


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


snake_case			:      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:
							snake_case			:      List[Any] 							=   object

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf



class    __lowercase  :

							"""simple docstring"""


							def __init__(    self				, A_	)->	int:
											_SCREAMING_SNAKE_CASE      = value





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





							def 		__magic_name__			(    self	)->	Dict:
											logger.error(
											    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'	)
											return self._value





							def 		__magic_name__			(    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							):

							"""simple docstring"""


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





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											return self._value







class    __lowercase  (       UpperCamelCase      ,     UpperCamelCase							):

							"""simple docstring"""


							def __init__(    self				, A_	)->	Optional[Any]:
											super().__init__(A_	)

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

											_SCREAMING_SNAKE_CASE      = None
											_SCREAMING_SNAKE_CASE      = None
											_SCREAMING_SNAKE_CASE      = None

											if isinstance(A_				, A_	):
															_SCREAMING_SNAKE_CASE      = value
											elif isinstance(A_				, (str, pathlib.Path)	):
															_SCREAMING_SNAKE_CASE      = value
											elif isinstance(A_				, torch.Tensor	):
															_SCREAMING_SNAKE_CASE      = value
											else:
															raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(A_	)}'''	)





							def 		__magic_name__			(    self				, A_=None				, A_=None	)->	Optional[Any]:
											from IPython.display import Image, display

											display(Image(self.to_string()	)	)





							def 		__magic_name__			(    self	)->	Optional[int]:
											if self._raw is not None:
															return self._raw

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





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

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

															return self._path

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

															# There is likely simpler than load into image into save
															_SCREAMING_SNAKE_CASE      = Image.fromarray((array * 255).astype(np.uinta	)	)

															_SCREAMING_SNAKE_CASE      = tempfile.mkdtemp()
															_SCREAMING_SNAKE_CASE      = os.path.join(A_				, str(uuid.uuida()	) + '.png'	)

															img.save(self._path	)

															return self._path







class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


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

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

											_SCREAMING_SNAKE_CASE      = None
											_SCREAMING_SNAKE_CASE      = None

											_SCREAMING_SNAKE_CASE      = samplerate

											if isinstance(A_				, (str, pathlib.Path)	):
															_SCREAMING_SNAKE_CASE      = value
											elif isinstance(A_				, torch.Tensor	):
															_SCREAMING_SNAKE_CASE      = value
											else:
															raise ValueError(F'''Unsupported audio type: {type(A_	)}'''	)





							def 		__magic_name__			(    self				, A_=None				, A_=None	)->	List[Any]:
											from IPython.display import Audio, display

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





							def 		__magic_name__			(    self	)->	Dict:
											if self._tensor is not None:
															return self._tensor

											if self._path is not None:
															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = sf.read(self._path	)
															_SCREAMING_SNAKE_CASE      = torch.tensor(A_	)
															return self._tensor





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

											if self._tensor is not None:
															_SCREAMING_SNAKE_CASE      = tempfile.mkdtemp()
															_SCREAMING_SNAKE_CASE      = os.path.join(A_				, str(uuid.uuida()	) + '.wav'	)
															sf.write(self._path				, self._tensor				, samplerate=self.samplerate	)
															return self._path







snake_case			:      List[str] 							=   {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
snake_case			:      List[str] 							=   {str: AgentText}

if is_vision_available():
							snake_case			:      List[Any] 							=   AgentImage


def 				SCREAMING_SNAKE_CASE (     *UpperCAmelCase__   ,**UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = [(arg.to_raw() if isinstance(UpperCAmelCase__   ,UpperCAmelCase__							) else arg) for arg in args]
				_SCREAMING_SNAKE_CASE      = {k: (v.to_raw() if isinstance(UpperCAmelCase__   ,UpperCAmelCase__							) else v) for k, v in kwargs.items()}
				return args, kwargs



def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=None							):

				"""simple docstring"""






				if isinstance(UpperCAmelCase__   ,UpperCAmelCase__							):
								_SCREAMING_SNAKE_CASE      = {}
								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:
																				_SCREAMING_SNAKE_CASE      = AGENT_TYPE_MAPPING[output_types[i]](UpperCAmelCase__							)
																else:
																				_SCREAMING_SNAKE_CASE      = AgentType(UpperCAmelCase__							)

												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(UpperCAmelCase__   ,_k							):
																								_SCREAMING_SNAKE_CASE      = _v(UpperCAmelCase__							)
																if k not in decoded_outputs:
																				_SCREAMING_SNAKE_CASE      = AgentType[v]

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

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

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

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

				return decoded_outputs




import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()



class    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					str        =						"hf-internal-testing/tiny-stable-diffusion-pipe"
							SCREAMING_SNAKE_CASE   :					str        =						1
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						77
							SCREAMING_SNAKE_CASE   :					Dict        =						32
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						4
							SCREAMING_SNAKE_CASE   :					Optional[int]        =						64
							SCREAMING_SNAKE_CASE   :					Tuple        =						torch.manual_seed(0							)





							def 		__magic_name__			(    self				, A_=0.9999	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = UNetaDConditionModel.from_pretrained(self.model_id				, subfolder='unet'	)
											_SCREAMING_SNAKE_CASE      = unet.to(A_	)
											_SCREAMING_SNAKE_CASE      = EMAModel(unet.parameters()				, decay=A_				, model_cls=A_				, model_config=unet.config	)
											return unet, ema_unet





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.num_in_channels				, self.latent_height				, self.latent_width				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randint(0				, 1000				, size=(self.batch_size,)				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.prompt_length				, self.text_encoder_hidden_dim				, generator=self.generator	).to(A_	)
											return noisy_latents, timesteps, encoder_hidden_states





							def 		__magic_name__			(    self				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = {}
											for k, param in unet.state_dict().items():
															_SCREAMING_SNAKE_CASE      = torch.randn_like(A_	) + (param * torch.randn_like(A_	))
															updated_state_dict.update({k: updated_param}	)
											unet.load_state_dict(A_	)
											return unet





							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											# Take the first (hypothetical) EMA step.
											ema_unet.step(unet.parameters()	)
											assert ema_unet.optimization_step == 1

											# Take two more.
											for _ in range(2	):
															ema_unet.step(unet.parameters()	)
											assert ema_unet.optimization_step == 3





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											# Since the `unet` is not being updated (i.e., backprop'd)
											# there won't be any difference between the `params` of `unet`
											# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
											ema_unet.step(unet.parameters()	)
											_SCREAMING_SNAKE_CASE      = list(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert torch.allclose(A_				, A_	)

											# The above holds true even if we call `ema.step()` multiple times since
											# `unet` params are still not being updated.
											for _ in range(4	):
															ema_unet.step(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											# Here we simulate the parameter updates for `unet`. Since there might
											# be some parameters which are initialized to zero we take extra care to
											# initialize their values to something non-zero before the multiplication.
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)

											# Take the EMA step.
											ema_unet.step(unet_pseudo_updated_step_one.parameters()	)

											# Now the EMA'd parameters won't be equal to the original model parameters.
											_SCREAMING_SNAKE_CASE      = list(unet_pseudo_updated_step_one.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert ~torch.allclose(A_				, A_	)

											# Ensure this is the case when we take multiple EMA steps.
											for _ in range(4	):
															ema_unet.step(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert ~torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Optional[int]:
											# If we call EMA step after a backpropagation consecutively for two times,
											# the shadow params from those two steps should be different.
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()

											# First backprop + EMA
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_one.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											# Second backprop + EMA
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_two.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											for step_one, step_two in zip(A_				, A_	):
															assert ~torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Dict:
											# If there's no decay even if there are backprops, EMA steps
											# won't take any effect i.e., the shadow params would remain the
											# same.
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models(decay=0.0	)
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_one.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_two.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											for step_one, step_two in zip(A_				, A_	):
															assert torch.allclose(A_				, A_	)





							@skip_mps
							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_dummy_inputs()

											with tempfile.TemporaryDirectory() as tmpdir:
															ema_unet.save_pretrained(A_	)
															_SCREAMING_SNAKE_CASE      = UNetaDConditionModel.from_pretrained(A_				, model_cls=A_	)
															_SCREAMING_SNAKE_CASE      = loaded_unet.to(unet.device	)

											# Since no EMA step has been performed the outputs should match.
											_SCREAMING_SNAKE_CASE      = unet(A_				, A_				, A_	).sample
											_SCREAMING_SNAKE_CASE      = loaded_unet(A_				, A_				, A_	).sample

											assert torch.allclose(A_				, A_				, atol=1e-4	)
