'''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  __magic_name__  (		__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   IFInpaintingPipeline
   lowerCAmelCase      :    List[str]  			=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
   lowerCAmelCase      :    Optional[Any]  			=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCAmelCase      :    List[str]  			=   PipelineTesterMixin.required_optional_params - {'''latents'''}



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    return self._get_dummy_components()



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  List[Any]		,  _lowercase					:  Optional[int]=0							):



    """simple docstring"""



    if str(_lowercase							).startswith('''mps'''							):
     _UpperCamelCase:     int							= torch.manual_seed(_lowercase							)
    else:
     _UpperCamelCase:     int							= torch.Generator(device=_lowercase							).manual_seed(_lowercase							)

    _UpperCamelCase:     Union[str, Any]							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)
    _UpperCamelCase:     Tuple							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)

    _UpperCamelCase:     Dict							= {
        '''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   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    self._test_save_load_optional_components()



   @unittest.skipIf(torch_device != '''cuda'''		,  reason='''float16 requires CUDA'''							)
   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    self._test_save_load_local()



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    self._test_inference_batch_single_identical(
        expected_max_diff=1E-2		,  )
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    '''split_dict'''  ,					[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42  ,					dataset_name='''my_dataset'''		)}		),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42		)}		),
        SplitDict({'''train''': SplitInfo()}		),
    ]  ,					)
def       lowerCAmelCase_     (		lowercase:							SplitDict		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= split_dict._to_yaml_list()
 assert len(lowercase		) == len(lowercase		)
 _UpperCamelCase:     Tuple							= SplitDict._from_yaml_list(lowercase		)
 for split_name, split_info in split_dict.items():
  # dataset_name field is deprecated, and is therefore not part of the YAML dump
  _UpperCamelCase:     Optional[Any]							= None
  # the split name of split_dict takes over the name of the split info object
  _UpperCamelCase:     Any							= split_name
 assert split_dict == reloaded





@pytest.mark.parametrize(
    '''split_info'''  ,					[SplitInfo(), SplitInfo(dataset_name=lowercase		), SplitInfo(dataset_name='''my_dataset'''		)]		)
def       lowerCAmelCase_     (		lowercase:							str		)    ->				Tuple:







 '''simple docstring'''
 # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
 # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
 _UpperCamelCase:     Optional[Any]							= asdict(SplitDict({'''train''': split_info}		)		)
 assert "dataset_name" in split_dict_asdict["train"]
 assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name