
'''simple docstring'''
import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=42
 lowerCamelCase												=None







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	List[str]=0.999      ,  __SCREAMING_SNAKE_CASE    :	Tuple="cosine"      ,  )					->							str:





  """simple docstring"""

  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(__SCREAMING_SNAKE_CASE    :	List[Any]			):
      return math.exp(t * -12.0			)

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

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







class 			__A					(			_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=[e.name for e in KarrasDiffusionSchedulers]
 lowerCamelCase												=1
 @register_to_config
 def __init__( self      :     Union[str, Any]					, lowerCamelCase      :     int = 10_00					, lowerCamelCase      :     float = 0.0_001					, lowerCamelCase      :     float = 0.02					, lowerCamelCase      :     str = "linear"					, lowerCamelCase      :     Optional[Union[np.ndarray, List[float]]] = None					, lowerCamelCase      :     Optional[bool] = False					, lowerCamelCase      :     str = "epsilon"					, lowerCamelCase      :     str = "linspace"					, lowerCamelCase      :     int = 0					, ):



   """simple docstring"""





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

   __A		:  Dict       				=		1.0 - self.betas
   __A		:  Dict       				=		torch.cumprod(self.alphas					, dim=0							)

   __A		:  str       				=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
   __A		:  Optional[Any]       				=		np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
   __A		:  List[str]       				=		torch.from_numpy(lowerCamelCase							)

   # setable values
   __A		:  Tuple       				=		None
   __A		:  Tuple       				=		use_karras_sigmas
   self.set_timesteps(lowerCamelCase					, lowerCamelCase							)
   __A		:  int       				=		[]
   __A		:  str       				=		False
 @property
 def 		lowercase_( self      :     str							):



   """simple docstring"""





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

   return (self.sigmas.max() ** 2 + 1) ** 0.5
 def 		lowercase_( self      :     str					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     Union[float, torch.FloatTensor]							):



   """simple docstring"""





   if isinstance(lowerCamelCase					, torch.Tensor							):
     __A		:  List[Any]       				=		timestep.to(self.timesteps.device							)
   __A		:  Union[str, Any]       				=		(self.timesteps == timestep).nonzero().item()
   __A		:  str       				=		self.sigmas[step_index]
   __A		:  Any       				=		sample / ((sigma**2 + 1) ** 0.5)
   __A		:  Optional[int]       				=		True
   return sample
 def 		lowercase_( self      :     Union[str, Any]					, lowerCamelCase      :     Optional[Any]					, lowerCamelCase      :     int					, lowerCamelCase      :     List[str]							):



   """simple docstring"""





   def lms_derivative(lowerCamelCase      :     Union[str, Any]							):
     __A		:  Union[str, Any]       				=		1.0
     for k in range(lowerCamelCase							):
       if current_order == k:
         continue
       prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
     return prod

   __A		:  Optional[Any]       				=		integrate.quad(lowerCamelCase					, self.sigmas[t]					, self.sigmas[t + 1]					, epsrel=1e-4							)[0]

   return integrated_coeff
 def 		lowercase_( self      :     Union[str, Any]					, lowerCamelCase      :     int					, lowerCamelCase      :     Union[str, torch.device] = None							):



   """simple docstring"""





   __A		:  List[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":
     __A		:  str       				=		np.linspace(0					, self.config.num_train_timesteps - 1					, lowerCamelCase					, dtype=lowerCamelCase							)[
         ::-1
     ].copy()
   elif self.config.timestep_spacing == "leading":
     __A		:  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
     __A		:  Optional[int]       				=		(np.arange(0					, lowerCamelCase							) * step_ratio).round()[::-1].copy().astype(lowerCamelCase							)
     timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
     __A		:  Optional[Any]       				=		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
     __A		:  Optional[int]       				=		(np.arange(self.config.num_train_timesteps					, 0					, -step_ratio							)).round().copy().astype(lowerCamelCase							)
     timesteps -= 1
   else:
     raise ValueError(
         f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."							)

   __A		:  Dict       				=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
   __A		:  Union[str, Any]       				=		np.log(lowerCamelCase							)
   __A		:  Any       				=		np.interp(lowerCamelCase					, np.arange(0					, len(lowerCamelCase							)							)					, lowerCamelCase							)

   if self.use_karras_sigmas:
     __A		:  Union[str, Any]       				=		self._convert_to_karras(in_sigmas=lowerCamelCase							)
     __A		:  List[str]       				=		np.array([self._sigma_to_t(lowerCamelCase					, lowerCamelCase							) for sigma in sigmas]							)

   __A		:  Dict       				=		np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)

   __A		:  Any       				=		torch.from_numpy(lowerCamelCase							).to(device=lowerCamelCase							)
   if str(lowerCamelCase							).startswith("""mps"""							):
     # mps does not support float64
     __A		:  Tuple       				=		torch.from_numpy(lowerCamelCase							).to(lowerCamelCase					, dtype=torch.floataa							)
   else:
     __A		:  Dict       				=		torch.from_numpy(lowerCamelCase							).to(device=lowerCamelCase							)

   __A		:  Dict       				=		[]
 def 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     List[Any]					, lowerCamelCase      :     str							):



   """simple docstring"""





   __A		:  List[Any]       				=		np.log(lowerCamelCase							)

   # get distribution
   __A		:  Union[str, Any]       				=		log_sigma - log_sigmas[:, np.newaxis]

   # get sigmas range
   __A		:  str       				=		np.cumsum((dists >= 0)					, axis=0							).argmax(axis=0							).clip(max=log_sigmas.shape[0] - 2							)
   __A		:  int       				=		low_idx + 1

   __A		:  str       				=		log_sigmas[low_idx]
   __A		:  Optional[Any]       				=		log_sigmas[high_idx]

   # interpolate sigmas
   __A		:  Optional[int]       				=		(low - log_sigma) / (low - high)
   __A		:  int       				=		np.clip(lowerCamelCase					, 0					, 1							)

   # transform interpolation to time range
   __A		:  List[Any]       				=		(1 - w) * low_idx + w * high_idx
   __A		:  Any       				=		t.reshape(sigma.shape							)
   return t
 def 		lowercase_( self      :     Dict					, lowerCamelCase      :     torch.FloatTensor							):



   """simple docstring"""





   __A		:  float       				=		in_sigmas[-1].item()
   __A		:  float       				=		in_sigmas[0].item()

   __A		:  Union[str, Any]       				=		7.0  # 7.0 is the value used in the paper
   __A		:  Tuple       				=		np.linspace(0					, 1					, self.num_inference_steps							)
   __A		:  Union[str, Any]       				=		sigma_min ** (1 / rho)
   __A		:  Tuple       				=		sigma_max ** (1 / rho)
   __A		:  Union[str, Any]       				=		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
   return sigmas
 def 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     Union[float, torch.FloatTensor]					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     int = 4					, lowerCamelCase      :     bool = True					, ):



   """simple docstring"""





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

   if isinstance(lowerCamelCase					, torch.Tensor							):
     __A		:  int       				=		timestep.to(self.timesteps.device							)
   __A		:  List[Any]       				=		(self.timesteps == timestep).nonzero().item()
   __A		:  Any       				=		self.sigmas[step_index]

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   if self.config.prediction_type == "epsilon":
     __A		:  Optional[Any]       				=		sample - sigma * model_output
   elif self.config.prediction_type == "v_prediction":
     # * c_out + input * c_skip
     __A		:  Tuple       				=		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
   elif self.config.prediction_type == "sample":
     __A		:  Optional[int]       				=		model_output
   else:
     raise ValueError(
         f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"							)

   # 2. Convert to an ODE derivative
   __A		:  str       				=		(sample - pred_original_sample) / sigma
   self.derivatives.append(lowerCamelCase							)
   if len(self.derivatives							) > order:
     self.derivatives.pop(0							)

   # 3. Compute linear multistep coefficients
   __A		:  List[str]       				=		min(step_index + 1					, lowerCamelCase							)
   __A		:  List[str]       				=		[self.get_lms_coefficient(lowerCamelCase					, lowerCamelCase					, lowerCamelCase							) for curr_order in range(lowerCamelCase							)]

   # 4. Compute previous sample based on the derivatives path
   __A		:  Tuple       				=		sample + sum(
       coeff * derivative for coeff, derivative in zip(lowerCamelCase					, reversed(self.derivatives							)							)							)

   if not return_dict:
     return (prev_sample,)

   return LMSDiscreteSchedulerOutput(prev_sample=lowerCamelCase					, pred_original_sample=lowerCamelCase							)
 def 		lowercase_( self      :     Optional[int]					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     torch.FloatTensor					, ):



   """simple docstring"""





   __A		:  Dict       				=		self.sigmas.to(device=original_samples.device					, dtype=original_samples.dtype							)
   if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase							):
     # mps does not support float64
     __A		:  Optional[int]       				=		self.timesteps.to(original_samples.device					, dtype=torch.floataa							)
     __A		:  Any       				=		timesteps.to(original_samples.device					, dtype=torch.floataa							)
   else:
     __A		:  Tuple       				=		self.timesteps.to(original_samples.device							)
     __A		:  Optional[Any]       				=		timesteps.to(original_samples.device							)

   __A		:  Optional[Any]       				=		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

   __A		:  Optional[int]       				=		sigmas[step_indices].flatten()
   while len(sigma.shape							) < len(original_samples.shape							):
     __A		:  Tuple       				=		sigma.unsqueeze(-1							)

   __A		:  str       				=		original_samples + noise * sigma
   return noisy_samples






 def __len__( self      :     Optional[Any]							):



   """simple docstring"""





   return self.config.num_train_timesteps





'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__ :		str					={'configuration_t5': ['T5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'T5Config', 'T5OnnxConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Optional[int]					=['T5Tokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=['T5TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         'T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'T5EncoderModel',
         'T5ForConditionalGeneration',
         'T5Model',
         'T5PreTrainedModel',
         'load_tf_weights_in_t5',
         'T5ForQuestionAnswering',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		List[str]					=[
         'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFT5EncoderModel',
         'TFT5ForConditionalGeneration',
         'TFT5Model',
         'TFT5PreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         'FlaxT5EncoderModel',
         'FlaxT5ForConditionalGeneration',
         'FlaxT5Model',
         'FlaxT5PreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta import TaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta_fast import TaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ta import (
              T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TaEncoderModel,
              TaForConditionalGeneration,
              TaForQuestionAnswering,
              TaModel,
              TaPreTrainedModel,
              load_tf_weights_in_ta,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_ta import (
              TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTaEncoderModel,
              TFTaForConditionalGeneration,
              TFTaModel,
              TFTaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_ta import (
              FlaxTaEncoderModel,
              FlaxTaForConditionalGeneration,
              FlaxTaModel,
              FlaxTaPreTrainedModel,
          )


else:
     import sys

     A__ :		Dict					=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



