

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 SchedulerMixin


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
class 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :torch.FloatTensor






class 	__magic_name__				(       snake_case ,			snake_case							):
     UpperCamelCase_  :Optional[Any]								=			1






     @register_to_config
     def __init__(							self		,	_lowercase = 40		,	_lowercase = 0.002		,	_lowercase = 80.0		,	_lowercase = 0.5		,	_lowercase = 1.0		,	_lowercase = 7.0		,	_lowercase = True		,	)->    Dict:
         # standard deviation of the initial noise distribution
         UpperCamelCase_      =	sigma_max

         UpperCamelCase_      =	np.linspace(0		,	1		,	_lowercase  )
         UpperCamelCase_      =	self._convert_to_karras(_lowercase  )
         UpperCamelCase_      =	self.sigma_to_t(_lowercase  )

         # setable values
         UpperCamelCase_      =	None
         UpperCamelCase_      =	torch.from_numpy(_lowercase  )
         UpperCamelCase_      =	torch.from_numpy(_lowercase  )
         UpperCamelCase_      =	False
         UpperCamelCase_      =	False






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase=None  )->    List[str]:
         if schedule_timesteps is None:
             UpperCamelCase_      =	self.timesteps

         UpperCamelCase_      =	(schedule_timesteps == timestep).nonzero()
         return indices.item()






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase  )->    torch.FloatTensor:
         # Get sigma corresponding to timestep
         if isinstance(_lowercase		,	torch.Tensor  ):
             UpperCamelCase_      =	timestep.to(self.timesteps.device  )
         UpperCamelCase_      =	self.index_for_timestep(_lowercase  )
         UpperCamelCase_      =	self.sigmas[step_idx]

         UpperCamelCase_      =	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         UpperCamelCase_      =	True
         return sample






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Any:
         if not isinstance(_lowercase		,	np.ndarray  ):
             UpperCamelCase_      =	np.array(_lowercase		,	dtype=np.floataa  )

         UpperCamelCase_      =	1_000 * 0.25 * np.log(sigmas + 1e-4_4  )

         return timesteps






     def   UpperCAmelCase_							(							self		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	)->    str:
         if num_inference_steps is None and timesteps is None:
             raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."  )

         if num_inference_steps is not None and timesteps is not None:
             raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."  )

         # Follow DDPMScheduler custom timesteps logic
         if timesteps is not None:
             for i in range(1		,	len(_lowercase  )  ):
                 if timesteps[i] >= timesteps[i - 1]:
                     raise ValueError("`timesteps` must be in descending order."  )

             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
                     F"`timesteps` must start before `self.config.train_timesteps`:"
                     F" {self.config.num_train_timesteps}."  )

             UpperCamelCase_      =	np.array(_lowercase		,	dtype=np.intaa  )
             UpperCamelCase_      =	True
         else:
             if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
                     F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
                     F" maximal {self.config.num_train_timesteps} timesteps."  )

             UpperCamelCase_      =	num_inference_steps

             UpperCamelCase_      =	self.config.num_train_timesteps // self.num_inference_steps
             UpperCamelCase_      =	(np.arange(0		,	_lowercase  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
             UpperCamelCase_      =	False

         # Map timesteps to Karras sigmas directly for multistep sampling
         # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
         UpperCamelCase_      =	self.config.num_train_timesteps
         UpperCamelCase_      =	timesteps[::-1].copy()
         UpperCamelCase_      =	ramp / (num_train_timesteps - 1)
         UpperCamelCase_      =	self._convert_to_karras(_lowercase  )
         UpperCamelCase_      =	self.sigma_to_t(_lowercase  )

         UpperCamelCase_      =	np.concatenate([sigmas, [self.sigma_min]]  ).astype(np.floataa  )
         UpperCamelCase_      =	torch.from_numpy(_lowercase  ).to(device=_lowercase  )

         if str(_lowercase  ).startswith("mps"  ):
             # mps does not support float64
             UpperCamelCase_      =	torch.from_numpy(_lowercase  ).to(_lowercase		,	dtype=torch.floataa  )
         else:
             UpperCamelCase_      =	torch.from_numpy(_lowercase  ).to(device=_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    int:
         UpperCamelCase_      =	self.config.sigma_min
         UpperCamelCase_      =	self.config.sigma_max

         UpperCamelCase_      =	self.config.rho
         UpperCamelCase_      =	sigma_min ** (1 / rho)
         UpperCamelCase_      =	sigma_max ** (1 / rho)
         UpperCamelCase_      =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    List[str]:
         UpperCamelCase_      =	self.config.sigma_data

         UpperCamelCase_      =	sigma_data**2 / (sigma**2 + sigma_data**2)
         UpperCamelCase_      =	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    List[Any]:
         UpperCamelCase_      =	self.config.sigma_min
         UpperCamelCase_      =	self.config.sigma_data

         UpperCamelCase_      =	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         UpperCamelCase_      =	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase = None		,	_lowercase = True		,	)->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:

         if (
             isinstance(_lowercase		,	_lowercase  )
             or isinstance(_lowercase		,	torch.IntTensor  )
             or isinstance(_lowercase		,	torch.LongTensor  )
         ):
             raise ValueError(
                 (
                     "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                     F" `{self.__class__}.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(_lowercase		,	torch.Tensor  ):
             UpperCamelCase_      =	timestep.to(self.timesteps.device  )

         UpperCamelCase_      =	self.config.sigma_min
         UpperCamelCase_      =	self.config.sigma_max

         UpperCamelCase_      =	self.index_for_timestep(_lowercase  )

         # sigma_next corresponds to next_t in original implementation
         UpperCamelCase_      =	self.sigmas[step_index]
         if step_index + 1 < self.config.num_train_timesteps:
             UpperCamelCase_      =	self.sigmas[step_index + 1]
         else:
             # Set sigma_next to sigma_min
             UpperCamelCase_      =	self.sigmas[-1]

         # Get scalings for boundary conditions
         UpperCamelCase_     ,					UpperCamelCase_      =	self.get_scalings_for_boundary_condition(_lowercase  )

         # 1. Denoise model output using boundary conditions
         UpperCamelCase_      =	c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
             UpperCamelCase_      =	denoised.clamp(-1		,	1  )

         # 2. Sample z ~ N(0, s_noise^2 * I)
         # Noise is not used for onestep sampling.
         if len(self.timesteps  ) > 1:
             UpperCamelCase_      =	randn_tensor(
                 model_output.shape		,	dtype=model_output.dtype		,	device=model_output.device		,	generator=_lowercase  )
         else:
             UpperCamelCase_      =	torch.zeros_like(_lowercase  )
         UpperCamelCase_      =	noise * self.config.s_noise

         UpperCamelCase_      =	sigma_next.clamp(min=_lowercase		,	max=_lowercase  )

         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         UpperCamelCase_      =	denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

         if not return_dict:
             return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	)->    torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         UpperCamelCase_      =	self.sigmas.to(device=original_samples.device		,	dtype=original_samples.dtype  )
         if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase  ):
             # mps does not support float64
             UpperCamelCase_      =	self.timesteps.to(original_samples.device		,	dtype=torch.floataa  )
             UpperCamelCase_      =	timesteps.to(original_samples.device		,	dtype=torch.floataa  )
         else:
             UpperCamelCase_      =	self.timesteps.to(original_samples.device  )
             UpperCamelCase_      =	timesteps.to(original_samples.device  )

         UpperCamelCase_      =	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

         UpperCamelCase_      =	sigmas[step_indices].flatten()
         while len(sigma.shape  ) < len(original_samples.shape  ):
             UpperCamelCase_      =	sigma.unsqueeze(-1  )

         UpperCamelCase_      =	original_samples + noise * sigma
         return noisy_samples






     def __len__(							self  )->    Dict:
         return self.config.num_train_timesteps








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,
)


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict				  =    ["""T5Tokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str				  =    ["""T5TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[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:
 SCREAMING_SNAKE_CASE		:int				  =    [
     """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFT5EncoderModel""",
     """TFT5ForConditionalGeneration""",
     """TFT5Model""",
     """TFT5PreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[str]				  =    [
     """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

 SCREAMING_SNAKE_CASE		:List[Any]				  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
