






'''simple docstring'''

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 							a_		(     _UpperCamelCase      ,			_UpperCamelCase      ,			unittest.TestCase							):
  lowercase							= IFInpaintingPipeline
  lowercase							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
  lowercase							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowercase							= PipelineTesterMixin.required_optional_params - {'latents'}

  def     A__		( self )    -> int:



        """simple docstring"""






        return self._get_dummy_components()

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=0 )    -> Union[str, Any]:



        """simple docstring"""






        if str(_SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
              UpperCamelCase							    =   torch.manual_seed(_SCREAMING_SNAKE_CASE )
        else:
              UpperCamelCase							    =   torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )

        UpperCamelCase							    =   floats_tensor((1, 3, 32, 32)     ,						rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
        UpperCamelCase							    =   floats_tensor((1, 3, 32, 32)     ,						rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )

        UpperCamelCase							    =   {
            """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 )    -> Optional[Any]:



        """simple docstring"""






        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )

  def     A__		( self )    -> Dict:



        """simple docstring"""






        self._test_save_load_optional_components()

  @unittest.skipIf(torch_device != """cuda"""     ,						reason="""float16 requires CUDA""" )
  def     A__		( self )    -> Optional[int]:



        """simple docstring"""






        super().test_save_load_floataa(expected_max_diff=1e-1 )

  def     A__		( self )    -> List[Any]:



        """simple docstring"""






        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )

  def     A__		( self )    -> Any:



        """simple docstring"""






        self._test_save_load_local()


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



        """simple docstring"""






        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2     ,						)






import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

a__		     =     logging.getLogger()





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					__UpperCamelCase					:	Dict             =      {}
					__UpperCamelCase					:	Dict             =      os.path.join(snake_case		,	"""all_results.json"""				)
					if os.path.exists(snake_case				):
										with open(snake_case		,	"""r"""				) as f:
															__UpperCamelCase					:	List[Any]             =      json.load(snake_case				)
					else:
										raise ValueError(F'''can\'t find {path}'''				)
					return results


a__		     =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Dict	)	->       Dict:







						"""simple docstring"""







						import xla_spawn

						__UpperCamelCase					:	Dict             =      self.get_auto_remove_tmp_dir()
						__UpperCamelCase					:	str             =      F'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

						with patch.object(lowerCAmelCase					,     """argv"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Union[str, Any]             =      time()
											xla_spawn.main()
											__UpperCamelCase					:	List[str]             =      time()

											__UpperCamelCase					:	Optional[int]             =      get_results(lowerCAmelCase	)
											self.assertGreaterEqual(result["""eval_accuracy"""]					,     0.75	)

											# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
											self.assertLess(end - start					,     500	)






	def 					lowerCamelCase__     (					self		:						int	)	->       List[Any]:







						"""simple docstring"""







						import xla_spawn

						__UpperCamelCase					:	List[Any]             =      """
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            """.split()
						with patch.object(lowerCAmelCase					,     """argv"""					,     lowerCAmelCase	):
											xla_spawn.main()






