




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE__ 						="""scheduler_config.json"""






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					1
				SCREAMING_SNAKE_CASE__					:							int	      =					2
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					3
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					4
				SCREAMING_SNAKE_CASE__					:							Any	      =					5






@dataclass
class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							jnp.ndarray






class  __magic_name__						:
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					SCHEDULER_CONFIG_NAME
				SCREAMING_SNAKE_CASE__					:							str	      =					["dtype"]
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					[]
				SCREAMING_SNAKE_CASE__					:							Dict	      =					True


				@classmethod
				def     _A							(							cls:   Any		,					_lowerCamelCase:   Dict[str, Any] = None		,					_lowerCamelCase:   Optional[str] = None		,					_lowerCamelCase:   List[str]=False		,					**_lowerCamelCase:   int		,					):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    cls.load_config(
											    pretrained_model_name_or_path=_lowerCamelCase		,					subfolder=_lowerCamelCase		,					return_unused_kwargs=_lowerCamelCase		,					**_lowerCamelCase		,					)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    cls.from_config(_lowerCamelCase		,					return_unused_kwargs=_lowerCamelCase		,					**_lowerCamelCase		)

											if hasattr(_lowerCamelCase		,					'''create_state'''		) and getattr(_lowerCamelCase		,					'''has_state'''		,					_lowerCamelCase		):
																		SCREAMING_SNAKE_CASE_   	=    scheduler.create_state()

											if return_unused_kwargs:
																		return scheduler, state, unused_kwargs

											return scheduler, state


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   Union[str, os.PathLike]		,					_lowerCamelCase:   bool = False		,					**_lowerCamelCase:   int		):
											self.save_config(save_directory=_lowerCamelCase		,					push_to_hub=_lowerCamelCase		,					**_lowerCamelCase		)


				@property
				def     _A							(							self:   Union[str, Any]		):
											return self._get_compatibles()





				@classmethod
				def     _A							(							cls:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    list(set([cls.__name__] + cls._compatibles		)		)
											SCREAMING_SNAKE_CASE_   	=    importlib.import_module(__name__.split('''.'''		)[0]		)
											SCREAMING_SNAKE_CASE_   	=    [
											    getattr(_lowerCamelCase		,					_lowerCamelCase		) for c in compatible_classes_str if hasattr(_lowerCamelCase		,					_lowerCamelCase		)
											]
											return compatible_classes






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							assert len(_lowerCAmelCase     ) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowerCAmelCase     ) - x.ndim)     )  ,    _lowerCAmelCase     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=0.999  ,    _lowerCAmelCase=jnp.floataa     ):

							def alpha_bar(_lowerCAmelCase     ):
														return math.cos((time_step + 0.008) / 1.008 * math.pi / 2     ) ** 2

							SCREAMING_SNAKE_CASE_   	=    []
							for i in range(_lowerCAmelCase     ):
														SCREAMING_SNAKE_CASE_   	=    i / num_diffusion_timesteps
														SCREAMING_SNAKE_CASE_   	=    (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(_lowerCAmelCase     ) / alpha_bar(_lowerCAmelCase     )  ,    _lowerCAmelCase     )     )
							return jnp.array(_lowerCAmelCase  ,    dtype=_lowerCAmelCase     )






@flax.struct.dataclass
class  __magic_name__						:
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							jnp.ndarray
				SCREAMING_SNAKE_CASE__					:							jnp.ndarray
				SCREAMING_SNAKE_CASE__					:							jnp.ndarray


				@classmethod
				def     _A							(							cls:   str		,					_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    scheduler.config

											if config.trained_betas is not None:
																		SCREAMING_SNAKE_CASE_   	=    jnp.asarray(config.trained_betas		,					dtype=scheduler.dtype		)
											elif config.beta_schedule == "linear":
																		SCREAMING_SNAKE_CASE_   	=    jnp.linspace(config.beta_start		,					config.beta_end		,					config.num_train_timesteps		,					dtype=scheduler.dtype		)
											elif config.beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		SCREAMING_SNAKE_CASE_   	=    (
																		    jnp.linspace(
																		        config.beta_start**0.5		,					config.beta_end**0.5		,					config.num_train_timesteps		,					dtype=scheduler.dtype		)
																		    ** 2
																		)
											elif config.beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		SCREAMING_SNAKE_CASE_   	=    betas_for_alpha_bar(config.num_train_timesteps		,					dtype=scheduler.dtype		)
											else:
																		raise NotImplementedError(
																		    f"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"		)

											SCREAMING_SNAKE_CASE_   	=    1.0 - betas

											SCREAMING_SNAKE_CASE_   	=    jnp.cumprod(_lowerCamelCase		,					axis=0		)

											return cls(
											    alphas=_lowerCamelCase		,					betas=_lowerCamelCase		,					alphas_cumprod=_lowerCamelCase		,					)






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    state.alphas_cumprod

							SCREAMING_SNAKE_CASE_   	=    alphas_cumprod[timesteps] ** 0.5
							SCREAMING_SNAKE_CASE_   	=    sqrt_alpha_prod.flatten()
							SCREAMING_SNAKE_CASE_   	=    broadcast_to_shape_from_left(_lowerCAmelCase  ,    original_samples.shape     )

							SCREAMING_SNAKE_CASE_   	=    (1 - alphas_cumprod[timesteps]) ** 0.5
							SCREAMING_SNAKE_CASE_   	=    sqrt_one_minus_alpha_prod.flatten()
							SCREAMING_SNAKE_CASE_   	=    broadcast_to_shape_from_left(_lowerCAmelCase  ,    original_samples.shape     )

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_sqrt_alpha_prod(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_sqrt_alpha_prod(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
			import torch






class  __magic_name__						(  TensorFormatter[Mapping, "torch.Tensor", Mapping]):
				'''simple docstring'''







				def __init__(							self:   int		,					_lowerCamelCase:   int=None		,					**_lowerCamelCase:   List[Any]		):
											super().__init__(features=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch_tensor_kwargs
											import torch  # noqa import torch at initialization


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											import torch

											if isinstance(_lowerCamelCase		,					_lowerCamelCase		) and column:
																		if all(
																		    isinstance(_lowerCamelCase		,					torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
																		    for x in column		):
																									return torch.stack(_lowerCamelCase		)
											return column


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   List[str]		):
											import torch

											if isinstance(_lowerCamelCase		,					(str, bytes, type(_lowerCamelCase		))		):
																		return value
											elif isinstance(_lowerCamelCase		,					(np.character, np.ndarray)		) and np.issubdtype(value.dtype		,					np.character		):
																		return value.tolist()

											SCREAMING_SNAKE_CASE_   	=    {}

											if isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.integer		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.intaa}
											elif isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.floating		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																		import PIL.Image

																		if isinstance(_lowerCamelCase		,					PIL.Image.Image		):
																									SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
											return torch.tensor(_lowerCamelCase		,					**{**default_dtype, **self.torch_tensor_kwargs}		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		):
											import torch

											# support for torch, tf, jax etc.
											if hasattr(_lowerCamelCase		,					'''__array__'''		) and not isinstance(_lowerCamelCase		,					torch.Tensor		):
																		SCREAMING_SNAKE_CASE_   	=    data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_lowerCamelCase		,					np.ndarray		):
																		if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																									return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											elif isinstance(_lowerCamelCase		,					(list, tuple)		):
																		return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											return self._tensorize(_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   dict		):
											return map_nested(self._recursive_tensorize		,					_lowerCamelCase		,					map_list=_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_row(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_row(_lowerCamelCase		)
											return self.recursive_tensorize(_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_column(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_column(_lowerCamelCase		,					pa_table.column_names[0]		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self._consolidate(_lowerCamelCase		)
											return column





				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											for column_name in batch:
																		SCREAMING_SNAKE_CASE_   	=    self._consolidate(batch[column_name]		)
											return batch


