



"""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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







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

    @property
    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= 1
      lowercase_								= 3
      lowercase_								= (3_2, 3_2)

      lowercase_								= floats_tensor((batch_size, num_channels) + sizes ,  rng=random.Random(0)).to(lowerCAmelCase_)
      return image

    @property
    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      torch.manual_seed(0)
      lowercase_								= UNetaDConditionModel(
          block_out_channels=(3_2, 3_2, 6_4) ,  layers_per_block=2 ,  sample_size=3_2 ,  in_channels=7 ,  out_channels=4 ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""") ,  up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""") ,  cross_attention_dim=3_2 ,  attention_head_dim=8 ,  use_linear_projection=lowerCAmelCase_ ,  only_cross_attention=(True, True, False) ,  num_class_embeds=1_0_0 ,  )
      return model

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      torch.manual_seed(0)
      lowercase_								= AutoencoderKL(
          block_out_channels=[3_2, 3_2, 6_4] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,  latent_channels=4 ,  )
      return model

    @property
    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      torch.manual_seed(0)
      lowercase_								= CLIPTextConfig(
          bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=3_2 ,  intermediate_size=3_7 ,  layer_norm_eps=1E-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=1_0_0_0 ,  hidden_act="""gelu""" ,  projection_dim=5_1_2 ,  )
      return CLIPTextModel(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowercase_								= self.dummy_cond_unet_upscale
      lowercase_								= DDPMScheduler()
      lowercase_								= DDIMScheduler(prediction_type="""v_prediction""")
      lowercase_								= self.dummy_vae
      lowercase_								= self.dummy_text_encoder
      lowercase_								= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

      lowercase_								= self.dummy_image.cpu().permute(0 ,  2 ,  3 ,  1)[0]
      lowercase_								= Image.fromarray(np.uinta(lowerCAmelCase_)).convert("""RGB""").resize((6_4, 6_4))

      # make sure here that pndm scheduler skips prk
      lowercase_								= StableDiffusionUpscalePipeline(
          unet=lowerCAmelCase_ ,  low_res_scheduler=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_ ,  vae=lowerCAmelCase_ ,  text_encoder=lowerCAmelCase_ ,  tokenizer=lowerCAmelCase_ ,  max_noise_level=3_5_0 ,  )
      lowercase_								= sd_pipe.to(lowerCAmelCase_)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      lowercase_								= """A painting of a squirrel eating a burger"""
      lowercase_								= torch.Generator(device=lowerCAmelCase_).manual_seed(0)
      lowercase_								= sd_pipe(
          [prompt] ,  image=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  guidance_scale=6.0 ,  noise_level=2_0 ,  num_inference_steps=2 ,  output_type="""np""" ,  )

      lowercase_								= output.images

      lowercase_								= torch.Generator(device=lowerCAmelCase_).manual_seed(0)
      lowercase_								= sd_pipe(
          [prompt] ,  image=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  guidance_scale=6.0 ,  noise_level=2_0 ,  num_inference_steps=2 ,  output_type="""np""" ,  return_dict=lowerCAmelCase_ ,  )[0]

      lowercase_								= image[0, -3:, -3:, -1]
      lowercase_								= image_from_tuple[0, -3:, -3:, -1]

      lowercase_								= low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)
      lowercase_								= np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661])

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

    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      lowercase_								= """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowercase_								= self.dummy_cond_unet_upscale
      lowercase_								= DDPMScheduler()
      lowercase_								= DDIMScheduler(prediction_type="""v_prediction""")
      lowercase_								= self.dummy_vae
      lowercase_								= self.dummy_text_encoder
      lowercase_								= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

      lowercase_								= self.dummy_image.cpu().permute(0 ,  2 ,  3 ,  1)[0]
      lowercase_								= Image.fromarray(np.uinta(lowerCAmelCase_)).convert("""RGB""").resize((6_4, 6_4))

      # make sure here that pndm scheduler skips prk
      lowercase_								= StableDiffusionUpscalePipeline(
          unet=lowerCAmelCase_ ,  low_res_scheduler=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_ ,  vae=lowerCAmelCase_ ,  text_encoder=lowerCAmelCase_ ,  tokenizer=lowerCAmelCase_ ,  max_noise_level=3_5_0 ,  )
      lowercase_								= sd_pipe.to(lowerCAmelCase_)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      lowercase_								= """A painting of a squirrel eating a burger"""
      lowercase_								= sd_pipe(
          2 * [prompt] ,  image=2 * [low_res_image] ,  guidance_scale=6.0 ,  noise_level=2_0 ,  num_inference_steps=2 ,  output_type="""np""" ,  )
      lowercase_								= output.images
      assert image.shape[0] == 2

      lowercase_								= torch.Generator(device=lowerCAmelCase_).manual_seed(0)
      lowercase_								= sd_pipe(
          [prompt] ,  image=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  num_images_per_prompt=2 ,  guidance_scale=6.0 ,  noise_level=2_0 ,  num_inference_steps=2 ,  output_type="""np""" ,  )
      lowercase_								= output.images
      assert image.shape[0] == 2




    @unittest.skipIf(torch_device != """cuda""" ,  """This test requires a GPU""")
    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      lowercase_								= self.dummy_cond_unet_upscale
      lowercase_								= DDPMScheduler()
      lowercase_								= DDIMScheduler(prediction_type="""v_prediction""")
      lowercase_								= self.dummy_vae
      lowercase_								= self.dummy_text_encoder
      lowercase_								= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

      lowercase_								= self.dummy_image.cpu().permute(0 ,  2 ,  3 ,  1)[0]
      lowercase_								= Image.fromarray(np.uinta(lowerCAmelCase_)).convert("""RGB""").resize((6_4, 6_4))

      # put models in fp16, except vae as it overflows in fp16
      lowercase_								= unet.half()
      lowercase_								= text_encoder.half()

      # make sure here that pndm scheduler skips prk
      lowercase_								= StableDiffusionUpscalePipeline(
          unet=lowerCAmelCase_ ,  low_res_scheduler=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_ ,  vae=lowerCAmelCase_ ,  text_encoder=lowerCAmelCase_ ,  tokenizer=lowerCAmelCase_ ,  max_noise_level=3_5_0 ,  )
      lowercase_								= sd_pipe.to(lowerCAmelCase_)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      lowercase_								= """A painting of a squirrel eating a burger"""
      lowercase_								= torch.manual_seed(0)
      lowercase_								= sd_pipe(
          [prompt] ,  image=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  num_inference_steps=2 ,  output_type="""np""" ,  ).images

      lowercase_								= low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)







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

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-upscale/low_res_cat.png""")
      lowercase_								= load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
          """/upsampled_cat.npy""")

      lowercase_								= """stabilityai/stable-diffusion-x4-upscaler"""
      lowercase_								= StableDiffusionUpscalePipeline.from_pretrained(lowerCAmelCase_)
      pipe.to(lowerCAmelCase_)
      pipe.set_progress_bar_config(disable=lowerCAmelCase_)
      pipe.enable_attention_slicing()

      lowercase_								= """a cat sitting on a park bench"""

      lowercase_								= torch.manual_seed(0)
      lowercase_								= pipe(
          prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  output_type="""np""" ,  )
      lowercase_								= output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)
      assert np.abs(expected_image - image).max() < 1E-3

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-upscale/low_res_cat.png""")
      lowercase_								= load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
          """/upsampled_cat_fp16.npy""")

      lowercase_								= """stabilityai/stable-diffusion-x4-upscaler"""
      lowercase_								= StableDiffusionUpscalePipeline.from_pretrained(
          lowerCAmelCase_ ,  torch_dtype=torch.floataa ,  )
      pipe.to(lowerCAmelCase_)
      pipe.set_progress_bar_config(disable=lowerCAmelCase_)
      pipe.enable_attention_slicing()

      lowercase_								= """a cat sitting on a park bench"""

      lowercase_								= torch.manual_seed(0)
      lowercase_								= pipe(
          prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  output_type="""np""" ,  )
      lowercase_								= output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)
      assert np.abs(expected_image - image).max() < 5E-1




    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







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

      lowercase_								= load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-upscale/low_res_cat.png""")

      lowercase_								= """stabilityai/stable-diffusion-x4-upscaler"""
      lowercase_								= StableDiffusionUpscalePipeline.from_pretrained(
          lowerCAmelCase_ ,  torch_dtype=torch.floataa ,  )
      pipe.to(lowerCAmelCase_)
      pipe.set_progress_bar_config(disable=lowerCAmelCase_)
      pipe.enable_attention_slicing(1)
      pipe.enable_sequential_cpu_offload()

      lowercase_								= """a cat sitting on a park bench"""

      lowercase_								= torch.manual_seed(0)
      lowercase_								= pipe(
          prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  num_inference_steps=5 ,  output_type="""np""" ,  )

      lowercase_								= torch.cuda.max_memory_allocated()
      # make sure that less than 2.9 GB is allocated
      assert mem_bytes < 2.9 * 1_0**9






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase						:		Optional[Any]			= {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "WavLMForAudioFrameClassification",
           "WavLMForCTC",
           "WavLMForSequenceClassification",
           "WavLMForXVector",
           "WavLMModel",
           "WavLMPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavlm import (
                  WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavLMForAudioFrameClassification,
                  WavLMForCTC,
                  WavLMForSequenceClassification,
                  WavLMForXVector,
                  WavLMModel,
                  WavLMPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

