






'''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		:		Union[str, Any]								=			logging.get_logger(__name__)

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

if is_torch_available():
						import torch

if is_soundfile_availble():
						import soundfile as sf


class 				snake_case__     :




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




					def __str__(       self       :						List[Any]     )  ->    Optional[int]:
								return self.to_string()




					def 						A     (       self       :						Union[str, Any]     )  ->    str:
								logger.error(
								    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''     )
								return self._value





					def 						A     (       self       :						str     )  ->    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 				snake_case__     (			UpperCamelCase      ,							UpperCamelCase):




					def 						A     (       self       :						int     )  ->    Dict:
								return self._value





					def 						A     (       self       :						Dict     )  ->    Tuple:
								return self._value






class 				snake_case__     (			UpperCamelCase      ,							UpperCamelCase):




					def __init__(       self       :						int							,				_A       :						str     )  ->    Dict:
								super().__init__(_A     )

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

								UpperCAmelCase_     :   Tuple       =   None
								UpperCAmelCase_     :   List[Any]       =   None
								UpperCAmelCase_     :   Dict       =   None

								if isinstance(_A							,				_A     ):
											UpperCAmelCase_     :   str       =   value
								elif isinstance(_A							,				(str, pathlib.Path)     ):
											UpperCAmelCase_     :   str       =   value
								elif isinstance(_A							,				torch.Tensor     ):
											UpperCAmelCase_     :   Dict       =   value
								else:
											raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(_A     )}"     )




					def 						A     (       self       :						Dict							,				_A       :						Tuple=None							,				_A       :						Tuple=None     )  ->    Dict:
								from IPython.display import Image, display

								display(Image(self.to_string()     )     )




					def 						A     (       self       :						Union[str, Any]     )  ->    Optional[Any]:
								if self._raw is not None:
											return self._raw

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





					def 						A     (       self       :						str     )  ->    Tuple:
								if self._path is not None:
											return self._path

								if self._raw is not None:
											UpperCAmelCase_     :   int       =   tempfile.mkdtemp()
											UpperCAmelCase_     :   List[str]       =   os.path.join(_A							,				str(uuid.uuida()     ) + '''.png'''     )
											self._raw.save(self._path     )

											return self._path

								if self._tensor is not None:
											UpperCAmelCase_     :   Optional[int]       =   self._tensor.cpu().detach().numpy()

											# There is likely simpler than load into image into save
											UpperCAmelCase_     :   Union[str, Any]       =   Image.fromarray((array * 2_55).astype(np.uinta     )     )

											UpperCAmelCase_     :   Dict       =   tempfile.mkdtemp()
											UpperCAmelCase_     :   Any       =   os.path.join(_A							,				str(uuid.uuida()     ) + '''.png'''     )

											img.save(self._path     )

											return self._path






class 				snake_case__     (			UpperCamelCase):




					def __init__(       self       :						Tuple							,				_A       :						List[str]							,				_A       :						List[str]=1_60_00     )  ->    List[str]:
								super().__init__(_A     )

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

								UpperCAmelCase_     :   Optional[int]       =   None
								UpperCAmelCase_     :   Optional[Any]       =   None

								UpperCAmelCase_     :   Any       =   samplerate

								if isinstance(_A							,				(str, pathlib.Path)     ):
											UpperCAmelCase_     :   int       =   value
								elif isinstance(_A							,				torch.Tensor     ):
											UpperCAmelCase_     :   Optional[Any]       =   value
								else:
											raise ValueError(F"Unsupported audio type: {type(_A     )}"     )




					def 						A     (       self       :						List[Any]							,				_A       :						Union[str, Any]=None							,				_A       :						Optional[int]=None     )  ->    Optional[Any]:
								from IPython.display import Audio, display

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




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

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





					def 						A     (       self       :						int     )  ->    Tuple:
								if self._path is not None:
											return self._path

								if self._tensor is not None:
											UpperCAmelCase_     :   Optional[int]       =   tempfile.mkdtemp()
											UpperCAmelCase_     :   int       =   os.path.join(_A							,				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		:		int								=			{str: AgentText}

if is_vision_available():
						_UpperCamelCase		:		List[str]								=			AgentImage
def __UpperCAmelCase (			*A			:	List[Any]	,   **A			:	Tuple						)  -> Dict:
			UpperCAmelCase_     :   List[str]       =   [(arg.to_raw() if isinstance(A	,   A						) else arg) for arg in args]
			UpperCAmelCase_     :   Dict       =   {k: (v.to_raw() if isinstance(A	,   A						) else v) for k, v in kwargs.items()}
			return args, kwargs







def __UpperCAmelCase (			A			:	Any	,   A			:	Dict=None						)  -> Union[str, Any]:
			if isinstance(A	,   A						):
						UpperCAmelCase_     :   Union[str, Any]       =   {}
						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:
															UpperCAmelCase_     :   Optional[Any]       =   AGENT_TYPE_MAPPING[output_types[i]](A						)
												else:
															UpperCAmelCase_     :   List[Any]       =   AgentType(A						)

									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(A	,   _k						):
																		UpperCAmelCase_     :   Any       =   _v(A						)
												if k not in decoded_outputs:
															UpperCAmelCase_     :   List[Any]       =   AgentType[v]

			elif isinstance(A	,   (list, tuple)						):
						UpperCAmelCase_     :   int       =   type(A						)()
						for i, v in enumerate(A						):
									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]](A						)						)
												else:
															decoded_outputs.append(AgentType(A						)						)
									else:
												# If the class does not have defined output, then we map according to the type
												UpperCAmelCase_     :   List[str]       =   False
												for _k, _v in INSTANCE_TYPE_MAPPING.items():
															if isinstance(A	,   _k						):
																		decoded_outputs.append(_v(A						)						)
																		UpperCAmelCase_     :   Any       =   True

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

			else:
						if output_types[0] in AGENT_TYPE_MAPPING:
									# If the class has defined outputs, we can map directly according to the class definition
									UpperCAmelCase_     :   Optional[Any]       =   AGENT_TYPE_MAPPING[output_types[0]](A						)

						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(A	,   _k						):
															return _v(A						)
									return AgentType(A						)

			return decoded_outputs














'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference


@slow
@require_torch_gpu
class 				snake_case__     (			unittest.TestCase):




					def 						A     (       self       :						Optional[Any]     )  ->    List[str]:
								UpperCAmelCase_     :   Optional[Any]       =   '''runwayml/stable-diffusion-v1-5'''
								UpperCAmelCase_     :   Optional[int]       =   TextToVideoZeroPipeline.from_pretrained(_A							,				torch_dtype=torch.floataa     ).to('''cuda'''     )
								UpperCAmelCase_     :   Tuple       =   DDIMScheduler.from_config(pipe.scheduler.config     )
								UpperCAmelCase_     :   Tuple       =   torch.Generator(device='''cuda'''     ).manual_seed(0     )

								UpperCAmelCase_     :   List[Any]       =   '''A bear is playing a guitar on Times Square'''
								UpperCAmelCase_     :   List[Any]       =   pipe(prompt=_A							,				generator=_A     ).images

								UpperCAmelCase_     :   Any       =   load_pt(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'''     )

								assert_mean_pixel_difference(_A							,				_A     )


