



"""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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase						:		List[Any]			= logging.get_logger(__name__)

UpperCAmelCase						:		Optional[Any]			= {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   "mgp-str"

    def __init__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[Any]=[3_2, 1_2_8] ,  lowerCAmelCase_			:				Union[str, Any]=4 ,  lowerCAmelCase_			:				Dict=3 ,  lowerCAmelCase_			:				List[str]=2_7 ,  lowerCAmelCase_			:				str=3_8 ,  lowerCAmelCase_			:				Optional[int]=5_0_2_5_7 ,  lowerCAmelCase_			:				Any=3_0_5_2_2 ,  lowerCAmelCase_			:				str=7_6_8 ,  lowerCAmelCase_			:				Any=1_2 ,  lowerCAmelCase_			:				Optional[Any]=1_2 ,  lowerCAmelCase_			:				Tuple=4.0 ,  lowerCAmelCase_			:				str=True ,  lowerCAmelCase_			:				List[str]=False ,  lowerCAmelCase_			:				List[Any]=1E-5 ,  lowerCAmelCase_			:				Any=0.0 ,  lowerCAmelCase_			:				int=0.0 ,  lowerCAmelCase_			:				str=0.0 ,  lowerCAmelCase_			:				Dict=False ,  lowerCAmelCase_			:				List[str]=0.02 ,  **lowerCAmelCase_			:				str ,  ):







      """simple docstring"""







      super().__init__(**lowerCAmelCase_)

      lowercase_								= image_size
      lowercase_								= patch_size
      lowercase_								= num_channels
      lowercase_								= max_token_length
      lowercase_								= num_character_labels
      lowercase_								= num_bpe_labels
      lowercase_								= num_wordpiece_labels
      lowercase_								= hidden_size
      lowercase_								= num_hidden_layers
      lowercase_								= num_attention_heads
      lowercase_								= mlp_ratio
      lowercase_								= distilled
      lowercase_								= layer_norm_eps
      lowercase_								= drop_rate
      lowercase_								= qkv_bias
      lowercase_								= attn_drop_rate
      lowercase_								= drop_path_rate
      lowercase_								= output_aa_attentions
      lowercase_								= initializer_range

