



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase	:   str    	={"configuration_umt5": ["UMT5Config", "UMT5OnnxConfig"]}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase	:   Tuple    	=[
		    "UMT5EncoderModel",
		    "UMT5ForConditionalGeneration",
		    "UMT5ForQuestionAnswering",
		    "UMT5Model",
		    "UMT5PreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_umta import UMTaConfig, UMTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_umta import (
				    UMTaEncoderModel,
				    UMTaForConditionalGeneration,
				    UMTaForQuestionAnswering,
				    UMTaModel,
				    UMTaPreTrainedModel,
				)
else:
		import sys

		_lowercase	:   Tuple    	=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









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


_lowercase	:   Tuple    	=logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :torch.FloatTensor




class      snake_case__       (A__      ,			A__	):







						"""simple docstring"""




						__lowerCAmelCase  :Optional[int]			      =  1

						@register_to_config
						def __init__(     self      ,     __lowercase = 4_0      ,     __lowercase = 0.0_0_2      ,     __lowercase = 8_0.0      ,     __lowercase = 0.5      ,     __lowercase = 1.0      ,     __lowercase = 7.0      ,     __lowercase = True      ,     )      ->   str:
									"""simple docstring"""




									a__     :		Dict		 =						sigma_max

									a__     :		int		 =						np.linspace(0      ,     1      ,     __lowercase						)
									a__     :		int		 =						self._convert_to_karras(__lowercase						)
									a__     :		Any		 =						self.sigma_to_t(__lowercase						)

									# setable values
									a__     :		Optional[int]		 =						None
									a__     :		Union[str, Any]		 =						torch.from_numpy(__lowercase						)
									a__     :		Tuple		 =						torch.from_numpy(__lowercase						)
									a__     :		Optional[Any]		 =						False
									a__     :		List[str]		 =						False

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase=None						)      ->   Union[str, Any]:
									"""simple docstring"""




									if schedule_timesteps is None:
												a__     :		int		 =						self.timesteps

									a__     :		Optional[int]		 =						(schedule_timesteps == timestep).nonzero()
									return indices.item()

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   torch.FloatTensor:
									"""simple docstring"""




									if isinstance(__lowercase      ,     torch.Tensor						):
												a__     :		str		 =						timestep.to(self.timesteps.device						)
									a__     :		Optional[Any]		 =						self.index_for_timestep(__lowercase						)
									a__     :		Tuple		 =						self.sigmas[step_idx]

									a__     :		Optional[Any]		 =						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									a__     :		List[str]		 =						True
									return sample

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Dict:
									"""simple docstring"""




									if not isinstance(__lowercase      ,     np.ndarray						):
												a__     :		Optional[Any]		 =						np.array(__lowercase      ,     dtype=np.floataa						)

									a__     :		str		 =						1_0_0_0 * 0.2_5 * np.log(sigmas + 1E-44						)

									return timesteps

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase = None      ,     __lowercase = None      ,     __lowercase = None      ,     )      ->   List[Any]:
									"""simple docstring"""




									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}.'''						)

												a__     :		List[str]		 =						np.array(__lowercase      ,     dtype=np.intaa						)
												a__     :		str		 =						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.'''						)

												a__     :		List[str]		 =						num_inference_steps

												a__     :		str		 =						self.config.num_train_timesteps // self.num_inference_steps
												a__     :		int		 =						(np.arange(0      ,     __lowercase						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
												a__     :		Union[str, Any]		 =						False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									a__     :		int		 =						self.config.num_train_timesteps
									a__     :		Optional[Any]		 =						timesteps[::-1].copy()
									a__     :		Optional[Any]		 =						ramp / (num_train_timesteps - 1)
									a__     :		Union[str, Any]		 =						self._convert_to_karras(__lowercase						)
									a__     :		Any		 =						self.sigma_to_t(__lowercase						)

									a__     :		Dict		 =						np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
									a__     :		List[str]		 =						torch.from_numpy(__lowercase						).to(device=__lowercase						)

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

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Optional[Any]:
									"""simple docstring"""




									a__     :		float		 =						self.config.sigma_min
									a__     :		float		 =						self.config.sigma_max

									a__     :		Optional[int]		 =						self.config.rho
									a__     :		List[str]		 =						sigma_min ** (1 / rho)
									a__     :		Any		 =						sigma_max ** (1 / rho)
									a__     :		Any		 =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Optional[int]:
									"""simple docstring"""




									a__     :		int		 =						self.config.sigma_data

									a__     :		List[Any]		 =						sigma_data**2 / (sigma**2 + sigma_data**2)
									a__     :		Optional[Any]		 =						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int:
									"""simple docstring"""




									a__     :		str		 =						self.config.sigma_min
									a__     :		Dict		 =						self.config.sigma_data

									a__     :		Dict		 =						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									a__     :		List[Any]		 =						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase = None      ,     __lowercase = True      ,     )      ->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:
									"""simple docstring"""




									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						):
												a__     :		Optional[int]		 =						timestep.to(self.timesteps.device						)

									a__     :		Tuple		 =						self.config.sigma_min
									a__     :		Tuple		 =						self.config.sigma_max

									a__     :		Union[str, Any]		 =						self.index_for_timestep(__lowercase						)

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

									# Get scalings for boundary conditions
									a__ ,		a__     :		Any		 =						self.get_scalings_for_boundary_condition(__lowercase						)

									# 1. Denoise model output using boundary conditions
									a__     :		Optional[int]		 =						c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
												a__     :		Dict		 =						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:
												a__     :		Optional[Any]		 =						randn_tensor(
												    model_output.shape      ,     dtype=model_output.dtype      ,     device=model_output.device      ,     generator=__lowercase						)
									else:
												a__     :		Any		 =						torch.zeros_like(__lowercase						)
									a__     :		Union[str, Any]		 =						noise * self.config.s_noise

									a__     :		Union[str, Any]		 =						sigma_next.clamp(min=__lowercase      ,     max=__lowercase						)

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									a__     :		Union[str, Any]		 =						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
												return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     )      ->   torch.FloatTensor:
									"""simple docstring"""




									a__     :		Union[str, Any]		 =						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
												a__     :		List[str]		 =						self.timesteps.to(original_samples.device      ,     dtype=torch.floataa						)
												a__     :		Any		 =						timesteps.to(original_samples.device      ,     dtype=torch.floataa						)
									else:
												a__     :		str		 =						self.timesteps.to(original_samples.device						)
												a__     :		List[str]		 =						timesteps.to(original_samples.device						)

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

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

									a__     :		Optional[int]		 =						original_samples + noise * sigma
									return noisy_samples

						def __len__(     self						)      ->   int:
									"""simple docstring"""




									return self.config.num_train_timesteps




