
"""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


_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        _lowercase   :




	def __init__( self		, UpperCAmelCase_					)		->							str:
			lowerCamelCase     :			Tuple		=    value




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




	def 			_UpperCamelCase    ( self					)		->							str:
			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					)		->							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        _lowercase   (				__UpperCAmelCase				,       __UpperCAmelCase		):




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			return self._value




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			return self._value






class        _lowercase   (				__UpperCAmelCase				,       __UpperCAmelCase		):




	def __init__( self		, UpperCAmelCase_					)		->							Dict:
			super().__init__(UpperCAmelCase_					)

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

			lowerCamelCase     :			int		=    None
			lowerCamelCase     :			Optional[Any]		=    None
			lowerCamelCase     :			Dict		=    None

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




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=None		, UpperCAmelCase_=None					)		->							Optional[int]:
			from IPython.display import Image, display

			display(Image(self.to_string()					)					)




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

			if self._path is not None:
					lowerCamelCase     :			int		=    Image.open(self._path					)
					return self._raw




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			if self._path is not None:
					return self._path

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

					return self._path

			if self._tensor is not None:
					lowerCamelCase     :			Union[str, Any]		=    self._tensor.cpu().detach().numpy()

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

					lowerCamelCase     :			Tuple		=    tempfile.mkdtemp()
					lowerCamelCase     :			Union[str, Any]		=    os.path.join(UpperCAmelCase_		, str(uuid.uuida()					) + '.png'					)

					img.save(self._path					)

					return self._path






class        _lowercase   (				__UpperCAmelCase		):




	def __init__( self		, UpperCAmelCase_		, UpperCAmelCase_=16000					)		->							Optional[Any]:
			super().__init__(UpperCAmelCase_					)

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

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

			lowerCamelCase     :			List[Any]		=    samplerate

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




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=None		, UpperCAmelCase_=None					)		->							Optional[Any]:
			from IPython.display import Audio, display

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




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			if self._tensor is not None:
					return self._tensor

			if self._path is not None:
					lowerCamelCase	,    lowerCamelCase     :			str		=    sf.read(self._path					)
					lowerCamelCase     :			Tuple		=    torch.tensor(UpperCAmelCase_					)
					return self._tensor




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			if self._path is not None:
					return self._path

			if self._tensor is not None:
					lowerCamelCase     :			Union[str, Any]		=    tempfile.mkdtemp()
					lowerCamelCase     :			List[Any]		=    os.path.join(UpperCAmelCase_		, 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 		UpperCAmelCase					(		*a_,						**a_			):





		'''simple docstring'''


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





def 		UpperCAmelCase					(		a_,						a_=None			):





		'''simple docstring'''


		if isinstance(a_,						a_			):
				lowerCamelCase     :			List[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:
										lowerCamelCase     :			Tuple		=    AGENT_TYPE_MAPPING[output_types[i]](a_			)
								else:
										lowerCamelCase     :			int		=    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			):
												lowerCamelCase     :			Optional[Any]		=    _v(a_			)
								if k not in decoded_outputs:
										lowerCamelCase     :			Dict		=    AgentType[v]

		elif isinstance(a_,						(list, tuple)			):
				lowerCamelCase     :			Optional[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
								lowerCamelCase     :			Dict		=    False
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(a_,						_k			):
												decoded_outputs.append(_v(a_			)			)
												lowerCamelCase     :			Tuple		=    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
						lowerCamelCase     :			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 torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	(KDPMaAncestralDiscreteScheduler,)
	lowercase_        =	1_0




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Optional[int]:
			lowerCamelCase     :			Any		=    {
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			}

			config.update(**UpperCAmelCase_					)
			return config




	def 			_UpperCamelCase    ( self					)		->							Any:
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		, [0.0002, 0.002, 0.02]					):
					self.check_over_configs(beta_start=UpperCAmelCase_		, beta_end=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Any		=    self.scheduler_classes[0]
			lowerCamelCase     :			List[str]		=    self.get_scheduler_config()
			lowerCamelCase     :			Union[str, Any]		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			Optional[Any]		=    torch.manual_seed(0					)

			lowerCamelCase     :			int		=    self.dummy_model()
			lowerCamelCase     :			Tuple		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			Union[str, Any]		=    sample.to(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Tuple		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			str		=    output.prev_sample

			lowerCamelCase     :			Optional[int]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3877					) < 1E-2
			assert abs(result_mean.item() - 18.0331					) < 5E-3




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Dict		=    self.scheduler_classes[0]
			lowerCamelCase     :			Union[str, Any]		=    self.get_scheduler_config(prediction_type='v_prediction'					)
			lowerCamelCase     :			int		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			int		=    self.dummy_model()
			lowerCamelCase     :			Dict		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			Optional[int]		=    sample.to(UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Dict		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Optional[Any]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			Optional[Any]		=    output.prev_sample

			lowerCamelCase     :			Union[str, Any]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Optional[Any]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 328.9970					) < 1E-2
			assert abs(result_mean.item() - 0.4284					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Any:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Union[str, Any]		=    self.scheduler_classes[0]
			lowerCamelCase     :			Dict		=    self.get_scheduler_config()
			lowerCamelCase     :			Dict		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps		, device=UpperCAmelCase_					)
			lowerCamelCase     :			int		=    torch.manual_seed(0					)

			lowerCamelCase     :			List[Any]		=    self.dummy_model()
			lowerCamelCase     :			Union[str, Any]		=    self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
					lowerCamelCase     :			Optional[int]		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			str		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			Dict		=    output.prev_sample

			lowerCamelCase     :			List[str]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			str		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3818					) < 1E-1
			assert abs(result_mean.item() - 18.0331					) < 1E-3






