





"""simple docstring"""




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




@dataclass
class  _a						(      _lowerCAmelCase				):
				UpperCamelCase								=    42




class  _a						(      _lowerCAmelCase	,				_lowerCAmelCase				):
				UpperCamelCase								=    1






				@register_to_config
				def __init__( self		:    List[Any], lowerCAmelCase__		:    int = 4_0, lowerCAmelCase__		:    float = 0.002, lowerCAmelCase__		:    float = 80.0, lowerCAmelCase__		:    float = 0.5, lowerCAmelCase__		:    float = 1.0, lowerCAmelCase__		:    float = 7.0, lowerCAmelCase__		:    bool = True, )	->      List[str]:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    sigma_max

										_UpperCamelCase			:					Dict     =    np.linspace(0, 1, lowerCAmelCase__							)
										_UpperCamelCase			:					Tuple     =    self._convert_to_karras(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.sigma_to_t(lowerCAmelCase__							)

										# setable values
										_UpperCamelCase			:					Optional[Any]     =    None
										_UpperCamelCase			:					Optional[Any]     =    torch.from_numpy(lowerCAmelCase__							)
										_UpperCamelCase			:					Dict     =    torch.from_numpy(lowerCAmelCase__							)
										_UpperCamelCase			:					Any     =    False
										_UpperCamelCase			:					str     =    False






				def 				snake_case	( self		:    Optional[Any], lowerCAmelCase__		:    Any, lowerCAmelCase__		:    List[str]=None							)	->      str:




										'''simple docstring'''



										if schedule_timesteps is None:
																_UpperCamelCase			:					Dict     =    self.timesteps

										_UpperCamelCase			:					Union[str, Any]     =    (schedule_timesteps == timestep).nonzero()
										return indices.item()






				def 				snake_case	( self		:    Any, lowerCAmelCase__		:    torch.FloatTensor, lowerCAmelCase__		:    Union[float, torch.FloatTensor]							)	->      torch.FloatTensor:




										'''simple docstring'''



										if isinstance(lowerCAmelCase__, torch.Tensor							):
																_UpperCamelCase			:					Union[str, Any]     =    timestep.to(self.timesteps.device							)
										_UpperCamelCase			:					Dict     =    self.index_for_timestep(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.sigmas[step_idx]

										_UpperCamelCase			:					Tuple     =    sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

										_UpperCamelCase			:					List[Any]     =    True
										return sample






				def 				snake_case	( self		:    Any, lowerCAmelCase__		:    Union[float, np.ndarray]							)	->      Optional[int]:




										'''simple docstring'''



										if not isinstance(lowerCAmelCase__, np.ndarray							):
																_UpperCamelCase			:					Dict     =    np.array(lowerCAmelCase__, dtype=np.floataa							)

										_UpperCamelCase			:					List[str]     =    1_0_0_0 * 0.25 * np.log(sigmas + 1e-4_4							)

										return timesteps






				def 				snake_case	( self		:    Union[str, Any], lowerCAmelCase__		:    Optional[int] = None, lowerCAmelCase__		:    Union[str, torch.device] = None, lowerCAmelCase__		:    Optional[List[int]] = None, )	->      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(lowerCAmelCase__							)							):
																						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			:					Any     =    np.array(lowerCAmelCase__, dtype=np.intaa							)
																_UpperCamelCase			:					int     =    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			:					int     =    num_inference_steps

																_UpperCamelCase			:					str     =    self.config.num_train_timesteps // self.num_inference_steps
																_UpperCamelCase			:					List[str]     =    (np.arange(0, lowerCAmelCase__							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
																_UpperCamelCase			:					Tuple     =    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			:					Optional[Any]     =    self.config.num_train_timesteps
										_UpperCamelCase			:					List[str]     =    timesteps[::-1].copy()
										_UpperCamelCase			:					Optional[Any]     =    ramp / (num_train_timesteps - 1)
										_UpperCamelCase			:					Dict     =    self._convert_to_karras(lowerCAmelCase__							)
										_UpperCamelCase			:					str     =    self.sigma_to_t(lowerCAmelCase__							)

										_UpperCamelCase			:					int     =    np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
										_UpperCamelCase			:					List[str]     =    torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__							)

										if str(lowerCAmelCase__							).startswith('''mps'''							):
																# mps does not support float64
																_UpperCamelCase			:					Dict     =    torch.from_numpy(lowerCAmelCase__							).to(lowerCAmelCase__, dtype=torch.floataa							)
										else:
																_UpperCamelCase			:					Tuple     =    torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__							)






				def 				snake_case	( self		:    str, lowerCAmelCase__		:    Optional[int]							)	->      str:




										'''simple docstring'''



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

										_UpperCamelCase			:					Any     =    self.config.rho
										_UpperCamelCase			:					int     =    sigma_min ** (1 / rho)
										_UpperCamelCase			:					Tuple     =    sigma_max ** (1 / rho)
										_UpperCamelCase			:					Tuple     =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas






				def 				snake_case	( self		:    Dict, lowerCAmelCase__		:    Optional[int]							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[int]     =    self.config.sigma_data

										_UpperCamelCase			:					Dict     =    sigma_data**2 / (sigma**2 + sigma_data**2)
										_UpperCamelCase			:					Dict     =    sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out






				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    List[str]							)	->      Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    self.config.sigma_min
										_UpperCamelCase			:					Union[str, Any]     =    self.config.sigma_data

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






				def 				snake_case	( self		:    str, lowerCAmelCase__		:    torch.FloatTensor, lowerCAmelCase__		:    Union[float, torch.FloatTensor], lowerCAmelCase__		:    torch.FloatTensor, lowerCAmelCase__		:    Optional[torch.Generator] = None, lowerCAmelCase__		:    bool = True, )	->      Union[CMStochasticIterativeSchedulerOutput, Tuple]:




										'''simple docstring'''



										if (
										    isinstance(lowerCAmelCase__, lowerCAmelCase__							)
										    or isinstance(lowerCAmelCase__, torch.IntTensor							)
										    or isinstance(lowerCAmelCase__, 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(lowerCAmelCase__, torch.Tensor							):
																_UpperCamelCase			:					List[str]     =    timestep.to(self.timesteps.device							)

										_UpperCamelCase			:					Any     =    self.config.sigma_min
										_UpperCamelCase			:					List[str]     =    self.config.sigma_max

										_UpperCamelCase			:					List[Any]     =    self.index_for_timestep(lowerCAmelCase__							)

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

										# Get scalings for boundary conditions
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    self.get_scalings_for_boundary_condition(lowerCAmelCase__							)

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

										_UpperCamelCase			:					List[Any]     =    sigma_next.clamp(min=lowerCAmelCase__, max=lowerCAmelCase__							)

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

										if not return_dict:
																return (prev_sample,)

										return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCAmelCase__							)






				def 				snake_case	( self		:    Optional[int], lowerCAmelCase__		:    torch.FloatTensor, lowerCAmelCase__		:    torch.FloatTensor, lowerCAmelCase__		:    torch.FloatTensor, )	->      torch.FloatTensor:




										'''simple docstring'''



										_UpperCamelCase			:					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(lowerCAmelCase__							):
																# mps does not support float64
																_UpperCamelCase			:					List[Any]     =    self.timesteps.to(original_samples.device, dtype=torch.floataa							)
																_UpperCamelCase			:					Tuple     =    timesteps.to(original_samples.device, dtype=torch.floataa							)
										else:
																_UpperCamelCase			:					Optional[Any]     =    self.timesteps.to(original_samples.device							)
																_UpperCamelCase			:					Dict     =    timesteps.to(original_samples.device							)

										_UpperCamelCase			:					Dict     =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

										_UpperCamelCase			:					List[Any]     =    original_samples + noise * sigma
										return noisy_samples






				def __len__( self		:    str							)	->      int:




										'''simple docstring'''



										return self.config.num_train_timesteps








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_	       =   {
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "EsmForMaskedLM",
							    "EsmForSequenceClassification",
							    "EsmForTokenClassification",
							    "EsmModel",
							    "EsmPreTrainedModel",
							]
							UpperCamelCase_	       =   ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFEsmForMaskedLM",
							    "TFEsmForSequenceClassification",
							    "TFEsmForTokenClassification",
							    "TFEsmModel",
							    "TFEsmPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
							from .tokenization_esm import EsmTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_esm import (
														    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EsmForMaskedLM,
														    EsmForSequenceClassification,
														    EsmForTokenClassification,
														    EsmModel,
														    EsmPreTrainedModel,
														)
														from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_esm import (
														    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFEsmForMaskedLM,
														    TFEsmForSequenceClassification,
														    TFEsmForTokenClassification,
														    TFEsmModel,
														    TFEsmPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure)





