					[
          "ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
          "EncodecModel",
          "EncodecPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_encodec import (
          ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EncodecConfig,
      )
      from .feature_extraction_encodec import EncodecFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_encodec import (
                ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
                EncodecModel,
                EncodecPreTrainedModel,
            )

else:
      import sys

      lowercase__  :      str        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





