





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    ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->List[Any]:



   '''simple docstring'''



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

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =1
   lowerCamelCase__:      Optional[Any]			  =3
   lowerCamelCase__:      str			  =(32, 32)

   lowerCamelCase__:      str			  =floats_tensor((batch_size, num_channels) + sizes       , rng=random.Random(0)).to(UpperCAmelCase_)
   return image

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:      List[Any]			  =UNetaDConditionModel(
       block_out_channels=(32, 32, 64)       , layers_per_block=2       , sample_size=32       , in_channels=7       , out_channels=4       , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")       , up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")       , cross_attention_dim=32       , attention_head_dim=8       , use_linear_projection=UpperCAmelCase_       , only_cross_attention=(True, True, False)       , num_class_embeds=100       , )
   return model

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->int:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:      Optional[int]			  =AutoencoderKL(
       block_out_channels=[32, 32, 64]       , 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 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->str:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:      Dict			  =CLIPTextConfig(
       bos_token_id=0       , eos_token_id=2       , hidden_size=32       , intermediate_size=37       , layer_norm_eps=1E-0_5       , num_attention_heads=4       , num_hidden_layers=5       , pad_token_id=1       , vocab_size=1_000       , hidden_act="gelu"       , projection_dim=512       , )
   return CLIPTextModel(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->int:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  ="cpu"  # ensure determinism for the device-dependent torch.Generator
   lowerCamelCase__:      str			  =self.dummy_cond_unet_upscale
   lowerCamelCase__:      int			  =DDPMScheduler()
   lowerCamelCase__:      str			  =DDIMScheduler(prediction_type="v_prediction")
   lowerCamelCase__:      Any			  =self.dummy_vae
   lowerCamelCase__:      List[Any]			  =self.dummy_text_encoder
   lowerCamelCase__:      Any			  =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   lowerCamelCase__:      Dict			  =self.dummy_image.cpu().permute(0       , 2       , 3       , 1)[0]
   lowerCamelCase__:      List[str]			  =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB").resize((64, 64))

   # make sure here that pndm scheduler skips prk
   lowerCamelCase__:      int			  =StableDiffusionUpscalePipeline(
       unet=UpperCAmelCase_       , low_res_scheduler=UpperCAmelCase_       , scheduler=UpperCAmelCase_       , vae=UpperCAmelCase_       , text_encoder=UpperCAmelCase_       , tokenizer=UpperCAmelCase_       , max_noise_level=350       , )
   lowerCamelCase__:      List[Any]			  =sd_pipe.to(UpperCAmelCase_)
   sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  ="A painting of a squirrel eating a burger"
   lowerCamelCase__:      Tuple			  =torch.Generator(device=UpperCAmelCase_).manual_seed(0)
   lowerCamelCase__:      Tuple			  =sd_pipe(
       [prompt]       , image=UpperCAmelCase_       , generator=UpperCAmelCase_       , guidance_scale=6.0       , noise_level=20       , num_inference_steps=2       , output_type="np"       , )

   lowerCamelCase__:      int			  =output.images

   lowerCamelCase__:      List[str]			  =torch.Generator(device=UpperCAmelCase_).manual_seed(0)
   lowerCamelCase__:      List[str]			  =sd_pipe(
       [prompt]       , image=UpperCAmelCase_       , generator=UpperCAmelCase_       , guidance_scale=6.0       , noise_level=20       , num_inference_steps=2       , output_type="np"       , return_dict=UpperCAmelCase_       , )[0]

   lowerCamelCase__:      Optional[int]			  =image[0, -3:, -3:, -1]
   lowerCamelCase__:      int			  =image_from_tuple[0, -3:, -3:, -1]

   lowerCamelCase__:      List[Any]			  =low_res_image.size[0] * 4
   assert image.shape == (1, expected_height_width, expected_height_width, 3)
   lowerCamelCase__:      Dict			  =np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

   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 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  ="cpu"  # ensure determinism for the device-dependent torch.Generator
   lowerCamelCase__:      Any			  =self.dummy_cond_unet_upscale
   lowerCamelCase__:      Optional[Any]			  =DDPMScheduler()
   lowerCamelCase__:      Optional[int]			  =DDIMScheduler(prediction_type="v_prediction")
   lowerCamelCase__:      List[str]			  =self.dummy_vae
   lowerCamelCase__:      List[Any]			  =self.dummy_text_encoder
   lowerCamelCase__:      str			  =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   lowerCamelCase__:      int			  =self.dummy_image.cpu().permute(0       , 2       , 3       , 1)[0]
   lowerCamelCase__:      Any			  =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB").resize((64, 64))

   # make sure here that pndm scheduler skips prk
   lowerCamelCase__:      Dict			  =StableDiffusionUpscalePipeline(
       unet=UpperCAmelCase_       , low_res_scheduler=UpperCAmelCase_       , scheduler=UpperCAmelCase_       , vae=UpperCAmelCase_       , text_encoder=UpperCAmelCase_       , tokenizer=UpperCAmelCase_       , max_noise_level=350       , )
   lowerCamelCase__:      List[Any]			  =sd_pipe.to(UpperCAmelCase_)
   sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      List[str]			  ="A painting of a squirrel eating a burger"
   lowerCamelCase__:      Optional[Any]			  =sd_pipe(
       2 * [prompt]       , image=2 * [low_res_image]       , guidance_scale=6.0       , noise_level=20       , num_inference_steps=2       , output_type="np"       , )
   lowerCamelCase__:      List[str]			  =output.images
   assert image.shape[0] == 2

   lowerCamelCase__:      Optional[int]			  =torch.Generator(device=UpperCAmelCase_).manual_seed(0)
   lowerCamelCase__:      Optional[int]			  =sd_pipe(
       [prompt]       , image=UpperCAmelCase_       , generator=UpperCAmelCase_       , num_images_per_prompt=2       , guidance_scale=6.0       , noise_level=20       , num_inference_steps=2       , output_type="np"       , )
   lowerCamelCase__:      Union[str, Any]			  =output.images
   assert image.shape[0] == 2







 @unittest.skipIf(torch_device != "cuda"       , "This test requires a GPU")
 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.dummy_cond_unet_upscale
   lowerCamelCase__:      str			  =DDPMScheduler()
   lowerCamelCase__:      Dict			  =DDIMScheduler(prediction_type="v_prediction")
   lowerCamelCase__:      Optional[Any]			  =self.dummy_vae
   lowerCamelCase__:      Dict			  =self.dummy_text_encoder
   lowerCamelCase__:      str			  =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   lowerCamelCase__:      Union[str, Any]			  =self.dummy_image.cpu().permute(0       , 2       , 3       , 1)[0]
   lowerCamelCase__:      List[Any]			  =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB").resize((64, 64))

   # put models in fp16, except vae as it overflows in fp16
   lowerCamelCase__:      List[str]			  =unet.half()
   lowerCamelCase__:      List[Any]			  =text_encoder.half()

   # make sure here that pndm scheduler skips prk
   lowerCamelCase__:      List[Any]			  =StableDiffusionUpscalePipeline(
       unet=UpperCAmelCase_       , low_res_scheduler=UpperCAmelCase_       , scheduler=UpperCAmelCase_       , vae=UpperCAmelCase_       , text_encoder=UpperCAmelCase_       , tokenizer=UpperCAmelCase_       , max_noise_level=350       , )
   lowerCamelCase__:      Tuple			  =sd_pipe.to(UpperCAmelCase_)
   sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      List[str]			  ="A painting of a squirrel eating a burger"
   lowerCamelCase__:      Optional[Any]			  =torch.manual_seed(0)
   lowerCamelCase__:      Union[str, Any]			  =sd_pipe(
       [prompt]       , image=UpperCAmelCase_       , generator=UpperCAmelCase_       , num_inference_steps=2       , output_type="np"       , ).images

   lowerCamelCase__:      int			  =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    ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->int:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->str:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/sd2-upscale/low_res_cat.png")
   lowerCamelCase__:      Union[str, Any]			  =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
       "/upsampled_cat.npy")

   lowerCamelCase__:      List[Any]			  ="stabilityai/stable-diffusion-x4-upscaler"
   lowerCamelCase__:      Optional[Any]			  =StableDiffusionUpscalePipeline.from_pretrained(UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)
   pipe.enable_attention_slicing()

   lowerCamelCase__:      str			  ="a cat sitting on a park bench"

   lowerCamelCase__:      Any			  =torch.manual_seed(0)
   lowerCamelCase__:      Union[str, Any]			  =pipe(
       prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , generator=UpperCAmelCase_       , output_type="np"       , )
   lowerCamelCase__:      Optional[int]			  =output.images[0]

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



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

   lowerCamelCase__:      Optional[int]			  ="stabilityai/stable-diffusion-x4-upscaler"
   lowerCamelCase__:      Dict			  =StableDiffusionUpscalePipeline.from_pretrained(
       UpperCAmelCase_       , torch_dtype=torch.floataa       , )
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)
   pipe.enable_attention_slicing()

   lowerCamelCase__:      Optional[Any]			  ="a cat sitting on a park bench"

   lowerCamelCase__:      Optional[Any]			  =torch.manual_seed(0)
   lowerCamelCase__:      List[Any]			  =pipe(
       prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , generator=UpperCAmelCase_       , output_type="np"       , )
   lowerCamelCase__:      List[str]			  =output.images[0]

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







 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->List[Any]:



   '''simple docstring'''



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

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

   lowerCamelCase__:      List[str]			  ="stabilityai/stable-diffusion-x4-upscaler"
   lowerCamelCase__:      int			  =StableDiffusionUpscalePipeline.from_pretrained(
       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()

   lowerCamelCase__:      Optional[int]			  ="a cat sitting on a park bench"

   lowerCamelCase__:      Tuple			  =torch.manual_seed(0)
   lowerCamelCase__:      Any			  =pipe(
       prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , generator=UpperCAmelCase_       , num_inference_steps=5       , output_type="np"       , )

   lowerCamelCase__:      Dict			  =torch.cuda.max_memory_allocated()
   # make sure that less than 2.9 GB is allocated
   assert mem_bytes < 2.9 * 10**9








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A									=				{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

