





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


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

@dataclass
class 					__A   (  a			):
				__A            = 42




class 					__A   (  a			,    a			):
				__A            = 1


				@register_to_config
				def __init__(						self      ,       UpperCAmelCase_ = 40      ,       UpperCAmelCase_ = 0.0_0_2      ,       UpperCAmelCase_ = 8_0.0      ,       UpperCAmelCase_ = 0.5      ,       UpperCAmelCase_ = 1.0      ,       UpperCAmelCase_ = 7.0      ,       UpperCAmelCase_ = True      ,       ):
								# standard deviation of the initial noise distribution
								lowerCamelCase		       =sigma_max

								lowerCamelCase		       =np.linspace(0      ,       1      ,       UpperCAmelCase_							)
								lowerCamelCase		       =self._convert_to_karras(UpperCAmelCase_							)
								lowerCamelCase		       =self.sigma_to_t(UpperCAmelCase_							)

								# setable values
								lowerCamelCase		       =None
								lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							)
								lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							)
								lowerCamelCase		       =False
								lowerCamelCase		       =False


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_=None							):
								if schedule_timesteps is None:
												lowerCamelCase		       =self.timesteps

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


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								# Get sigma corresponding to timestep
								if isinstance(UpperCAmelCase_      ,       torch.Tensor							):
												lowerCamelCase		       =timestep.to(self.timesteps.device							)
								lowerCamelCase		       =self.index_for_timestep(UpperCAmelCase_							)
								lowerCamelCase		       =self.sigmas[step_idx]

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

								lowerCamelCase		       =True
								return sample


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								if not isinstance(UpperCAmelCase_      ,       np.ndarray							):
												lowerCamelCase		       =np.array(UpperCAmelCase_      ,       dtype=np.floataa							)

								lowerCamelCase		       =1000 * 0.2_5 * np.log(sigmas + 1E-44							)

								return timesteps


				def _snake_case				(						self      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       ):
								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(UpperCAmelCase_							)							):
																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}."""							)

												lowerCamelCase		       =np.array(UpperCAmelCase_      ,       dtype=np.intaa							)
												lowerCamelCase		       =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."""							)

												lowerCamelCase		       =num_inference_steps

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

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

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

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


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =self.config.sigma_min
								lowerCamelCase		       =self.config.sigma_max

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


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =self.config.sigma_data

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


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =self.config.sigma_min
								lowerCamelCase		       =self.config.sigma_data

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


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       ):

								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"""
												        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(UpperCAmelCase_      ,       torch.Tensor							):
												lowerCamelCase		       =timestep.to(self.timesteps.device							)

								lowerCamelCase		       =self.config.sigma_min
								lowerCamelCase		       =self.config.sigma_max

								lowerCamelCase		       =self.index_for_timestep(UpperCAmelCase_							)

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

								# Get scalings for boundary conditions
								lowerCamelCase       ,     lowerCamelCase		       =self.get_scalings_for_boundary_condition(UpperCAmelCase_							)

								# 1. Denoise model output using boundary conditions
								lowerCamelCase		       =c_out * model_output + c_skip * sample
								if self.config.clip_denoised:
												lowerCamelCase		       =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:
												lowerCamelCase		       =randn_tensor(
												    model_output.shape      ,       dtype=model_output.dtype      ,       device=model_output.device      ,       generator=UpperCAmelCase_							)
								else:
												lowerCamelCase		       =torch.zeros_like(UpperCAmelCase_							)
								lowerCamelCase		       =noise * self.config.s_noise

								lowerCamelCase		       =sigma_next.clamp(min=UpperCAmelCase_      ,       max=UpperCAmelCase_							)

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

								if not return_dict:
												return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       ):
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowerCamelCase		       =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		       =self.timesteps.to(original_samples.device      ,       dtype=torch.floataa							)
												lowerCamelCase		       =timesteps.to(original_samples.device      ,       dtype=torch.floataa							)
								else:
												lowerCamelCase		       =self.timesteps.to(original_samples.device							)
												lowerCamelCase		       =timesteps.to(original_samples.device							)

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

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

								lowerCamelCase		       =original_samples + noise * sigma
								return noisy_samples


				def __len__(						self							):
								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,
)


UpperCAmelCase__  :	Optional[Any]     ={'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =['''PegasusTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	int     =['''PegasusTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	str     =[
							    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''PegasusForCausalLM''',
							    '''PegasusForConditionalGeneration''',
							    '''PegasusModel''',
							    '''PegasusPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =[
							    '''TFPegasusForConditionalGeneration''',
							    '''TFPegasusModel''',
							    '''TFPegasusPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Dict     =[
							    '''FlaxPegasusForConditionalGeneration''',
							    '''FlaxPegasusModel''',
							    '''FlaxPegasusPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_pegasus import PegasusTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_pegasus_fast import PegasusTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pegasus import (
														    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PegasusForCausalLM,
														    PegasusForConditionalGeneration,
														    PegasusModel,
														    PegasusPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_pegasus import (
														    FlaxPegasusForConditionalGeneration,
														    FlaxPegasusModel,
														    FlaxPegasusPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__  :	Union[str, Any]     =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





