



'''simple docstring'''



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):
					a      :     List[str]        =							1



					@register_to_config
					def __init__(    self			,				UpperCamelCase=2000			,				UpperCamelCase=0.1			,				UpperCamelCase=20			,				UpperCamelCase=1E-3     )     ->			Any:
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  None



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			Optional[int]:
						__lowerCAmelCase												=  torch.linspace(1			,				self.config.sampling_eps			,				UpperCamelCase			,				device=UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase=None     )     ->			Tuple:
						if self.timesteps is None:
							raise ValueError(
							    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"     )

						# TODO(Patrick) better comments + non-PyTorch
						# postprocess model score
						__lowerCAmelCase												=  (
						    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
						)
						__lowerCAmelCase												=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
						__lowerCAmelCase												=  std.flatten()
						while len(std.shape     ) < len(score.shape     ):
							__lowerCAmelCase												=  std.unsqueeze(-1     )
						__lowerCAmelCase												=  -score / std

						# compute
						__lowerCAmelCase												=  -1.0 / len(self.timesteps     )

						__lowerCAmelCase												=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
						__lowerCAmelCase												=  beta_t.flatten()
						while len(beta_t.shape     ) < len(x.shape     ):
							__lowerCAmelCase												=  beta_t.unsqueeze(-1     )
						__lowerCAmelCase												=  -0.5 * beta_t * x

						__lowerCAmelCase												=  torch.sqrt(UpperCamelCase     )
						__lowerCAmelCase												=  drift - diffusion**2 * score
						__lowerCAmelCase												=  x + drift * dt

						# add noise
						__lowerCAmelCase												=  randn_tensor(x.shape			,				layout=x.layout			,				generator=UpperCamelCase			,				device=x.device			,				dtype=x.dtype     )
						__lowerCAmelCase												=  x_mean + diffusion * math.sqrt(-dt     ) * noise

						return x, x_mean



					def __len__(    self     )     ->			List[Any]:
						return self.config.num_train_timesteps




'''simple docstring'''



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


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

lowerCAmelCase	:		Dict[Optional[str], Type[Formatter]]				=   {}
lowerCAmelCase	:		Dict[Optional[str], str]				=   {}
lowerCAmelCase	:		Dict[Optional[str], Exception]				=   {}



def   __lowerCAmelCase					(							lowerCamelCase   :						type				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''					)
	__lowerCAmelCase												=  formatter_cls
	for alias in set(aliases + [format_type]					):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''					)
		__lowerCAmelCase												=  format_type



def   __lowerCAmelCase					(							lowerCamelCase   :						Exception				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None					):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	for alias in set(aliases + [format_type]					):
		__lowerCAmelCase												=  unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	lowerCAmelCase	:		Optional[int]				=   ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	lowerCAmelCase	:		str				=   ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	lowerCAmelCase	:		Any				=   ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]					):






	'''simple docstring'''




	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type




def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]				,		**lowerCamelCase   :						Tuple					):






	'''simple docstring'''




	__lowerCAmelCase												=  get_format_type_from_alias(lowerCamelCase					)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**lowerCamelCase					)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None					)}, but got \'{format_type}\''''					)