




import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)


def    A						(__A   :    List[str]   ,				__A   :    Optional[Any]=False       )   ->      List[Any]:







     """simple docstring"""


     try:
          UpperCAmelCase_          =							os.environ[key]
     except KeyError:
          # KEY isn't set, default to `default`.
          UpperCAmelCase_          =							default
     else:
          # KEY is set, convert it to True or False.
          try:
               UpperCAmelCase_          =							strtobool(__A       )
          except ValueError:
               # More values are supported, but let's keep the message simple.
               raise ValueError(F"""If set, {key} must be yes or no."""       )
     return _value


snake_case_ : Any    		=       parse_flag_from_env("RUN_SLOW", default=False)


def    A						(__A   :    Any       )   ->      Tuple:







     """simple docstring"""


     return unittest.skip('''Test was skipped'''       )(__A       )


def    A						(__A   :    int       )   ->      Dict:







     """simple docstring"""


     return unittest.skipUnless(_run_slow_tests   ,				'''test is slow'''       )(__A       )


def    A						(__A   :    Dict       )   ->      int:







     """simple docstring"""


     return unittest.skipUnless(not torch.cuda.is_available()   ,				'''test requires only a CPU'''       )(__A       )


def    A						(__A   :    Dict       )   ->      Tuple:







     """simple docstring"""


     return unittest.skipUnless(torch.cuda.is_available()   ,				'''test requires a GPU'''       )(__A       )


def    A						(__A   :    Optional[Any]       )   ->      Any:







     """simple docstring"""


     return unittest.skipUnless(is_xpu_available()   ,				'''test requires a XPU'''       )(__A       )


def    A						(__A   :    List[Any]       )   ->      str:







     """simple docstring"""


     return unittest.skipUnless(is_mps_available()   ,				'''test requires a `mps` backend support in `torch`'''       )(__A       )


def    A						(__A   :    int       )   ->      Optional[int]:







     """simple docstring"""


     return unittest.skipUnless(
         is_transformers_available() and is_datasets_available()   ,				'''test requires the Hugging Face suite'''       )(__A       )


def    A						(__A   :    Any       )   ->      List[Any]:







     """simple docstring"""


     return unittest.skipUnless(is_bnb_available()   ,				'''test requires the bitsandbytes library'''       )(__A       )


def    A						(__A   :    List[Any]       )   ->      List[str]:







     """simple docstring"""


     return unittest.skipUnless(is_tpu_available()   ,				'''test requires TPU'''       )(__A       )


def    A						(__A   :    List[Any]       )   ->      Optional[Any]:







     """simple docstring"""


     return unittest.skipUnless(torch.cuda.device_count() == 1   ,				'''test requires a GPU'''       )(__A       )


def    A						(__A   :    List[str]       )   ->      List[str]:







     """simple docstring"""


     return unittest.skipUnless(torch.xpu.device_count() == 1   ,				'''test requires a XPU'''       )(__A       )


def    A						(__A   :    Optional[int]       )   ->      Union[str, Any]:







     """simple docstring"""


     return unittest.skipUnless(torch.cuda.device_count() > 1   ,				'''test requires multiple GPUs'''       )(__A       )


def    A						(__A   :    Optional[Any]       )   ->      Union[str, Any]:







     """simple docstring"""


     return unittest.skipUnless(torch.xpu.device_count() > 1   ,				'''test requires multiple XPUs'''       )(__A       )


def    A						(__A   :    Union[str, Any]       )   ->      int:







     """simple docstring"""


     return unittest.skipUnless(is_safetensors_available()   ,				'''test requires safetensors'''       )(__A       )


def    A						(__A   :    Dict       )   ->      Optional[int]:







     """simple docstring"""


     return unittest.skipUnless(is_deepspeed_available()   ,				'''test requires DeepSpeed'''       )(__A       )


def    A						(__A   :    Union[str, Any]       )   ->      str:







     """simple docstring"""


     return unittest.skipUnless(is_torch_version('''>='''   ,				'''1.12.0'''       )   ,				'''test requires torch version >= 1.12.0'''       )(__A       )


def    A						(__A   :    Any=None   ,				__A   :    List[Any]=None       )   ->      List[str]:







     """simple docstring"""


     if test_case is None:
          return partial(__A   ,				version=__A       )
     return unittest.skipUnless(is_torch_version('''>='''   ,				__A       )   ,				F"""test requires torch version >= {version}"""       )(__A       )


def    A						(__A   :    Union[str, Any]       )   ->      Any:







     """simple docstring"""


     return unittest.skipUnless(is_tensorboard_available()   ,				'''test requires Tensorboard'''       )(__A       )


def    A						(__A   :    Union[str, Any]       )   ->      str:







     """simple docstring"""


     return unittest.skipUnless(is_wandb_available()   ,				'''test requires wandb'''       )(__A       )


def    A						(__A   :    Any       )   ->      Optional[Any]:







     """simple docstring"""


     return unittest.skipUnless(is_comet_ml_available()   ,				'''test requires comet_ml'''       )(__A       )


snake_case_ : Optional[int]    		=       (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)


def    A						(__A   :    Union[str, Any]       )   ->      Tuple:







     """simple docstring"""


     return unittest.skipUnless(
         _atleast_one_tracker_available   ,				'''test requires at least one tracker to be available and for `comet_ml` to not be installed'''   ,				)(__A       )







class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  True







   @classmethod
   def lowerCamelCase						(      cls		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							tempfile.mkdtemp()







   @classmethod
   def lowerCamelCase						(      cls		:						Dict):

        """simple docstring"""


        if os.path.exists(cls.tmpdir):
             shutil.rmtree(cls.tmpdir)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        if self.clear_on_setup:
             for path in Path(self.tmpdir).glob('''**/*'''):
                  if path.is_file():
                       path.unlink()
                  elif path.is_dir():
                       shutil.rmtree(_snake_case)







class __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        super().tearDown()
        # Reset the state of the AcceleratorState singleton.
        AcceleratorState._reset_state()
        PartialState._reset_state()







class __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Union[mock.Mock, List[mock.Mock]]):

        """simple docstring"""


        UpperCAmelCase_          =							mocks if isinstance(_snake_case					,						(tuple, list)) else [mocks]
        for m in self.mocks:
             m.start()
             self.addCleanup(m.stop)







def    A						(__A   :    Optional[int]       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							AcceleratorState()
     UpperCAmelCase_          =							tensor[None].clone().to(state.device       )
     UpperCAmelCase_          =							gather(__A       ).cpu()
     UpperCAmelCase_          =							tensor[0].cpu()
     for i in range(tensors.shape[0]       ):
          if not torch.equal(tensors[i]   ,				__A       ):
               return False
     return True







class __snake_case			:







   def __init__(      self		:						List[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							returncode
        UpperCAmelCase_          =							stdout
        UpperCAmelCase_          =							stderr







async def    A						(__A   :    List[Any]   ,				__A   :    Union[str, Any]       )   ->      Tuple:







     """simple docstring"""


     while True:
          UpperCAmelCase_          =							await stream.readline()
          if line:
               callback(__A       )
          else:
               break


async def    A						(__A   :    Optional[int]   ,				__A   :    Optional[int]=None   ,				__A   :    Any=None   ,				__A   :    List[Any]=None   ,				__A   :    Any=False   ,				__A   :    Optional[int]=False       )   ->      _RunOutput:







     """simple docstring"""


     if echo:
          print('''\nRunning: '''   ,				''' '''.join(__A       )       )

     UpperCAmelCase_          =							await asyncio.create_subprocess_exec(
         cmd[0]   ,				*cmd[1:]   ,				stdin=__A   ,				stdout=asyncio.subprocess.PIPE   ,				stderr=asyncio.subprocess.PIPE   ,				env=__A   ,				)

     # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
     # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
     #
     # If it starts hanging, will need to switch to the following code. The problem is that no data
     # will be seen until it's done and if it hangs for example there will be no debug info.
     # out, err = await p.communicate()
     # return _RunOutput(p.returncode, out, err)

     UpperCAmelCase_          =							[]
     UpperCAmelCase_          =							[]

     def tee(__A   :    Optional[int]   ,				__A   :    List[Any]   ,				__A   :    str   ,				__A   :    Any=""       ):
          UpperCAmelCase_          =							line.decode('''utf-8'''       ).rstrip()
          sink.append(__A       )
          if not quiet:
               print(__A   ,				__A   ,				file=__A       )

    # XXX: the timeout doesn't seem to make any difference here
     await asyncio.wait(
         [
             asyncio.create_task(_read_stream(p.stdout   ,				lambda __A       : tee(__A   ,				__A   ,				sys.stdout   ,				label='''stdout:'''       )       )       ),
             asyncio.create_task(_read_stream(p.stderr   ,				lambda __A       : tee(__A   ,				__A   ,				sys.stderr   ,				label='''stderr:'''       )       )       ),
         ]   ,				timeout=__A   ,				)
     return _RunOutput(await p.wait()   ,				__A   ,				__A       )


def    A						(__A   :    Dict   ,				__A   :    Any=None   ,				__A   :    Any=None   ,				__A   :    List[Any]=180   ,				__A   :    Optional[int]=False   ,				__A   :    Any=True       )   ->      _RunOutput:







     """simple docstring"""


     UpperCAmelCase_          =							asyncio.get_event_loop()
     UpperCAmelCase_          =							loop.run_until_complete(
         _stream_subprocess(__A   ,				env=__A   ,				stdin=__A   ,				timeout=__A   ,				quiet=__A   ,				echo=__A       )       )

     UpperCAmelCase_          =							''' '''.join(__A       )
     if result.returncode > 0:
          UpperCAmelCase_          =							'''\n'''.join(result.stderr       )
          raise RuntimeError(
              F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
              F"""The combined stderr from workers follows:\n{stderr}"""       )

     return result







class __snake_case			(			a		):
   pass







def    A						(__A   :    List[str]   ,				__A   :    Tuple=False       )   ->      Optional[Any]:







     """simple docstring"""


     try:
          UpperCAmelCase_          =							subprocess.check_output(__A   ,				stderr=subprocess.STDOUT       )
          if return_stdout:
               if hasattr(__A   ,				'''decode'''       ):
                    UpperCAmelCase_          =							output.decode('''utf-8'''       )
               return output
     except subprocess.CalledProcessError as e:
          raise SubprocessCallException(
              F"""Command `{" ".join(__A       )}` failed with the following error:\n\n{e.output.decode()}"""       ) from e







"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A			   = False


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    pass



@nightly
@require_torch_gpu
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   List[str]					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   str					=      torch.manual_seed(0							)
       lowercase__:   Union[str, Any]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(_UpperCAmelCase							)
          lowercase__:   Optional[Any]					=      VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Optional[int]					=      generator.manual_seed(0							)
       lowercase__:   List[str]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion'''    ,			torch_dtype=torch.floataa							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Tuple					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   Optional[Any]					=      torch.manual_seed(0							)
       lowercase__:   Tuple					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='''numpy'''							).images

       lowercase__:   Union[str, Any]					=      image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowercase__:   Any					=      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

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