





"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
       lowerCamelCase     :      Dict         =IFPipeline
       lowerCamelCase     :      int         =TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
       lowerCamelCase     :      int         =TEXT_TO_IMAGE_BATCH_PARAMS
       lowerCamelCase     :      int         =PipelineTesterMixin.required_optional_params - {"""latents"""}






       def 					__a     (     self   )			->				List[str]:
        return self._get_dummy_components()






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=0   )			->				Dict:
        if str(lowerCAmelCase__   ).startswith("mps"   ):
         a   :	Tuple			=       torch.manual_seed(lowerCAmelCase__   )
        else:
         a   :	int			=       torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )

        a   :	Optional[Any]			=       {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "numpy",
        }

        return inputs






       def 					__a     (     self   )			->				Union[str, Any]:
        self._test_save_load_optional_components()






       @unittest.skipIf(torch_device != "cuda"   ,      reason="float16 requires CUDA"   )
       def 					__a     (     self   )			->				Any:
        # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
        super().test_save_load_floataa(expected_max_diff=1E-1   )






       def 					__a     (     self   )			->				Union[str, Any]:
        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )






       def 					__a     (     self   )			->				Optional[int]:
        self._test_save_load_local()






       def 					__a     (     self   )			->				Tuple:
        self._test_inference_batch_single_identical(
            expected_max_diff=1E-2   ,      )






       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()   ,      reason="XFormers attention is only available with CUDA and `xformers` installed"   ,      )
       def 					__a     (     self   )			->				str:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )


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






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






       def 					__a     (     self   )			->				Tuple:
        # if

        a   :	Tuple			=       IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"   ,      variant="fp16"   ,      torch_dtype=torch.floataa   )

        a   :	str			=       IFSuperResolutionPipeline.from_pretrained(
            "DeepFloyd/IF-II-L-v1.0"   ,      variant="fp16"   ,      torch_dtype=torch.floataa   ,      text_encoder=lowerCAmelCase__   ,      tokenizer=lowerCAmelCase__   )

        # pre compute text embeddings and remove T5 to save memory

        pipe_a.text_encoder.to("cuda"   )

        a   :	List[str]			=       pipe_a.encode_prompt("anime turtle"   ,      device="cuda"   )

        del pipe_a.tokenizer
        del pipe_a.text_encoder
        gc.collect()

        a   :	Optional[int]			=       None
        a   :	Optional[int]			=       None

        pipe_a.enable_model_cpu_offload()
        pipe_a.enable_model_cpu_offload()

        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

        self._test_if(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )

        pipe_a.remove_all_hooks()
        pipe_a.remove_all_hooks()

        # img2img

        a   :	Union[str, Any]			=       IFImgaImgPipeline(**pipe_a.components   )
        a   :	List[Any]			=       IFImgaImgSuperResolutionPipeline(**pipe_a.components   )

        pipe_a.enable_model_cpu_offload()
        pipe_a.enable_model_cpu_offload()

        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

        self._test_if_imgaimg(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )

        pipe_a.remove_all_hooks()
        pipe_a.remove_all_hooks()

        # inpainting

        a   :	Union[str, Any]			=       IFInpaintingPipeline(**pipe_a.components   )
        a   :	List[str]			=       IFInpaintingSuperResolutionPipeline(**pipe_a.components   )

        pipe_a.enable_model_cpu_offload()
        pipe_a.enable_model_cpu_offload()

        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

        self._test_if_inpainting(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
        # pipeline 1

        _start_torch_memory_measurement()

        a   :	List[str]			=       torch.Generator(device="cpu"   ).manual_seed(0   )
        a   :	Dict			=       pipe_a(
            prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

        a   :	List[str]			=       output.images[0]

        assert image.shape == (64, 64, 3)

        a   :	Dict			=       torch.cuda.max_memory_allocated()
        assert mem_bytes < 13 * 10**9

        a   :	Optional[Any]			=       load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"   )
        assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

        # pipeline 2

        _start_torch_memory_measurement()

        a   :	List[str]			=       torch.Generator(device="cpu"   ).manual_seed(0   )

        a   :	Optional[int]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

        a   :	Union[str, Any]			=       pipe_a(
            prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

        a   :	List[str]			=       output.images[0]

        assert image.shape == (256, 256, 3)

        a   :	int			=       torch.cuda.max_memory_allocated()
        assert mem_bytes < 4 * 10**9

        a   :	Union[str, Any]			=       load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"   )
        assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				int:
        # pipeline 1

        _start_torch_memory_measurement()

        a   :	int			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

        a   :	Tuple			=       torch.Generator(device="cpu"   ).manual_seed(0   )

        a   :	List[Any]			=       pipe_a(
            prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

        a   :	Tuple			=       output.images[0]

        assert image.shape == (64, 64, 3)

        a   :	int			=       torch.cuda.max_memory_allocated()
        assert mem_bytes < 10 * 10**9

        a   :	Optional[int]			=       load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"   )
        assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

        # pipeline 2

        _start_torch_memory_measurement()

        a   :	Optional[int]			=       torch.Generator(device="cpu"   ).manual_seed(0   )

        a   :	List[Any]			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
        a   :	str			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

        a   :	Dict			=       pipe_a(
            prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      original_image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

        a   :	int			=       output.images[0]

        assert image.shape == (256, 256, 3)

        a   :	str			=       torch.cuda.max_memory_allocated()
        assert mem_bytes < 4 * 10**9

        a   :	Any			=       load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"   )
        assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






       def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[int]:
        # pipeline 1

        _start_torch_memory_measurement()

        a   :	Union[str, Any]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
        a   :	List[Any]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(1   )   ).to(lowerCAmelCase__   )

        a   :	Optional[int]			=       torch.Generator(device="cpu"   ).manual_seed(0   )
        a   :	List[str]			=       pipe_a(
            prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      mask_image=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

        a   :	List[Any]			=       output.images[0]

        assert image.shape == (64, 64, 3)

        a   :	Tuple			=       torch.cuda.max_memory_allocated()
        assert mem_bytes < 10 * 10**9

        a   :	Union[str, Any]			=       load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"   )
        assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

        # pipeline 2

        _start_torch_memory_measurement()

        a   :	str			=       torch.Generator(device="cpu"   ).manual_seed(0   )

        a   :	Optional[int]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
        a   :	int			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
        a   :	Dict			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(1   )   ).to(lowerCAmelCase__   )

        a   :	Optional[int]			=       pipe_a(
            prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      mask_image=lowerCAmelCase__   ,      original_image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

        a   :	List[str]			=       output.images[0]

        assert image.shape == (256, 256, 3)

        a   :	Tuple			=       torch.cuda.max_memory_allocated()
        assert mem_bytes < 4 * 10**9

        a   :	Optional[Any]			=       load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"   )
        assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






def    _SCREAMING_SNAKE_CASE	(			)				->List[str]:






 '''simple docstring'''






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









"""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()


