



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


__a     =    logging.get_logger(__name__)

if is_vision_available():
						import PIL.Image
						from PIL import Image
						from PIL.Image import Image as ImageType
else:
						__a     =    object

if is_torch_available():
						import torch

if is_soundfile_availble():
						import soundfile as sf
class     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							str						,					snake_case:							Dict						)       ->     List[Any]:
											snake_case_			:Union[str, Any]						=      value






					def __str__(	self:							Tuple						)       ->     str:
											return self.to_string()






					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     Optional[Any]:
											logger.error(
											    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""						)
											return self._value






					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     str:
											logger.error(
											    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""						)
											return str(self._value						)

class     lowerCamelCase  (      _lowerCAmelCase   ,						_lowerCAmelCase    ):






					'''simple docstring'''





					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Tuple:
											return self._value






					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     List[str]:
											return self._value

class     lowerCamelCase  (      _lowerCAmelCase   ,						_lowerCAmelCase    ):






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							Tuple						)       ->     Any:
											super().__init__(snake_case						)

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

											snake_case_			:List[Any]						=      None
											snake_case_			:Tuple						=      None
											snake_case_			:List[str]						=      None

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






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							str=None						,					snake_case:							Any=None						)       ->     Tuple:
											from IPython.display import Image, display

											display(Image(self.to_string()						)						)






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[str]:
											if self._raw is not None:
																	return self._raw

											if self._path is not None:
																	snake_case_			:Optional[int]						=      Image.open(self._path						)
																	return self._raw






					def        lowerCAmelCase_    (	self:							Tuple						)       ->     Any:
											if self._path is not None:
																	return self._path

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

																	return self._path

											if self._tensor is not None:
																	snake_case_			:Tuple						=      self._tensor.cpu().detach().numpy()

																	# There is likely simpler than load into image into save
																	snake_case_			:Optional[int]						=      Image.fromarray((array * 255).astype(np.uinta						)						)

																	snake_case_			:str						=      tempfile.mkdtemp()
																	snake_case_			:Union[str, Any]						=      os.path.join(snake_case						,					str(uuid.uuida()						) + """.png"""						)

																	img.save(self._path						)

																	return self._path

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					def __init__(	self:							Optional[Any]						,					snake_case:							Union[str, Any]						,					snake_case:							int=16_000						)       ->     List[str]:
											super().__init__(snake_case						)

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

											snake_case_			:List[str]						=      None
											snake_case_			:List[str]						=      None

											snake_case_			:Union[str, Any]						=      samplerate

											if isinstance(snake_case						,					(str, pathlib.Path)						):
																	snake_case_			:Tuple						=      value
											elif isinstance(snake_case						,					torch.Tensor						):
																	snake_case_			:Dict						=      value
											else:
																	raise ValueError(f"""Unsupported audio type: {type(snake_case						)}"""						)






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							Tuple=None						,					snake_case:							str=None						)       ->     Optional[Any]:
											from IPython.display import Audio, display

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






					def        lowerCAmelCase_    (	self:							List[str]						)       ->     int:
											if self._tensor is not None:
																	return self._tensor

											if self._path is not None:
																	snake_case_			:Union[str, Any]						=      sf.read(self._path						)
																	snake_case_			:Union[str, Any]						=      torch.tensor(snake_case						)
																	return self._tensor






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Any:
											if self._path is not None:
																	return self._path

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

__a     =    {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
__a     =    {str: AgentText}

if is_vision_available():
						__a     =    AgentImage
def        A_  (      *_lowercase, **_lowercase     ):







						'''simple docstring'''


						snake_case_			:Optional[Any]						=      [(arg.to_raw() if isinstance(_lowercase, _lowercase     ) else arg) for arg in args]
						snake_case_			:Optional[Any]						=      {k: (v.to_raw() if isinstance(_lowercase, _lowercase     ) else v) for k, v in kwargs.items()}
						return args, kwargs

def        A_  (      _lowercase, _lowercase=None     ):







						'''simple docstring'''


						if isinstance(_lowercase, _lowercase     ):
												snake_case_			: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:
																														snake_case_			:List[Any]						=      AGENT_TYPE_MAPPING[output_types[i]](_lowercase     )
																								else:
																														snake_case_			:str						=      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     ):
																																				snake_case_			:Union[str, Any]						=      _v(_lowercase     )
																								if k not in decoded_outputs:
																														snake_case_			:List[Any]						=      AgentType[v]

						elif isinstance(_lowercase, (list, tuple)     ):
												snake_case_			:List[str]						=      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
																								snake_case_			:List[Any]						=      False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(_lowercase, _k     ):
																																				decoded_outputs.append(_v(_lowercase     )     )
																																				snake_case_			:Tuple						=      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
																		snake_case_			:int						=      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     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					RePaintPipeline
					_A		:      List[Any]						=					IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
					_A		:      Union[str, Any]						=					PipelineTesterMixin.required_optional_params - {
					    """latents""",
					    """num_images_per_prompt""",
					    """callback""",
					    """callback_steps""",
					}
					_A		:      Tuple						=					IMAGE_INPAINTING_BATCH_PARAMS
					_A		:      Any						=					False






					def        lowerCAmelCase_    (	self:							Any						)       ->     List[str]:
											torch.manual_seed(0						)
											torch.manual_seed(0						)
											snake_case_			:Union[str, Any]						=      UNetaDModel(
											    block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,					)
											snake_case_			:str						=      RePaintScheduler()
											snake_case_			:Any						=      {"""unet""": unet, """scheduler""": scheduler}
											return components






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							Dict						,					snake_case:							List[Any]=0						)       ->     Tuple:
											if str(snake_case						).startswith("""mps"""						):
																	snake_case_			:int						=      torch.manual_seed(snake_case						)
											else:
																	snake_case_			:Optional[Any]						=      torch.Generator(device=snake_case						).manual_seed(snake_case						)
											snake_case_			:Dict						=      np.random.RandomState(snake_case						).standard_normal((1, 3, 32, 32)						)
											snake_case_			:int						=      torch.from_numpy(snake_case						).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Dict						=      (image > 0).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Union[str, Any]						=      {
											    """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        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											snake_case_			:int						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
											snake_case_			:List[str]						=      self.get_dummy_components()
											snake_case_			:Union[str, Any]						=      RePaintPipeline(**snake_case						)
											snake_case_			:Tuple						=      sd_pipe.to(snake_case						)
											sd_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_			:Tuple						=      self.get_dummy_inputs(snake_case						)
											snake_case_			:Dict						=      sd_pipe(**snake_case						).images
											snake_case_			:List[Any]						=      image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_			:Optional[int]						=      np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]						)

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






					@skip_mps
					def        lowerCAmelCase_    (	self:							Dict						)       ->     int:
											return super().test_save_load_local()






					@unittest.skip("""non-deterministic pipeline"""						)
					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     List[Any]:
											return super().test_inference_batch_single_identical()






					@skip_mps
					def        lowerCAmelCase_    (	self:							int						)       ->     Optional[Any]:
											return super().test_dict_tuple_outputs_equivalent()






					@skip_mps
					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Optional[Any]:
											return super().test_save_load_optional_components()






					@skip_mps
					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											return super().test_attention_slicing_forward_pass()

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






					'''simple docstring'''





					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     str:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Tuple:
											snake_case_			:Any						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256.png"""						)
											snake_case_			:Union[str, Any]						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""						)
											snake_case_			:str						=      load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256_result.npy"""						)

											snake_case_			:List[str]						=      """google/ddpm-ema-celebahq-256"""
											snake_case_			:Optional[int]						=      UNetaDModel.from_pretrained(snake_case						)
											snake_case_			:List[Any]						=      RePaintScheduler.from_pretrained(snake_case						)

											snake_case_			:str						=      RePaintPipeline(unet=snake_case						,					scheduler=snake_case						).to(snake_case						)
											repaint.set_progress_bar_config(disable=snake_case						)
											repaint.enable_attention_slicing()

											snake_case_			:int						=      torch.manual_seed(0						)
											snake_case_			:str						=      repaint(
											    snake_case						,					snake_case						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=snake_case						,					output_type="""np"""						,					)
											snake_case_			:List[str]						=      output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image						).mean() < 1E-2
