


'''simple docstring'''







import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


__UpperCamelCase:   List[str]    =	logging.get_logger(__name__)

if is_vision_available():
   import PIL.Image
   from PIL import Image
   from PIL.Image import Image as ImageType
else:
   __UpperCamelCase:   Optional[Any]    =	object

if is_torch_available():
   import torch

if is_soundfile_availble():
   import soundfile as sf







class A__	:






      def __init__(			self      :str					,						SCREAMING_SNAKE_CASE      :Optional[Any]     )	-> Optional[int]:




             '''simple docstring'''
             _a   :			List[str]					 =value






      def __str__(			self      :Any     )	-> List[str]:




             '''simple docstring'''
             return self.to_string()






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




             '''simple docstring'''
             logger.error(
                 """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""     )
             return self._value






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




             '''simple docstring'''
             logger.error(
                 """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""     )
             return str(self._value     )







class A__	(						__UpperCAmelCase			,					__UpperCAmelCase				):






      def __UpperCAmelCase	(			self      :int     )	-> Union[str, Any]:




             '''simple docstring'''
             return self._value






      def __UpperCAmelCase	(			self      :List[str]     )	-> Dict:




             '''simple docstring'''
             return self._value







class A__	(						__UpperCAmelCase			,					__UpperCAmelCase				):






      def __init__(			self      :Any					,						SCREAMING_SNAKE_CASE      :List[str]     )	-> List[str]:




             '''simple docstring'''
             super().__init__(lowerCamelCase__     )

             if not is_vision_available():
                    raise ImportError("""PIL must be installed in order to handle images."""     )

             _a   :			Tuple					 =None
             _a   :			Dict					 =None
             _a   :			str					 =None

             if isinstance(lowerCamelCase__					,						lowerCamelCase__     ):
                    _a   :			List[Any]					 =value
             elif isinstance(lowerCamelCase__					,						(str, pathlib.Path)     ):
                    _a   :			Any					 =value
             elif isinstance(lowerCamelCase__					,						torch.Tensor     ):
                    _a   :			Optional[int]					 =value
             else:
                    raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase__     )}"     )






      def __UpperCAmelCase	(			self      :Any					,						SCREAMING_SNAKE_CASE      :Any=None					,						SCREAMING_SNAKE_CASE      :Optional[int]=None     )	-> Dict:




             '''simple docstring'''
             from IPython.display import Image, display

             display(Image(self.to_string()     )     )






      def __UpperCAmelCase	(			self      :Optional[int]     )	-> Dict:




             '''simple docstring'''



             if self._raw is not None:
                    return self._raw

             if self._path is not None:
                    _a   :			Dict					 =Image.open(self._path     )
                    return self._raw






      def __UpperCAmelCase	(			self      :List[str]     )	-> str:




             '''simple docstring'''



             if self._path is not None:
                    return self._path

             if self._raw is not None:
                    _a   :			Optional[Any]					 =tempfile.mkdtemp()
                    _a   :			Optional[Any]					 =os.path.join(lowerCamelCase__					,						str(uuid.uuida()     ) + """.png"""     )
                    self._raw.save(self._path     )

                    return self._path

             if self._tensor is not None:
                    _a   :			Dict					 =self._tensor.cpu().detach().numpy()

                    # There is likely simpler than load into image into save
                    _a   :			Dict					 =Image.fromarray((array * 2_5_5).astype(np.uinta     )     )

                    _a   :			Union[str, Any]					 =tempfile.mkdtemp()
                    _a   :			Optional[int]					 =os.path.join(lowerCamelCase__					,						str(uuid.uuida()     ) + """.png"""     )

                    img.save(self._path     )

                    return self._path







class A__	(						__UpperCAmelCase				):






      def __init__(			self      :int					,						SCREAMING_SNAKE_CASE      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :Tuple=1_6_0_0_0     )	-> Tuple:




             '''simple docstring'''
             super().__init__(lowerCamelCase__     )

             if not is_soundfile_availble():
                    raise ImportError("""soundfile must be installed in order to handle audio."""     )

             _a   :			Any					 =None
             _a   :			List[Any]					 =None

             _a   :			Optional[int]					 =samplerate

             if isinstance(lowerCamelCase__					,						(str, pathlib.Path)     ):
                    _a   :			Union[str, Any]					 =value
             elif isinstance(lowerCamelCase__					,						torch.Tensor     ):
                    _a   :			str					 =value
             else:
                    raise ValueError(f"Unsupported audio type: {type(lowerCamelCase__     )}"     )






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :Tuple=None					,						SCREAMING_SNAKE_CASE      :List[str]=None     )	-> Union[str, Any]:




             '''simple docstring'''
             from IPython.display import Audio, display

             display(Audio(self.to_string()					,						rate=self.samplerate     )     )






      def __UpperCAmelCase	(			self      :Any     )	-> List[str]:




             '''simple docstring'''



             if self._tensor is not None:
                    return self._tensor

             if self._path is not None:
                    _a ,   _a   :			List[str]					 =sf.read(self._path     )
                    _a   :			List[str]					 =torch.tensor(lowerCamelCase__     )
                    return self._tensor






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




             '''simple docstring'''



             if self._path is not None:
                    return self._path

             if self._tensor is not None:
                    _a   :			Any					 =tempfile.mkdtemp()
                    _a   :			List[Any]					 =os.path.join(lowerCamelCase__					,						str(uuid.uuida()     ) + """.wav"""     )
                    sf.write(self._path					,						self._tensor					,						samplerate=self.samplerate     )
                    return self._path

__UpperCamelCase:   int    =	{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
__UpperCamelCase:   Union[str, Any]    =	{str: AgentText}

if is_vision_available():
   __UpperCamelCase:   Tuple    =	AgentImage


def      SCREAMING_SNAKE_CASE_ (  *_UpperCAmelCase     :     Tuple   ,**_UpperCAmelCase     :     List[str]  )  ->      Optional[int]:
       _a   :			Tuple					 =[(arg.to_raw() if isinstance(lowercase__   ,lowercase__  ) else arg) for arg in args]
       _a   :			str					 ={k: (v.to_raw() if isinstance(lowercase__   ,lowercase__  ) else v) for k, v in kwargs.items()}
       return args, kwargs


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Optional[int]   ,_UpperCAmelCase     :     Tuple=None  )  ->      str:
       if isinstance(lowercase__   ,lowercase__  ):
              _a   :			List[str]					 ={}
              for i, (k, v) in enumerate(outputs.items()  ):
                     if output_types is not None:
                            # If the class has defined outputs, we can map directly according to the class definition
                            if output_types[i] in AGENT_TYPE_MAPPING:
                                   _a   :			List[Any]					 =AGENT_TYPE_MAPPING[output_types[i]](lowercase__  )
                            else:
                                   _a   :			List[Any]					 =AgentType(lowercase__  )

                     else:
                            # If the class does not have defined output, then we map according to the type
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(lowercase__   ,_k  ):
                                          _a   :			List[Any]					 =_v(lowercase__  )
                            if k not in decoded_outputs:
                                   _a   :			Any					 =AgentType[v]

       elif isinstance(lowercase__   ,(list, tuple)  ):
              _a   :			Union[str, Any]					 =type(lowercase__  )()
              for i, v in enumerate(lowercase__  ):
                     if output_types is not None:
                            # If the class has defined outputs, we can map directly according to the class definition
                            if output_types[i] in AGENT_TYPE_MAPPING:
                                   decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](lowercase__  )  )
                            else:
                                   decoded_outputs.append(AgentType(lowercase__  )  )
                     else:
                            # If the class does not have defined output, then we map according to the type
                            _a   :			str					 =False
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(lowercase__   ,_k  ):
                                          decoded_outputs.append(_v(lowercase__  )  )
                                          _a   :			Optional[int]					 =True

                            if not found:
                                   decoded_outputs.append(AgentType(lowercase__  )  )

       else:
              if output_types[0] in AGENT_TYPE_MAPPING:
                     # If the class has defined outputs, we can map directly according to the class definition
                     _a   :			str					 =AGENT_TYPE_MAPPING[output_types[0]](lowercase__  )

              else:
                     # If the class does not have defined output, then we map according to the type
                     for _k, _v in INSTANCE_TYPE_MAPPING.items():
                            if isinstance(lowercase__   ,_k  ):
                                   return _v(lowercase__  )
                     return AgentType(lowercase__  )

       return decoded_outputs






'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class A__	(						UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  str     	=							RePaintPipeline
      __UpperCamelCase	:  Dict     	=							IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      __UpperCamelCase	:  List[str]     	=							PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      __UpperCamelCase	:  List[Any]     	=							IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCamelCase	:  Tuple     	=							False






      def __UpperCAmelCase	(			self      :List[Any]     )	-> List[str]:




             '''simple docstring'''
             torch.manual_seed(0     )
             torch.manual_seed(0     )
             _a   :			Tuple					 =UNetaDModel(
                 block_out_channels=(3_2, 6_4)					,						layers_per_block=2					,						sample_size=3_2					,						in_channels=3					,						out_channels=3					,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,						)
             _a   :			Any					 =RePaintScheduler()
             _a   :			Optional[Any]					 ={"""unet""": unet, """scheduler""": scheduler}
             return components






      def __UpperCAmelCase	(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[Any]=0     )	-> Any:




             '''simple docstring'''
             if str(SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
                    _a   :			Optional[Any]					 =torch.manual_seed(SCREAMING_SNAKE_CASE     )
             else:
                    _a   :			Any					 =torch.Generator(device=SCREAMING_SNAKE_CASE     ).manual_seed(SCREAMING_SNAKE_CASE     )
             _a   :			int					 =np.random.RandomState(SCREAMING_SNAKE_CASE     ).standard_normal((1, 3, 3_2, 3_2)     )
             _a   :			Union[str, Any]					 =torch.from_numpy(SCREAMING_SNAKE_CASE     ).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			List[Any]					 =(image > 0).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			str					 ={
                 """image""": image,
                 """mask_image""": mask,
                 """generator""": generator,
                 """num_inference_steps""": 5,
                 """eta""": 0.0,
                 """jump_length""": 2,
                 """jump_n_sample""": 2,
                 """output_type""": """numpy""",
             }
             return inputs






      def __UpperCAmelCase	(			self      :Dict     )	-> Dict:




             '''simple docstring'''
             _a   :			Tuple					 ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
             _a   :			Tuple					 =self.get_dummy_components()
             _a   :			str					 =RePaintPipeline(**SCREAMING_SNAKE_CASE     )
             _a   :			int					 =sd_pipe.to(SCREAMING_SNAKE_CASE     )
             sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )

             _a   :			Optional[int]					 =self.get_dummy_inputs(SCREAMING_SNAKE_CASE     )
             _a   :			Optional[Any]					 =sd_pipe(**SCREAMING_SNAKE_CASE     ).images
             _a   :			Any					 =image[0, -3:, -3:, -1]

             assert image.shape == (1, 3_2, 3_2, 3)
             _a   :			str					 =np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






      @skip_mps
      def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             return super().test_save_load_local()






      @unittest.skip("""non-deterministic pipeline"""     )
      def __UpperCAmelCase	(			self      :Tuple     )	-> int:




             '''simple docstring'''
             return super().test_inference_batch_single_identical()






      @skip_mps
      def __UpperCAmelCase	(			self      :Any     )	-> Tuple:




             '''simple docstring'''
             return super().test_dict_tuple_outputs_equivalent()






      @skip_mps
      def __UpperCAmelCase	(			self      :str     )	-> Optional[Any]:




             '''simple docstring'''
             return super().test_save_load_optional_components()






      @skip_mps
      def __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




             '''simple docstring'''
             return super().test_attention_slicing_forward_pass()







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






      def __UpperCAmelCase	(			self      :Tuple     )	-> Optional[int]:




             '''simple docstring'''
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()






      def __UpperCAmelCase	(			self      :str     )	-> int:




             '''simple docstring'''
             _a   :			Optional[int]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256.png"""     )
             _a   :			Optional[Any]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""     )
             _a   :			Union[str, Any]					 =load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256_result.npy"""     )

             _a   :			str					 ="""google/ddpm-ema-celebahq-256"""
             _a   :			Union[str, Any]					 =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE     )
             _a   :			Dict					 =RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE     )

             _a   :			str					 =RePaintPipeline(unet=SCREAMING_SNAKE_CASE					,						scheduler=SCREAMING_SNAKE_CASE     ).to(SCREAMING_SNAKE_CASE     )
             repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )
             repaint.enable_attention_slicing()

             _a   :			Dict					 =torch.manual_seed(0     )
             _a   :			Any					 =repaint(
                 SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						num_inference_steps=2_5_0					,						eta=0.0					,						jump_length=1_0					,						jump_n_sample=1_0					,						generator=SCREAMING_SNAKE_CASE					,						output_type="""np"""					,						)
             _a   :			Any					 =output.images[0]

             assert image.shape == (2_5_6, 2_5_6, 3)
             assert np.abs(expected_image - image     ).mean() < 1e-2


