


"""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


__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		torch.FloatTensor





class   __A  (snake_case__    ,		snake_case__):





							'''simple docstring'''

							__lowercase:		Dict				   =     1



							@register_to_config
							def __init__(					self				:   Tuple		,							UpperCAmelCase_				:   int = 40		,							UpperCAmelCase_				:   float = 0.002		,							UpperCAmelCase_				:   float = 80.0		,							UpperCAmelCase_				:   float = 0.5		,							UpperCAmelCase_				:   float = 1.0		,							UpperCAmelCase_				:   float = 7.0		,							UpperCAmelCase_				:   bool = True		,							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		sigma_max

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

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



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   str=None							)       ->Optional[Any]:



														"""simple docstring"""



														if schedule_timesteps is None:
																					snake_case_     							=		self.timesteps

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



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   torch.FloatTensor		,							UpperCAmelCase_				:   Union[float, torch.FloatTensor]							)       ->torch.FloatTensor:



														"""simple docstring"""



														if isinstance(UpperCAmelCase_		,							torch.Tensor							):
																					snake_case_     							=		timestep.to(self.timesteps.device							)
														snake_case_     							=		self.index_for_timestep(UpperCAmelCase_							)
														snake_case_     							=		self.sigmas[step_idx]

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

														snake_case_     							=		True
														return sample



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Union[float, np.ndarray]							)       ->Tuple:



														"""simple docstring"""



														if not isinstance(UpperCAmelCase_		,							np.ndarray							):
																					snake_case_     							=		np.array(UpperCAmelCase_		,							dtype=np.floataa							)

														snake_case_     							=		1_000 * 0.25 * np.log(sigmas + 1E-44							)

														return timesteps



							def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   Optional[int] = None		,							UpperCAmelCase_				:   Union[str, torch.device] = None		,							UpperCAmelCase_				:   Optional[List[int]] = None		,							)       ->List[str]:



														"""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(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}."""							)

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

																					snake_case_     							=		num_inference_steps

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

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

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

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



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   List[Any]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		self.config.sigma_min
														snake_case_     							=		self.config.sigma_max

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



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Optional[Any]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		self.config.sigma_data

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



							def 		lowerCAmelCase							(					self				:   Any		,							UpperCAmelCase_				:   int							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		self.config.sigma_min
														snake_case_     							=		self.config.sigma_data

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



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   torch.FloatTensor		,							UpperCAmelCase_				:   Union[float, torch.FloatTensor]		,							UpperCAmelCase_				:   torch.FloatTensor		,							UpperCAmelCase_				:   Optional[torch.Generator] = None		,							UpperCAmelCase_				:   bool = True		,							)       ->Union[CMStochasticIterativeSchedulerOutput, Tuple]:



														"""simple docstring"""



														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							):
																					snake_case_     							=		timestep.to(self.timesteps.device							)

														snake_case_     							=		self.config.sigma_min
														snake_case_     							=		self.config.sigma_max

														snake_case_     							=		self.index_for_timestep(UpperCAmelCase_							)

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

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

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

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

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

														if not return_dict:
																					return (prev_sample,)

														return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   torch.FloatTensor		,							UpperCAmelCase_				:   torch.FloatTensor		,							UpperCAmelCase_				:   torch.FloatTensor		,							)       ->torch.FloatTensor:



														"""simple docstring"""



														snake_case_     							=		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
																					snake_case_     							=		self.timesteps.to(original_samples.device		,							dtype=torch.floataa							)
																					snake_case_     							=		timesteps.to(original_samples.device		,							dtype=torch.floataa							)
														else:
																					snake_case_     							=		self.timesteps.to(original_samples.device							)
																					snake_case_     							=		timesteps.to(original_samples.device							)

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

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

														snake_case_     							=		original_samples + noise * sigma
														return noisy_samples



							def __len__(					self				:   str							)       ->Union[str, Any]:



														"""simple docstring"""



														return self.config.num_train_timesteps







"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class   __A  (snake_case__    ,		snake_case__    ,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(					self				:   List[str]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   float		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   bool = False		,							)       ->Dict:



														"""simple docstring"""



														super().__init__()

														snake_case_     							=		nn.Linear(UpperCAmelCase_		,							UpperCAmelCase_		,							bias=UpperCAmelCase_							)

														snake_case_     							=		nn.Embedding(UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		False

														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)

														snake_case_     							=		TaConfig(
														    d_model=UpperCAmelCase_		,							num_heads=UpperCAmelCase_		,							d_kv=UpperCAmelCase_		,							d_ff=UpperCAmelCase_		,							feed_forward_proj=UpperCAmelCase_		,							dropout_rate=UpperCAmelCase_		,							is_decoder=UpperCAmelCase_		,							is_encoder_decoder=UpperCAmelCase_		,							)
														snake_case_     							=		nn.ModuleList()
														for lyr_num in range(UpperCAmelCase_							):
																					snake_case_     							=		TaBlock(UpperCAmelCase_							)
																					self.encoders.append(UpperCAmelCase_							)

														snake_case_     							=		TaLayerNorm(UpperCAmelCase_							)
														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   List[str]							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.input_proj(UpperCAmelCase_							)

														# terminal relative positional encodings
														snake_case_     							=		encoder_inputs.shape[1]
														snake_case_     							=		torch.arange(UpperCAmelCase_		,							device=encoder_inputs.device							)

														snake_case_     							=		encoder_inputs_mask.sum(-1							)
														snake_case_     							=		torch.roll(input_positions.unsqueeze(0							)		,							tuple(seq_lens.tolist()							)		,							dims=0							)
														x += self.position_encoding(UpperCAmelCase_							)

														snake_case_     							=		self.dropout_pre(UpperCAmelCase_							)

														# inverted the attention mask
														snake_case_     							=		encoder_inputs.size()
														snake_case_     							=		self.get_extended_attention_mask(UpperCAmelCase_		,							UpperCAmelCase_							)

														for lyr in self.encoders:
																					snake_case_     							=		lyr(UpperCAmelCase_		,							UpperCAmelCase_							)[0]
														snake_case_     							=		self.layer_norm(UpperCAmelCase_							)

														return self.dropout_post(UpperCAmelCase_							), encoder_inputs_mask



