


import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:  # picklable for multiprocessing

     """simple docstring"""







     return i + 1







@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 	_snake_case		(    )       ->      Optional[Any]:

     """simple docstring"""







     with parallel_backend("spark"				):
          assert ParallelBackendConfig.backend_name == "spark"

     _lowerCAmelCase   :					Optional[int]        =    [1, 2, 3]
     with pytest.raises(lowercase__				):
          with parallel_backend("unsupported backend"				):
               map_nested(lowercase__		,							lowercase__		,							num_proc=2				)

     with pytest.raises(lowercase__				):
          with parallel_backend("unsupported backend"				):
               map_nested(lowercase__		,							lowercase__		,							num_proc=-1				)







@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"		,							[2, -1]				)
def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    [1, 2]
     _lowerCAmelCase   :					List[Any]        =    {"a": 1, "b": 2}
     _lowerCAmelCase   :					str        =    {"a": [1, 2], "b": [3, 4]}
     _lowerCAmelCase   :					Any        =    {"a": {"1": 1}, "b": 2}
     _lowerCAmelCase   :					str        =    {"a": 1, "b": 2, "c": 3, "d": 4}
     _lowerCAmelCase   :					Union[str, Any]        =    [2, 3]
     _lowerCAmelCase   :					str        =    {"a": 2, "b": 3}
     _lowerCAmelCase   :					Dict        =    {"a": [2, 3], "b": [4, 5]}
     _lowerCAmelCase   :					Optional[Any]        =    {"a": {"1": 2}, "b": 3}
     _lowerCAmelCase   :					Union[str, Any]        =    {"a": 2, "b": 3, "c": 4, "d": 5}

     with parallel_backend("spark"				):
          assert map_nested(lowercase__		,							lowercase__		,							num_proc=lowercase__				) == expected_map_nested_sa
          assert map_nested(lowercase__		,							lowercase__		,							num_proc=lowercase__				) == expected_map_nested_sa
          assert map_nested(lowercase__		,							lowercase__		,							num_proc=lowercase__				) == expected_map_nested_sa
          assert map_nested(lowercase__		,							lowercase__		,							num_proc=lowercase__				) == expected_map_nested_sa
          assert map_nested(lowercase__		,							lowercase__		,							num_proc=lowercase__				) == expected_map_nested_sa









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 ):



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    1
      _lowerCAmelCase   :					List[str]        =    3
      _lowerCAmelCase   :					int        =    (3_2, 3_2)

      _lowerCAmelCase   :					Dict        =    floats_tensor((batch_size, num_channels) + sizes     ,						rng=random.Random(0       )       ).to(A_       )
      return image

 @property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Dict        =    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=A_     ,						only_cross_attention=(True, True, False)     ,						num_class_embeds=1_0_0     ,						)
      return model

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






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Union[str, Any]        =    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    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Optional[Any]        =    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(A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




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

      _lowerCAmelCase   :					List[Any]        =    self.dummy_image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					List[Any]        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((6_4, 6_4)       )

      # make sure here that pndm scheduler skips prk
      _lowerCAmelCase   :					List[Any]        =    StableDiffusionUpscalePipeline(
          unet=A_     ,						low_res_scheduler=A_     ,						scheduler=A_     ,						vae=A_     ,						text_encoder=A_     ,						tokenizer=A_     ,						max_noise_level=3_5_0     ,						)
      _lowerCAmelCase   :					Optional[int]        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					List[str]        =    "A painting of a squirrel eating a burger"
      _lowerCAmelCase   :					Tuple        =    torch.Generator(device=A_       ).manual_seed(0       )
      _lowerCAmelCase   :					Optional[int]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					Tuple        =    output.images

      _lowerCAmelCase   :					Optional[int]        =    torch.Generator(device=A_       ).manual_seed(0       )
      _lowerCAmelCase   :					Union[str, Any]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						return_dict=A_     ,						)[0]

      _lowerCAmelCase   :					List[Any]        =    image[0, -3:, -3:, -1]
      _lowerCAmelCase   :					List[str]        =    image_from_tuple[0, -3:, -3:, -1]

      _lowerCAmelCase   :					Optional[int]        =    low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)
      _lowerCAmelCase   :					str        =    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    __magic_name__       (		self :	int       ):






      '''simple docstring'''




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

      _lowerCAmelCase   :					Dict        =    self.dummy_image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Tuple        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((6_4, 6_4)       )

      # make sure here that pndm scheduler skips prk
      _lowerCAmelCase   :					Optional[Any]        =    StableDiffusionUpscalePipeline(
          unet=A_     ,						low_res_scheduler=A_     ,						scheduler=A_     ,						vae=A_     ,						text_encoder=A_     ,						tokenizer=A_     ,						max_noise_level=3_5_0     ,						)
      _lowerCAmelCase   :					Dict        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

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

      _lowerCAmelCase   :					str        =    torch.Generator(device=A_       ).manual_seed(0       )
      _lowerCAmelCase   :					List[Any]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						num_images_per_prompt=2     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					int        =    output.images
      assert image.shape[0] == 2


 @unittest.skipIf(torch_device != "cuda"     ,						"This test requires a GPU"       )
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




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

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

      # put models in fp16, except vae as it overflows in fp16
      _lowerCAmelCase   :					Tuple        =    unet.half()
      _lowerCAmelCase   :					Optional[Any]        =    text_encoder.half()

      # make sure here that pndm scheduler skips prk
      _lowerCAmelCase   :					List[str]        =    StableDiffusionUpscalePipeline(
          unet=A_     ,						low_res_scheduler=A_     ,						scheduler=A_     ,						vae=A_     ,						text_encoder=A_     ,						tokenizer=A_     ,						max_noise_level=3_5_0     ,						)
      _lowerCAmelCase   :					Optional[Any]        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

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

      _lowerCAmelCase   :					List[str]        =    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 ):



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	List[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   :					Dict        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
          "/upsampled_cat.npy"       )

      _lowerCAmelCase   :					Union[str, Any]        =    "stabilityai/stable-diffusion-x4-upscaler"
      _lowerCAmelCase   :					Optional[int]        =    StableDiffusionUpscalePipeline.from_pretrained(A_       )
      pipe.to(A_       )
      pipe.set_progress_bar_config(disable=A_       )
      pipe.enable_attention_slicing()

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

      _lowerCAmelCase   :					str        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					Optional[int]        =    pipe(
          prompt=A_     ,						image=A_     ,						generator=A_     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					int        =    output.images[0]

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

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




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

      _lowerCAmelCase   :					Union[str, Any]        =    "stabilityai/stable-diffusion-x4-upscaler"
      _lowerCAmelCase   :					Any        =    StableDiffusionUpscalePipeline.from_pretrained(
          A_     ,						torch_dtype=torch.floataa     ,						)
      pipe.to(A_       )
      pipe.set_progress_bar_config(disable=A_       )
      pipe.enable_attention_slicing()

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

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

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


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




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

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

      _lowerCAmelCase   :					Tuple        =    "stabilityai/stable-diffusion-x4-upscaler"
      _lowerCAmelCase   :					Any        =    StableDiffusionUpscalePipeline.from_pretrained(
          A_     ,						torch_dtype=torch.floataa     ,						)
      pipe.to(A_       )
      pipe.set_progress_bar_config(disable=A_       )
      pipe.enable_attention_slicing(1       )
      pipe.enable_sequential_cpu_offload()

      _lowerCAmelCase   :					List[Any]        =    "a cat sitting on a park bench"

      _lowerCAmelCase   :					Union[str, Any]        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					List[Any]        =    pipe(
          prompt=A_     ,						image=A_     ,						generator=A_     ,						num_inference_steps=5     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					Optional[Any]        =    torch.cuda.max_memory_allocated()
      # make sure that less than 2.9 GB is allocated
      assert mem_bytes < 2.9 * 1_0**9






