
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     __lowerCAmelCase			(			a__						,    a__=False  )       ->  int:
				try:
								__a										=    os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								__a										=    default
				else:
								# KEY is set, convert it to True or False.
								try:
												__a										=    strtobool(lowerCAmelCase__  )
								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


A     :	Tuple    = parse_flag_from_env('RUN_SLOW', default=False)


def     __lowerCAmelCase			(			a__  )       ->  Any:
				return unittest.skip('''Test was skipped'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  List[str]:
				return unittest.skipUnless(_run_slow_tests						,    '''test is slow'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  List[str]:
				return unittest.skipUnless(not torch.cuda.is_available()						,    '''test requires only a CPU'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  int:
				return unittest.skipUnless(torch.cuda.is_available()						,    '''test requires a GPU'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  List[str]:
				return unittest.skipUnless(is_xpu_available()						,    '''test requires a XPU'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  Any:
				return unittest.skipUnless(is_mps_available()						,    '''test requires a `mps` backend support in `torch`'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  Tuple:
				return unittest.skipUnless(
				    is_transformers_available() and is_datasets_available()						,    '''test requires the Hugging Face suite'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  List[str]:
				return unittest.skipUnless(is_bnb_available()						,    '''test requires the bitsandbytes library'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  Optional[Any]:
				return unittest.skipUnless(is_tpu_available()						,    '''test requires TPU'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  List[str]:
				return unittest.skipUnless(torch.cuda.device_count() == 1						,    '''test requires a GPU'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  List[Any]:
				return unittest.skipUnless(torch.xpu.device_count() == 1						,    '''test requires a XPU'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  Dict:
				return unittest.skipUnless(torch.cuda.device_count() > 1						,    '''test requires multiple GPUs'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  Tuple:
				return unittest.skipUnless(torch.xpu.device_count() > 1						,    '''test requires multiple XPUs'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  List[Any]:
				return unittest.skipUnless(is_safetensors_available()						,    '''test requires safetensors'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  Any:
				return unittest.skipUnless(is_deepspeed_available()						,    '''test requires DeepSpeed'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  Optional[int]:
				return unittest.skipUnless(is_torch_version('''>='''						,    '''1.12.0'''  )						,    '''test requires torch version >= 1.12.0'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__=None						,    a__=None  )       ->  str:
				if test_case is None:
								return partial(lowerCAmelCase__						,    version=lowerCAmelCase__  )
				return unittest.skipUnless(is_torch_version('''>='''						,    lowerCAmelCase__  )						,    F"""test requires torch version >= {version}"""  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  List[str]:
				return unittest.skipUnless(is_tensorboard_available()						,    '''test requires Tensorboard'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  List[Any]:
				return unittest.skipUnless(is_wandb_available()						,    '''test requires wandb'''  )(lowerCAmelCase__  )


def     __lowerCAmelCase			(			a__  )       ->  str:
				return unittest.skipUnless(is_comet_ml_available()						,    '''test requires comet_ml'''  )(lowerCAmelCase__  )


A     :	Union[str, Any]    = (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)


def     __lowerCAmelCase			(			a__  )       ->  int:
				return unittest.skipUnless(
				    _atleast_one_tracker_available						,    '''test requires at least one tracker to be available and for `comet_ml` to not be installed'''						,    )(lowerCAmelCase__  )







class 			__A( unittest.TestCase			):
				snake_case_						   =    True




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls				)    ->      int:


								'''simple docstring'''




								__a										=    tempfile.mkdtemp()




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls				)    ->      Dict:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''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(A__				)







class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




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







class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




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



def     __lowerCAmelCase			(			a__  )       ->  int:
				__a										=    AcceleratorState()
				__a										=    tensor[None].clone().to(state.device  )
				__a										=    gather(lowerCAmelCase__  ).cpu()
				__a										=    tensor[0].cpu()
				for i in range(tensors.shape[0]  ):
								if not torch.equal(tensors[i]						,    lowerCAmelCase__  ):
												return False
				return True







class 			__A:




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    returncode
								__a										=    stdout
								__a										=    stderr



async def     __lowerCAmelCase			(			a__						,    a__  )       ->  Tuple:
				while True:
								__a										=    await stream.readline()
								if line:
												callback(lowerCAmelCase__  )
								else:
												break


async def     __lowerCAmelCase			(			a__						,    a__=None						,    a__=None						,    a__=None						,    a__=False						,    a__=False  )       ->  Optional[Any]:
				if echo:
								print('''\nRunning: '''						,    ''' '''.join(lowerCAmelCase__  )  )

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

				# 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)

				__a										=    []
				__a										=    []

				def tee(a__						,    a__						,    a__						,    a__=""  ):
								__a										=    line.decode('''utf-8'''  ).rstrip()
								sink.append(lowerCAmelCase__  )
								if not quiet:
												print(lowerCAmelCase__						,    lowerCAmelCase__						,    file=lowerCAmelCase__  )

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


def     __lowerCAmelCase			(			a__						,    a__=None						,    a__=None						,    a__=180						,    a__=False						,    a__=True  )       ->  Optional[Any]:
				__a										=    asyncio.get_event_loop()
				__a										=    loop.run_until_complete(
				    _stream_subprocess(lowerCAmelCase__						,    env=lowerCAmelCase__						,    stdin=lowerCAmelCase__						,    timeout=lowerCAmelCase__						,    quiet=lowerCAmelCase__						,    echo=lowerCAmelCase__  )  )

				__a										=    ''' '''.join(lowerCAmelCase__  )
				if result.returncode > 0:
								__a										=    '''\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 			__A( SCREAMING_SNAKE_CASE__			):
				pass



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

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_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  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# 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):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)




