import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp







def     A__	(       __A ,			__A ,			__A ,			__A ,			__A = 4_096						):







    '''simple docstring'''
    _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		Union[str, Any]			    =    key.shape[-3:]
    _lowerCamelCase  :		Tuple			    =    value.shape[-1]
    _lowerCamelCase  :		Any			    =    min(__A ,			__A						)
    _lowerCamelCase  :		Union[str, Any]			    =    query / jnp.sqrt(__A						)

    @functools.partial(jax.checkpoint ,			prevent_cse=__A						)
    def summarize_chunk(__A ,			__A ,			__A						):
        _lowerCamelCase  :		Any			    =    jnp.einsum("""...qhd,...khd->...qhk""" ,			__A ,			__A ,			precision=__A						)

        _lowerCamelCase  :		Any			    =    jnp.max(__A ,			axis=-1 ,			keepdims=__A						)
        _lowerCamelCase  :		int			    =    jax.lax.stop_gradient(__A						)
        _lowerCamelCase  :		Union[str, Any]			    =    jnp.exp(attn_weights - max_score						)

        _lowerCamelCase  :		Any			    =    jnp.einsum("""...vhf,...qhv->...qhf""" ,			__A ,			__A ,			precision=__A						)
        _lowerCamelCase  :		Tuple			    =    jnp.einsum("""...qhk->...qh""" ,			__A						)

        return (exp_values, exp_weights.sum(axis=-1						), max_score)

    def chunk_scanner(__A						):
        # julienne key array
        _lowerCamelCase  :		Tuple			    =    jax.lax.dynamic_slice(
            operand=__A ,			start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0] ,			slice_sizes=list(key.shape[:-3]						) + [key_chunk_size, num_heads, k_features] ,			)

        # julienne value array
        _lowerCamelCase  :		Dict			    =    jax.lax.dynamic_slice(
            operand=__A ,			start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0] ,			slice_sizes=list(value.shape[:-3]						) + [key_chunk_size, num_heads, v_features] ,			)

        return summarize_chunk(__A ,			__A ,			__A						)

    _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		List[str]			    =    jax.lax.map(f=__A ,			xs=jnp.arange(0 ,			__A ,			__A						)						)

    _lowerCamelCase  :		Tuple			    =    jnp.max(__A ,			axis=0 ,			keepdims=__A						)
    _lowerCamelCase  :		List[str]			    =    jnp.exp(chunk_max - global_max						)

    chunk_values *= jnp.expand_dims(__A ,			axis=-1						)
    chunk_weights *= max_diffs

    _lowerCamelCase  :		Union[str, Any]			    =    chunk_values.sum(axis=0						)
    _lowerCamelCase  :		Dict			    =    jnp.expand_dims(__A ,			-1						).sum(axis=0						)

    return all_values / all_weights







def     A__	(       __A ,			__A ,			__A ,			__A=jax.lax.Precision.HIGHEST ,			__A = 1_024 ,			__A = 4_096						):







    '''simple docstring'''
    _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		Tuple			    =    query.shape[-3:]

    def chunk_scanner(__A ,			__A						):
        # julienne query array
        _lowerCamelCase  :		Optional[Any]			    =    jax.lax.dynamic_slice(
            operand=__A ,			start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0] ,			slice_sizes=list(query.shape[:-3]						) + [min(__A ,			__A						), num_heads, q_features] ,			)

        return (
            chunk_idx + query_chunk_size,  # unused ignore it
            _query_chunk_attention(
                query=__A ,			key=__A ,			value=__A ,			precision=__A ,			key_chunk_size=__A						),
        )

    _lowerCamelCase    ,				_lowerCamelCase  :		str			    =    jax.lax.scan(
        f=__A ,			init=0 ,			xs=__A ,			length=math.ceil(num_q / query_chunk_size						)						)

    return jnp.concatenate(__A ,			axis=-3						)  # fuse the chunked result back




class 						__snake_case						(			nn.Module		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							8
      _snake_case     		=							64
      _snake_case     		=							0.0
      _snake_case     		=							False
      _snake_case     		=							jnp.floataa



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->str:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.dim_head * self.heads
          _lowerCamelCase  :		Any			    =    self.dim_head**-0.5

          # Weights were exported with old names {to_q, to_k, to_v, to_out}
          _lowerCamelCase  :		Tuple			    =    nn.Dense(_UpperCamelCase     ,	use_bias=_UpperCamelCase     ,	dtype=self.dtype     ,	name="""to_q""")
          _lowerCamelCase  :		Any			    =    nn.Dense(_UpperCamelCase     ,	use_bias=_UpperCamelCase     ,	dtype=self.dtype     ,	name="""to_k""")
          _lowerCamelCase  :		Optional[int]			    =    nn.Dense(_UpperCamelCase     ,	use_bias=_UpperCamelCase     ,	dtype=self.dtype     ,	name="""to_v""")

          _lowerCamelCase  :		Any			    =    nn.Dense(self.query_dim     ,	dtype=self.dtype     ,	name="""to_out_0""")
          _lowerCamelCase  :		Optional[Any]			    =    nn.Dropout(rate=self.dropout)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					Union[str, Any])					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		Optional[Any]			    =    tensor.shape
          _lowerCamelCase  :		Tuple			    =    self.heads
          _lowerCamelCase  :		Any			    =    tensor.reshape(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	dim // head_size)
          _lowerCamelCase  :		int			    =    jnp.transpose(_UpperCamelCase     ,	(0, 2, 1, 3))
          _lowerCamelCase  :		str			    =    tensor.reshape(batch_size * head_size     ,	_UpperCamelCase     ,	dim // head_size)
          return tensor



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any]     ,	_UpperCamelCase  :					Dict)					->str:


          """simple docstring"""


          _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		int			    =    tensor.shape
          _lowerCamelCase  :		List[str]			    =    self.heads
          _lowerCamelCase  :		str			    =    tensor.reshape(batch_size // head_size     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)
          _lowerCamelCase  :		Dict			    =    jnp.transpose(_UpperCamelCase     ,	(0, 2, 1, 3))
          _lowerCamelCase  :		List[str]			    =    tensor.reshape(batch_size // head_size     ,	_UpperCamelCase     ,	dim * head_size)
          return tensor




      def __call__(       self  :					Optional[int]     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Any=None     ,	_UpperCamelCase  :					Dict=True)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    hidden_states if context is None else context

          _lowerCamelCase  :		Any			    =    self.query(_UpperCamelCase)
          _lowerCamelCase  :		int			    =    self.key(_UpperCamelCase)
          _lowerCamelCase  :		Any			    =    self.value(_UpperCamelCase)

          _lowerCamelCase  :		Tuple			    =    self.reshape_heads_to_batch_dim(_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    self.reshape_heads_to_batch_dim(_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    self.reshape_heads_to_batch_dim(_UpperCamelCase)

          if self.use_memory_efficient_attention:
              _lowerCamelCase  :		Union[str, Any]			    =    query_states.transpose(1     ,	0     ,	2)
              _lowerCamelCase  :		int			    =    key_states.transpose(1     ,	0     ,	2)
              _lowerCamelCase  :		Optional[Any]			    =    value_states.transpose(1     ,	0     ,	2)

              # this if statement create a chunk size for each layer of the unet
              # the chunk size is equal to the query_length dimension of the deepest layer of the unet

              _lowerCamelCase  :		List[Any]			    =    query_states.shape[-3]
              if flatten_latent_dim % 64 == 0:
                  _lowerCamelCase  :		List[str]			    =    int(flatten_latent_dim / 64)
              elif flatten_latent_dim % 16 == 0:
                  _lowerCamelCase  :		Union[str, Any]			    =    int(flatten_latent_dim / 16)
              elif flatten_latent_dim % 4 == 0:
                  _lowerCamelCase  :		str			    =    int(flatten_latent_dim / 4)
              else:
                  _lowerCamelCase  :		Dict			    =    int(_UpperCamelCase)

              _lowerCamelCase  :		Optional[int]			    =    jax_memory_efficient_attention(
                  _UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	query_chunk_size=_UpperCamelCase     ,	key_chunk_size=4096 * 4)

              _lowerCamelCase  :		List[Any]			    =    hidden_states.transpose(1     ,	0     ,	2)
          else:
              # compute attentions
              _lowerCamelCase  :		int			    =    jnp.einsum("""b i d, b j d->b i j"""     ,	_UpperCamelCase     ,	_UpperCamelCase)
              _lowerCamelCase  :		List[Any]			    =    attention_scores * self.scale
              _lowerCamelCase  :		Dict			    =    nn.softmax(_UpperCamelCase     ,	axis=2)

              # attend to values
              _lowerCamelCase  :		Dict			    =    jnp.einsum("""b i j, b j d -> b i d"""     ,	_UpperCamelCase     ,	_UpperCamelCase)

          _lowerCamelCase  :		int			    =    self.reshape_batch_dim_to_heads(_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    self.proj_attn(_UpperCamelCase)
          return self.dropout_layer(_UpperCamelCase     ,	deterministic=_UpperCamelCase)





class 						__snake_case						(			nn.Module		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							42
      _snake_case     		=							42
      _snake_case     		=							0.0
      _snake_case     		=							False
      _snake_case     		=							jnp.floataa
      _snake_case     		=							False



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    FlaxAttention(
              self.dim     ,	self.n_heads     ,	self.d_head     ,	self.dropout     ,	self.use_memory_efficient_attention     ,	dtype=self.dtype)
          # cross attention
          _lowerCamelCase  :		List[str]			    =    FlaxAttention(
              self.dim     ,	self.n_heads     ,	self.d_head     ,	self.dropout     ,	self.use_memory_efficient_attention     ,	dtype=self.dtype)
          _lowerCamelCase  :		Optional[int]			    =    FlaxFeedForward(dim=self.dim     ,	dropout=self.dropout     ,	dtype=self.dtype)
          _lowerCamelCase  :		Any			    =    nn.LayerNorm(epsilon=1E-5     ,	dtype=self.dtype)
          _lowerCamelCase  :		Dict			    =    nn.LayerNorm(epsilon=1E-5     ,	dtype=self.dtype)
          _lowerCamelCase  :		Optional[Any]			    =    nn.LayerNorm(epsilon=1E-5     ,	dtype=self.dtype)
          _lowerCamelCase  :		str			    =    nn.Dropout(rate=self.dropout)




      def __call__(       self  :					List[str]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Tuple=True)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    hidden_states
          if self.only_cross_attention:
              _lowerCamelCase  :		str			    =    self.attna(self.norma(_UpperCamelCase)     ,	_UpperCamelCase     ,	deterministic=_UpperCamelCase)
          else:
              _lowerCamelCase  :		str			    =    self.attna(self.norma(_UpperCamelCase)     ,	deterministic=_UpperCamelCase)
          _lowerCamelCase  :		Any			    =    hidden_states + residual

          # cross attention
          _lowerCamelCase  :		Dict			    =    hidden_states
          _lowerCamelCase  :		Tuple			    =    self.attna(self.norma(_UpperCamelCase)     ,	_UpperCamelCase     ,	deterministic=_UpperCamelCase)
          _lowerCamelCase  :		str			    =    hidden_states + residual

          # feed forward
          _lowerCamelCase  :		Optional[Any]			    =    hidden_states
          _lowerCamelCase  :		Union[str, Any]			    =    self.ff(self.norma(_UpperCamelCase)     ,	deterministic=_UpperCamelCase)
          _lowerCamelCase  :		int			    =    hidden_states + residual

          return self.dropout_layer(_UpperCamelCase     ,	deterministic=_UpperCamelCase)





class 						__snake_case						(			nn.Module		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							42
      _snake_case     		=							42
      _snake_case     		=							1
      _snake_case     		=							0.0
      _snake_case     		=							False
      _snake_case     		=							False
      _snake_case     		=							jnp.floataa
      _snake_case     		=							False



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Any:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    nn.GroupNorm(num_groups=32     ,	epsilon=1E-5)

          _lowerCamelCase  :		str			    =    self.n_heads * self.d_head
          if self.use_linear_projection:
              _lowerCamelCase  :		Union[str, Any]			    =    nn.Dense(_UpperCamelCase     ,	dtype=self.dtype)
          else:
              _lowerCamelCase  :		Optional[Any]			    =    nn.Conv(
                  _UpperCamelCase     ,	kernel_size=(1, 1)     ,	strides=(1, 1)     ,	padding="""VALID"""     ,	dtype=self.dtype     ,	)

          _lowerCamelCase  :		Dict			    =    [
              FlaxBasicTransformerBlock(
                  _UpperCamelCase     ,	self.n_heads     ,	self.d_head     ,	dropout=self.dropout     ,	only_cross_attention=self.only_cross_attention     ,	dtype=self.dtype     ,	use_memory_efficient_attention=self.use_memory_efficient_attention     ,	)
              for _ in range(self.depth)
          ]

          if self.use_linear_projection:
              _lowerCamelCase  :		Optional[Any]			    =    nn.Dense(_UpperCamelCase     ,	dtype=self.dtype)
          else:
              _lowerCamelCase  :		List[str]			    =    nn.Conv(
                  _UpperCamelCase     ,	kernel_size=(1, 1)     ,	strides=(1, 1)     ,	padding="""VALID"""     ,	dtype=self.dtype     ,	)

          _lowerCamelCase  :		Optional[Any]			    =    nn.Dropout(rate=self.dropout)




      def __call__(       self  :					Tuple     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					List[str]     ,	_UpperCamelCase  :					Any=True)					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		str			    =    hidden_states.shape
          _lowerCamelCase  :		List[str]			    =    hidden_states
          _lowerCamelCase  :		Optional[int]			    =    self.norm(_UpperCamelCase)
          if self.use_linear_projection:
              _lowerCamelCase  :		int			    =    hidden_states.reshape(_UpperCamelCase     ,	height * width     ,	_UpperCamelCase)
              _lowerCamelCase  :		List[str]			    =    self.proj_in(_UpperCamelCase)
          else:
              _lowerCamelCase  :		Optional[int]			    =    self.proj_in(_UpperCamelCase)
              _lowerCamelCase  :		Any			    =    hidden_states.reshape(_UpperCamelCase     ,	height * width     ,	_UpperCamelCase)

          for transformer_block in self.transformer_blocks:
              _lowerCamelCase  :		Optional[int]			    =    transformer_block(_UpperCamelCase     ,	_UpperCamelCase     ,	deterministic=_UpperCamelCase)

          if self.use_linear_projection:
              _lowerCamelCase  :		Optional[int]			    =    self.proj_out(_UpperCamelCase)
              _lowerCamelCase  :		Any			    =    hidden_states.reshape(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)
          else:
              _lowerCamelCase  :		str			    =    hidden_states.reshape(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)
              _lowerCamelCase  :		Tuple			    =    self.proj_out(_UpperCamelCase)

          _lowerCamelCase  :		List[str]			    =    hidden_states + residual
          return self.dropout_layer(_UpperCamelCase     ,	deterministic=_UpperCamelCase)





class 						__snake_case						(			nn.Module		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							0.0
      _snake_case     		=							jnp.floataa



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    FlaxGEGLU(self.dim     ,	self.dropout     ,	self.dtype)
          _lowerCamelCase  :		Optional[int]			    =    nn.Dense(self.dim     ,	dtype=self.dtype)




      def __call__(       self  :					Tuple     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					str=True)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    self.net_a(_UpperCamelCase     ,	deterministic=_UpperCamelCase)
          _lowerCamelCase  :		int			    =    self.net_a(_UpperCamelCase)
          return hidden_states





class 						__snake_case						(			nn.Module		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							0.0
      _snake_case     		=							jnp.floataa



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.dim * 4
          _lowerCamelCase  :		Any			    =    nn.Dense(inner_dim * 2     ,	dtype=self.dtype)
          _lowerCamelCase  :		Any			    =    nn.Dropout(rate=self.dropout)




      def __call__(       self  :					Any     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					str=True)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    self.proj(_UpperCamelCase)
          _lowerCamelCase    ,				_lowerCamelCase  :		List[Any]			    =    jnp.split(_UpperCamelCase     ,	2     ,	axis=2)
          return self.dropout_layer(hidden_linear * nn.gelu(_UpperCamelCase)     ,	deterministic=_UpperCamelCase)





import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


lowerCAmelCase      :				Dict     			=logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							None







def     A__	(       __A ,			__A=0.999 ,			__A="cosine" ,			):







    '''simple docstring'''
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__A						):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__A						):
            return math.exp(t * -12.0						)

    else:
        raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""						)

    _lowerCamelCase  :		int			    =    []
    for i in range(__A						):
        _lowerCamelCase  :		Optional[int]			    =    i / num_diffusion_timesteps
        _lowerCamelCase  :		int			    =    (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__A						) / alpha_bar_fn(__A						) ,			__A						)						)
    return torch.tensor(__A ,			dtype=torch.floataa						)




class 						__snake_case						(			__lowerCAmelCase			,	__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							[e.name for e in KarrasDiffusionSchedulers]
      _snake_case     		=							1



      @register_to_config
      def __init__(       self  :					Tuple     ,	_UpperCamelCase  :					int = 1000     ,	_UpperCamelCase  :					float = 0.0_0_0_1     ,	_UpperCamelCase  :					float = 0.0_2     ,	_UpperCamelCase  :					str = "linear"     ,	_UpperCamelCase  :					Optional[Union[np.ndarray, List[float]]] = None     ,	_UpperCamelCase  :					str = "epsilon"     ,	_UpperCamelCase  :					str = "linspace"     ,	_UpperCamelCase  :					int = 0     ,	)					->Optional[int]:


          """simple docstring"""


          if trained_betas is not None:
              _lowerCamelCase  :		List[str]			    =    torch.tensor(_UpperCamelCase     ,	dtype=torch.floataa)
          elif beta_schedule == "linear":
              _lowerCamelCase  :		List[str]			    =    torch.linspace(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	dtype=torch.floataa)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              _lowerCamelCase  :		int			    =    (
                  torch.linspace(beta_start**0.5     ,	beta_end**0.5     ,	_UpperCamelCase     ,	dtype=torch.floataa) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              _lowerCamelCase  :		Any			    =    betas_for_alpha_bar(_UpperCamelCase)
          else:
              raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

          _lowerCamelCase  :		Tuple			    =    1.0 - self.betas
          _lowerCamelCase  :		int			    =    torch.cumprod(self.alphas     ,	dim=0)

          _lowerCamelCase  :		Tuple			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		Dict			    =    np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
          _lowerCamelCase  :		Tuple			    =    torch.from_numpy(_UpperCamelCase)

          # setable values
          _lowerCamelCase  :		List[str]			    =    None
          _lowerCamelCase  :		int			    =    np.linspace(0     ,	num_train_timesteps - 1     ,	_UpperCamelCase     ,	dtype=_UpperCamelCase)[::-1].copy()
          _lowerCamelCase  :		Optional[Any]			    =    torch.from_numpy(_UpperCamelCase)
          _lowerCamelCase  :		Tuple			    =    False



      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Dict:


          """simple docstring"""


          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor])					->torch.FloatTensor:


          """simple docstring"""


          if isinstance(_UpperCamelCase     ,	torch.Tensor):
              _lowerCamelCase  :		Optional[int]			    =    timestep.to(self.timesteps.device)
          _lowerCamelCase  :		Tuple			    =    (self.timesteps == timestep).nonzero().item()
          _lowerCamelCase  :		str			    =    self.sigmas[step_index]
          _lowerCamelCase  :		List[str]			    =    sample / ((sigma**2 + 1) ** 0.5)
          _lowerCamelCase  :		str			    =    True
          return sample



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Union[str, torch.device] = None)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
              _lowerCamelCase  :		Any			    =    np.linspace(0     ,	self.config.num_train_timesteps - 1     ,	_UpperCamelCase     ,	dtype=_UpperCamelCase)[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              _lowerCamelCase  :		Tuple			    =    self.config.num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              _lowerCamelCase  :		List[Any]			    =    (np.arange(0     ,	_UpperCamelCase) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              _lowerCamelCase  :		List[str]			    =    self.config.num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              _lowerCamelCase  :		str			    =    (np.arange(self.config.num_train_timesteps     ,	0     ,	-step_ratio)).round().copy().astype(_UpperCamelCase)
              timesteps -= 1
          else:
              raise ValueError(
                  F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

          _lowerCamelCase  :		Tuple			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		Union[str, Any]			    =    np.interp(_UpperCamelCase     ,	np.arange(0     ,	len(_UpperCamelCase))     ,	_UpperCamelCase)
          _lowerCamelCase  :		Optional[int]			    =    np.concatenate([sigmas, [0.0]]).astype(np.floataa)
          _lowerCamelCase  :		Dict			    =    torch.from_numpy(_UpperCamelCase).to(device=_UpperCamelCase)
          if str(_UpperCamelCase).startswith("""mps"""):
              # mps does not support float64
              _lowerCamelCase  :		List[str]			    =    torch.from_numpy(_UpperCamelCase).to(_UpperCamelCase     ,	dtype=torch.floataa)
          else:
              _lowerCamelCase  :		Any			    =    torch.from_numpy(_UpperCamelCase).to(device=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Optional[torch.Generator] = None     ,	_UpperCamelCase  :					bool = True     ,	)					->Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:


          """simple docstring"""


          if (
              isinstance(_UpperCamelCase     ,	_UpperCamelCase)
              or isinstance(_UpperCamelCase     ,	torch.IntTensor)
              or isinstance(_UpperCamelCase     ,	torch.LongTensor)
          ):
              raise ValueError(
                  (
                      """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                      """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                      """ one of the `scheduler.timesteps` as a timestep."""
                  )     ,	)

          if not self.is_scale_input_called:
              logger.warning(
                  """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                  """See `StableDiffusionPipeline` for a usage example.""")

          if isinstance(_UpperCamelCase     ,	torch.Tensor):
              _lowerCamelCase  :		Optional[Any]			    =    timestep.to(self.timesteps.device)

          _lowerCamelCase  :		Optional[int]			    =    (self.timesteps == timestep).nonzero().item()
          _lowerCamelCase  :		Union[str, Any]			    =    self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              _lowerCamelCase  :		int			    =    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              _lowerCamelCase  :		Optional[Any]			    =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
              raise NotImplementedError("""prediction_type not implemented yet: sample""")
          else:
              raise ValueError(
                  F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

          _lowerCamelCase  :		Tuple			    =    self.sigmas[step_index]
          _lowerCamelCase  :		int			    =    self.sigmas[step_index + 1]
          _lowerCamelCase  :		Tuple			    =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          _lowerCamelCase  :		List[Any]			    =    (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          _lowerCamelCase  :		List[Any]			    =    (sample - pred_original_sample) / sigma

          _lowerCamelCase  :		Tuple			    =    sigma_down - sigma

          _lowerCamelCase  :		Optional[int]			    =    sample + derivative * dt

          _lowerCamelCase  :		int			    =    model_output.device
          _lowerCamelCase  :		List[Any]			    =    randn_tensor(model_output.shape     ,	dtype=model_output.dtype     ,	device=_UpperCamelCase     ,	generator=_UpperCamelCase)

          _lowerCamelCase  :		List[Any]			    =    prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=_UpperCamelCase     ,	pred_original_sample=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	)					->torch.FloatTensor:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.sigmas.to(device=original_samples.device     ,	dtype=original_samples.dtype)
          if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase):
              # mps does not support float64
              _lowerCamelCase  :		str			    =    self.timesteps.to(original_samples.device     ,	dtype=torch.floataa)
              _lowerCamelCase  :		Union[str, Any]			    =    timesteps.to(original_samples.device     ,	dtype=torch.floataa)
          else:
              _lowerCamelCase  :		Union[str, Any]			    =    self.timesteps.to(original_samples.device)
              _lowerCamelCase  :		List[str]			    =    timesteps.to(original_samples.device)

          _lowerCamelCase  :		Optional[int]			    =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          _lowerCamelCase  :		List[Any]			    =    sigmas[step_indices].flatten()
          while len(sigma.shape) < len(original_samples.shape):
              _lowerCamelCase  :		Optional[Any]			    =    sigma.unsqueeze(-1)

          _lowerCamelCase  :		List[str]			    =    original_samples + noise * sigma
          return noisy_samples




      def __len__(       self  :					Dict)					->str:


          """simple docstring"""


          return self.config.num_train_timesteps




