


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 							_UpperCamelCase					(	__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Optional[Any]         =   IFInpaintingPipeline
       _UpperCamelCase				:					str         =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
       _UpperCamelCase				:					Dict         =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       _UpperCamelCase				:					List[str]         =   PipelineTesterMixin.required_optional_params - {'''latents'''}

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




              """simple docstring"""


              return self._get_dummy_components()

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: Any=0		)       -> int:




              """simple docstring"""


              if str(_a		).startswith("mps"		):
                     UpperCamelCase_          =  torch.manual_seed(_a		)
              else:
                     UpperCamelCase_          =  torch.Generator(device=_a		).manual_seed(_a		)

              UpperCamelCase_          =  floats_tensor((1, 3, 32, 32)					,   rng=random.Random(_a		)		).to(_a		)
              UpperCamelCase_          =  floats_tensor((1, 3, 32, 32)					,   rng=random.Random(_a		)		).to(_a		)

              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 lowercase (		self: List[str]		)       -> Union[str, Any]:




              """simple docstring"""


              self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)

       def lowercase (		self: Optional[Any]		)       -> Optional[int]:




              """simple docstring"""


              self._test_save_load_optional_components()

       @unittest.skipIf(torch_device != "cuda"					,   reason="float16 requires CUDA"		)
       def lowercase (		self: List[str]		)       -> List[Any]:




              """simple docstring"""


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

       def lowercase (		self: Dict		)       -> Any:




              """simple docstring"""


              self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)

       def lowercase (		self: Any		)       -> int:




              """simple docstring"""


              self._test_save_load_local()



       def lowercase (		self: Dict		)       -> Tuple:




              """simple docstring"""


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








import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				'T5Config'




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		jnp.ndarray:
       UpperCamelCase_          =  jnp.zeros_like(UpperCamelCase_  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 0].set(UpperCamelCase_  )

       UpperCamelCase_          =  jnp.where(shifted_input_ids == -100					,		UpperCamelCase_					,		UpperCamelCase_  )
       return shifted_input_ids



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Union[str, Any]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Optional[int]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   '''mt5'''
       _UpperCamelCase				:					List[Any]         =   MTaConfig

