





"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   )			->				List[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							@property
							def 					__a     (     self   )			->				Optional[Any]:
								a   :	str			=       1
								a   :	Tuple			=       3
								a   :	Tuple			=       (32, 32)

								a   :	Tuple			=       floats_tensor((batch_size, num_channels) + sizes   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								return image






							@property
							def 					__a     (     self   )			->				Any:
								torch.manual_seed(0   )
								a   :	List[Any]			=       UNetaDConditionModel(
								    block_out_channels=(32, 64)   ,      layers_per_block=2   ,      sample_size=32   ,      in_channels=4   ,      out_channels=4   ,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,      cross_attention_dim=32   ,      )
								return model






							@property
							def 					__a     (     self   )			->				Union[str, Any]:
								torch.manual_seed(0   )
								a   :	Dict			=       AutoencoderKL(
								    block_out_channels=[32, 64]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,      latent_channels=4   ,      )
								return model






							@property
							def 					__a     (     self   )			->				str:
								torch.manual_seed(0   )
								a   :	Union[str, Any]			=       RobertaSeriesConfig(
								    hidden_size=32   ,      project_dim=32   ,      intermediate_size=37   ,      layer_norm_eps=1E-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=5006   ,      )
								return RobertaSeriesModelWithTransformation(lowerCAmelCase__   )






							@property
							def 					__a     (     self   )			->				Any:
								def extract(*lowerCAmelCase__   ,      **lowerCAmelCase__   ):


									class  __UpperCamelCase       :






																def __init__(     self   )			->				Optional[Any]:
																	a   :	Dict			=       torch.ones([0]   )






																def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[Any]:
																	self.pixel_values.to(lowerCAmelCase__   )
																	return self
									return Out()

								return extract






							def 					__a     (     self   )			->				List[Any]:
								a   :	Dict			=       "cpu"  # ensure determinism for the device-dependent torch.Generator
								a   :	Any			=       self.dummy_cond_unet
								a   :	Dict			=       PNDMScheduler(skip_prk_steps=lowerCAmelCase__   )
								a   :	Tuple			=       self.dummy_vae
								a   :	Any			=       self.dummy_text_encoder
								a   :	Tuple			=       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"   )
								a   :	str			=       77

								a   :	Tuple			=       self.dummy_image.to(lowerCAmelCase__   )
								a   :	Optional[int]			=       init_image / 2 + 0.5

								# make sure here that pndm scheduler skips prk
								a   :	Optional[Any]			=       AltDiffusionImgaImgPipeline(
								    unet=lowerCAmelCase__   ,      scheduler=lowerCAmelCase__   ,      vae=lowerCAmelCase__   ,      text_encoder=lowerCAmelCase__   ,      tokenizer=lowerCAmelCase__   ,      safety_checker=lowerCAmelCase__   ,      feature_extractor=self.dummy_extractor   ,      )
								a   :	Any			=       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,      do_normalize=lowerCAmelCase__   )
								a   :	Optional[Any]			=       alt_pipe.to(lowerCAmelCase__   )
								alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

								a   :	List[Any]			=       "A painting of a squirrel eating a burger"
								a   :	Any			=       torch.Generator(device=lowerCAmelCase__   ).manual_seed(0   )
								a   :	Dict			=       alt_pipe(
								    [prompt]   ,      generator=lowerCAmelCase__   ,      guidance_scale=6.0   ,      num_inference_steps=2   ,      output_type="np"   ,      image=lowerCAmelCase__   ,      )

								a   :	List[str]			=       output.images

								a   :	Dict			=       torch.Generator(device=lowerCAmelCase__   ).manual_seed(0   )
								a   :	List[Any]			=       alt_pipe(
								    [prompt]   ,      generator=lowerCAmelCase__   ,      guidance_scale=6.0   ,      num_inference_steps=2   ,      output_type="np"   ,      image=lowerCAmelCase__   ,      return_dict=lowerCAmelCase__   ,      )[0]

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

								assert image.shape == (1, 32, 32, 3)
								a   :	Dict			=       np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5E-3
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 5E-3






							@unittest.skipIf(torch_device != "cuda"   ,      "This test requires a GPU"   )
							def 					__a     (     self   )			->				List[str]:
								a   :	str			=       self.dummy_cond_unet
								a   :	int			=       PNDMScheduler(skip_prk_steps=lowerCAmelCase__   )
								a   :	Optional[Any]			=       self.dummy_vae
								a   :	Tuple			=       self.dummy_text_encoder
								a   :	Optional[int]			=       XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"   )
								a   :	Optional[Any]			=       77

								a   :	Any			=       self.dummy_image.to(lowerCAmelCase__   )

								# put models in fp16
								a   :	Any			=       unet.half()
								a   :	List[str]			=       vae.half()
								a   :	Optional[Any]			=       bert.half()

								# make sure here that pndm scheduler skips prk
								a   :	List[str]			=       AltDiffusionImgaImgPipeline(
								    unet=lowerCAmelCase__   ,      scheduler=lowerCAmelCase__   ,      vae=lowerCAmelCase__   ,      text_encoder=lowerCAmelCase__   ,      tokenizer=lowerCAmelCase__   ,      safety_checker=lowerCAmelCase__   ,      feature_extractor=self.dummy_extractor   ,      )
								a   :	Any			=       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,      do_normalize=lowerCAmelCase__   )
								a   :	List[str]			=       alt_pipe.to(lowerCAmelCase__   )
								alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

								a   :	Any			=       "A painting of a squirrel eating a burger"
								a   :	Any			=       torch.manual_seed(0   )
								a   :	int			=       alt_pipe(
								    [prompt]   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      image=lowerCAmelCase__   ,      ).images

								assert image.shape == (1, 32, 32, 3)






							@unittest.skipIf(torch_device != "cuda"   ,      "This test requires a GPU"   )
							def 					__a     (     self   )			->				List[Any]:
								a   :	List[Any]			=       load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/img2img/sketch-mountains-input.jpg"   )
								# resize to resolution that is divisible by 8 but not 16 or 32
								a   :	Optional[Any]			=       init_image.resize((760, 504)   )

								a   :	Optional[int]			=       "BAAI/AltDiffusion"
								a   :	Union[str, Any]			=       AltDiffusionImgaImgPipeline.from_pretrained(
								    lowerCAmelCase__   ,      safety_checker=lowerCAmelCase__   ,      )
								pipe.to(lowerCAmelCase__   )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__   )
								pipe.enable_attention_slicing()

								a   :	str			=       "A fantasy landscape, trending on artstation"

								a   :	Tuple			=       torch.manual_seed(0   )
								a   :	List[Any]			=       pipe(
								    prompt=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      strength=0.75   ,      guidance_scale=7.5   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )
								a   :	Union[str, Any]			=       output.images[0]

								a   :	List[Any]			=       image[255:258, 383:386, -1]

								assert image.shape == (504, 760, 3)
								a   :	Dict			=       np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000]   )

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


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






							def 					__a     (     self   )			->				List[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							def 					__a     (     self   )			->				Optional[int]:
								a   :	str			=       load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/img2img/sketch-mountains-input.jpg"   )
								a   :	Any			=       init_image.resize((768, 512)   )
								a   :	Optional[int]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"   )

								a   :	Optional[Any]			=       "BAAI/AltDiffusion"
								a   :	Optional[int]			=       AltDiffusionImgaImgPipeline.from_pretrained(
								    lowerCAmelCase__   ,      safety_checker=lowerCAmelCase__   ,      )
								pipe.to(lowerCAmelCase__   )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__   )
								pipe.enable_attention_slicing()

								a   :	Tuple			=       "A fantasy landscape, trending on artstation"

								a   :	Union[str, Any]			=       torch.manual_seed(0   )
								a   :	Dict			=       pipe(
								    prompt=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      strength=0.75   ,      guidance_scale=7.5   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )
								a   :	int			=       output.images[0]

								assert image.shape == (512, 768, 3)
								# img2img is flaky across GPUs even in fp32, so using MAE here
								assert np.abs(expected_image - image   ).max() < 1E-2









"""simple docstring"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str=None      )				->Optional[Any]:






	'''simple docstring'''






	if subparsers is not None:
		a   :	Dict			=       subparsers.add_parser("test"      )
	else:
		a   :	Tuple			=       argparse.ArgumentParser("Accelerate test command"      )

	parser.add_argument(
	    "--config_file"					, default=_lowercase					, help=(
	        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
	        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
	        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
	        "with 'huggingface'."
	    )					, )

	if subparsers is not None:
		parser.set_defaults(func=_lowercase      )
	return parser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->str:






	'''simple docstring'''






	a   :	List[Any]			=       os.path.sep.join(__file__.split(os.path.sep      )[:-2] + ["test_utils", "scripts", "test_script.py"]      )

	if args.config_file is None:
		a   :	int			=       script_name
	else:
		a   :	int			=       F"""--config_file={args.config_file} {script_name}"""

	a   :	Optional[int]			=       ["accelerate-launch"] + test_args.split()
	a   :	Optional[int]			=       execute_subprocess_async(_lowercase					, env=os.environ.copy()      )
	if result.returncode == 0:
		print("Test is a success! You are ready for your distributed training!"      )






def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	Any			=       test_command_parser()
	a   :	Union[str, Any]			=       parser.parse_args()
	test_command(_lowercase      )


if __name__ == "__main__":
		main()


