


'''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 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 @property
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							1
      _a      =							3
      _a      =							(32, 32)

      _a      =							floats_tensor((batch_size, num_channels) + sizes ,			rng=random.Random(0  )  ).to(__lowercase  )
      return image
 @property
 def 							__UpperCAmelCase						(	self  )   ->							int:
      torch.manual_seed(0  )
      _a      =							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=__lowercase ,			only_cross_attention=(True, True, False) ,			num_class_embeds=1_00 ,			)
      return model
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      torch.manual_seed(0  )
      _a      =							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 							__UpperCAmelCase						(	self  )   ->							Tuple:
      torch.manual_seed(0  )
      _a      =							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=10_00 ,			hidden_act='gelu' ,			projection_dim=5_12 ,			)
      return CLIPTextModel(__lowercase  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
      _a      =							self.dummy_cond_unet_upscale
      _a      =							DDPMScheduler()
      _a      =							DDIMScheduler(prediction_type='v_prediction'  )
      _a      =							self.dummy_vae
      _a      =							self.dummy_text_encoder
      _a      =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

      _a      =							self.dummy_image.cpu().permute(0 ,			2 ,			3 ,			1  )[0]
      _a      =							Image.fromarray(np.uinta(__lowercase  )  ).convert('RGB'  ).resize((64, 64)  )

      # make sure here that pndm scheduler skips prk
      _a      =							StableDiffusionUpscalePipeline(
          unet=__lowercase ,			low_res_scheduler=__lowercase ,			scheduler=__lowercase ,			vae=__lowercase ,			text_encoder=__lowercase ,			tokenizer=__lowercase ,			max_noise_level=3_50 ,			)
      _a      =							sd_pipe.to(__lowercase  )
      sd_pipe.set_progress_bar_config(disable=__lowercase  )

      _a      =							'''A painting of a squirrel eating a burger'''
      _a      =							torch.Generator(device=__lowercase  ).manual_seed(0  )
      _a      =							sd_pipe(
          [prompt] ,			image=__lowercase ,			generator=__lowercase ,			guidance_scale=6.0 ,			noise_level=20 ,			num_inference_steps=2 ,			output_type='np' ,			)

      _a      =							output.images

      _a      =							torch.Generator(device=__lowercase  ).manual_seed(0  )
      _a      =							sd_pipe(
          [prompt] ,			image=__lowercase ,			generator=__lowercase ,			guidance_scale=6.0 ,			noise_level=20 ,			num_inference_steps=2 ,			output_type='np' ,			return_dict=__lowercase ,			)[0]

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

      _a      =							low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)
      _a      =							np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]  )

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

      _a      =							self.dummy_image.cpu().permute(0 ,			2 ,			3 ,			1  )[0]
      _a      =							Image.fromarray(np.uinta(__lowercase  )  ).convert('RGB'  ).resize((64, 64)  )

      # make sure here that pndm scheduler skips prk
      _a      =							StableDiffusionUpscalePipeline(
          unet=__lowercase ,			low_res_scheduler=__lowercase ,			scheduler=__lowercase ,			vae=__lowercase ,			text_encoder=__lowercase ,			tokenizer=__lowercase ,			max_noise_level=3_50 ,			)
      _a      =							sd_pipe.to(__lowercase  )
      sd_pipe.set_progress_bar_config(disable=__lowercase  )

      _a      =							'''A painting of a squirrel eating a burger'''
      _a      =							sd_pipe(
          2 * [prompt] ,			image=2 * [low_res_image] ,			guidance_scale=6.0 ,			noise_level=20 ,			num_inference_steps=2 ,			output_type='np' ,			)
      _a      =							output.images
      assert image.shape[0] == 2

      _a      =							torch.Generator(device=__lowercase  ).manual_seed(0  )
      _a      =							sd_pipe(
          [prompt] ,			image=__lowercase ,			generator=__lowercase ,			num_images_per_prompt=2 ,			guidance_scale=6.0 ,			noise_level=20 ,			num_inference_steps=2 ,			output_type='np' ,			)
      _a      =							output.images
      assert image.shape[0] == 2




 @unittest.skipIf(torch_device != 'cuda' ,			'This test requires a GPU'  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							self.dummy_cond_unet_upscale
      _a      =							DDPMScheduler()
      _a      =							DDIMScheduler(prediction_type='v_prediction'  )
      _a      =							self.dummy_vae
      _a      =							self.dummy_text_encoder
      _a      =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

      _a      =							self.dummy_image.cpu().permute(0 ,			2 ,			3 ,			1  )[0]
      _a      =							Image.fromarray(np.uinta(__lowercase  )  ).convert('RGB'  ).resize((64, 64)  )

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

      # make sure here that pndm scheduler skips prk
      _a      =							StableDiffusionUpscalePipeline(
          unet=__lowercase ,			low_res_scheduler=__lowercase ,			scheduler=__lowercase ,			vae=__lowercase ,			text_encoder=__lowercase ,			tokenizer=__lowercase ,			max_noise_level=3_50 ,			)
      _a      =							sd_pipe.to(__lowercase  )
      sd_pipe.set_progress_bar_config(disable=__lowercase  )

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

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





@slow
@require_torch_gpu
class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/sd2-upscale/low_res_cat.png'  )
      _a      =							load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
          '/upsampled_cat.npy'  )

      _a      =							'''stabilityai/stable-diffusion-x4-upscaler'''
      _a      =							StableDiffusionUpscalePipeline.from_pretrained(__lowercase  )
      pipe.to(__lowercase  )
      pipe.set_progress_bar_config(disable=__lowercase  )
      pipe.enable_attention_slicing()

      _a      =							'''a cat sitting on a park bench'''

      _a      =							torch.manual_seed(0  )
      _a      =							pipe(
          prompt=__lowercase ,			image=__lowercase ,			generator=__lowercase ,			output_type='np' ,			)
      _a      =							output.images[0]

      assert image.shape == (5_12, 5_12, 3)
      assert np.abs(expected_image - image  ).max() < 1e-3
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/sd2-upscale/low_res_cat.png'  )
      _a      =							load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
          '/upsampled_cat_fp16.npy'  )

      _a      =							'''stabilityai/stable-diffusion-x4-upscaler'''
      _a      =							StableDiffusionUpscalePipeline.from_pretrained(
          __lowercase ,			torch_dtype=torch.floataa ,			)
      pipe.to(__lowercase  )
      pipe.set_progress_bar_config(disable=__lowercase  )
      pipe.enable_attention_slicing()

      _a      =							'''a cat sitting on a park bench'''

      _a      =							torch.manual_seed(0  )
      _a      =							pipe(
          prompt=__lowercase ,			image=__lowercase ,			generator=__lowercase ,			output_type='np' ,			)
      _a      =							output.images[0]

      assert image.shape == (5_12, 5_12, 3)
      assert np.abs(expected_image - image  ).max() < 5e-1




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

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

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

      _a      =							'''a cat sitting on a park bench'''

      _a      =							torch.manual_seed(0  )
      _a      =							pipe(
          prompt=__lowercase ,			image=__lowercase ,			generator=__lowercase ,			num_inference_steps=5 ,			output_type='np' ,			)

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








'''simple docstring'''






from typing import TYPE_CHECKING

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


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

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

