
'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class   lowerCamelCase	(						A_							,				A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							List[Any]     =	IFImgaImgSuperResolutionPipeline
     UpperCAmelCase__       :							Optional[int]     =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     UpperCAmelCase__       :							Optional[Any]     =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}						)
     UpperCAmelCase__       :							Union[str, Any]     =	PipelineTesterMixin.required_optional_params - {"latents"}





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Tuple:
         return self._get_superresolution_dummy_components()





     def 	UpperCAmelCase(self   :					str       ,					_A   :					Optional[int]       ,					_A   :					List[Any]=0     )		->		int:
         if str(_A     ).startswith("mps"     ):
             snake_case				  =      torch.manual_seed(_A     )
         else:
             snake_case				  =      torch.Generator(device=_A     ).manual_seed(_A     )

         snake_case				  =      floats_tensor((1, 3, 3_2, 3_2)       ,					rng=random.Random(_A     )     ).to(_A     )
         snake_case				  =      floats_tensor((1, 3, 1_6, 1_6)       ,					rng=random.Random(_A     )     ).to(_A     )

         snake_case				  =      {
             "prompt": "A painting of a squirrel eating a burger",
             "image": image,
             "original_image": original_image,
             "generator": generator,
             "num_inference_steps": 2,
             "output_type": "numpy",
         }

         return inputs





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





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





     @unittest.skipIf(torch_device != "cuda"       ,					reason="float16 requires CUDA"     )
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         # 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 	UpperCAmelCase(self   :					Dict     )		->		str:
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		Any:
         self._test_save_load_local()





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		int:
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2       ,					)



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment



def 		lowercase_    (     A__				,	A__=()				,	A__=None				,	A__="no"				,	A__="29500"	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      False
    snake_case				  =      False
    if any(key.startswith("KAGGLE"	) for key in os.environ.keys()	):
        snake_case				  =      True
    elif "IPython" in sys.modules:
        snake_case				  =      "google.colab" in str(sys.modules["IPython"].get_ipython()	)

    try:
        snake_case				  =      PrecisionType(mixed_precision.lower()	)
    except ValueError:
        raise ValueError(
            F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'	)

    if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"				,	A__	) is not None):
        # TPU launch
        import torch_xla.distributed.xla_multiprocessing as xmp

        if len(AcceleratorState._shared_state	) > 0:
            raise ValueError(
                "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                "your training function. Restart your notebook and make sure no cells initializes an "
                "`Accelerator`."	)
        if num_processes is None:
            snake_case				  =      8

        snake_case				  =      PrepareForLaunch(A__				,	distributed_type="TPU"	)
        print(F'Launching a training on {num_processes} TPU cores.'	)
        xmp.spawn(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
    elif in_colab:
        # No need for a distributed launch otherwise as it's either CPU or one GPU.
        if torch.cuda.is_available():
            print("Launching training on one GPU."	)
        else:
            print("Launching training on one CPU."	)
        function(*A__	)
    else:
        if num_processes is None:
            raise ValueError(
                "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."	)

        if num_processes > 1:
            # Multi-GPU launch
            from torch.multiprocessing import start_processes
            from torch.multiprocessing.spawn import ProcessRaisedException

            if len(AcceleratorState._shared_state	) > 0:
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                    "inside your training function. Restart your notebook and make sure no cells initializes an "
                    "`Accelerator`."	)

            if torch.cuda.is_initialized():
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                    "function."	)

            # torch.distributed will expect a few environment variable to be here. We set the ones common to each
            # process here (the other ones will be set be the launcher).
            with patch_environment(
                world_size=A__				,	master_addr="127.0.01"				,	master_port=A__				,	mixed_precision=A__	):
                snake_case				  =      PrepareForLaunch(A__				,	distributed_type="MULTI_GPU"	)
                print(F'Launching training on {num_processes} GPUs.'	)
                try:
                    start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
                except ProcessRaisedException as e:
                    if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                        raise RuntimeError(
                            "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                            "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                            "Please review your imports and test them when running the `notebook_launcher()` to identify "
                            "which one is problematic."	) from e

        else:
            # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
            if is_mps_available():
                snake_case				  =      "1"
                print("Launching training on MPS."	)
            elif torch.cuda.is_available():
                print("Launching training on one GPU."	)
            else:
                print("Launching training on CPU."	)
            function(*A__	)





def 		lowercase_    (     A__				,	A__=()				,	A__=2	)							->							Dict:
    """simple docstring"""

    from torch.multiprocessing import start_processes

    with tempfile.NamedTemporaryFile() as tmp_file:
        # torch.distributed will expect a few environment variable to be here. We set the ones common to each
        # process here (the other ones will be set be the launcher).
        with patch_environment(
            world_size=A__				,	master_addr="127.0.01"				,	master_port="29500"				,	accelerate_mixed_precision="no"				,	accelerate_debug_rdv_file=tmp_file.name				,	accelerate_use_cpu="yes"				,	):
            snake_case				  =      PrepareForLaunch(A__				,	debug=A__	)
            start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)

