




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 		lowercase__   (		__A:	List[str]							,__A:	Tuple=0.999							,__A:	str="cosine"							,):





					'''simple docstring'''






					if alpha_transform_type == "cosine":

										def alpha_bar_fn(__A:	Any				):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(__A:	List[str]				):
															return math.exp(t * -12.0				)

					else:
										raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

					__magic_name__							:	str    							=							[]
					for i in range(__A				):
										__magic_name__							:	List[str]    							=							i / num_diffusion_timesteps
										__magic_name__							:	Dict    							=							(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(__A				) / alpha_bar_fn(__A				)							,__A				)				)
					return torch.tensor(__A							,dtype=torch.floataa				)


class    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[e.name for e in KarrasDiffusionSchedulers]
			UpperCamelCase__								=2

			@register_to_config
			def __init__( self			:   Optional[int]       ,						lowerCamelCase_			:   int = 1000       ,						lowerCamelCase_			:   float = 0.0_0_0_8_5       ,						lowerCamelCase_			:   float = 0.0_1_2       ,						lowerCamelCase_			:   str = "linear"       ,						lowerCamelCase_			:   Optional[Union[np.ndarray, List[float]]] = None       ,						lowerCamelCase_			:   str = "epsilon"       ,						lowerCamelCase_			:   Optional[bool] = False       ,						lowerCamelCase_			:   Optional[bool] = False       ,						lowerCamelCase_			:   float = 1.0       ,						lowerCamelCase_			:   str = "linspace"       ,						lowerCamelCase_			:   int = 0       ,						)						->   Any:
								if trained_betas is not None:
													__magic_name__							:	List[Any]    							=							torch.tensor(lowerCamelCase_       ,						dtype=torch.floataa  )
								elif beta_schedule == "linear":
													__magic_name__							:	List[Any]    							=							torch.linspace(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						dtype=torch.floataa  )
								elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__magic_name__							:	Tuple    							=							(
													    torch.linspace(beta_start**0.5       ,						beta_end**0.5       ,						lowerCamelCase_       ,						dtype=torch.floataa  ) ** 2
													)
								elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__magic_name__							:	Optional[Any]    							=							betas_for_alpha_bar(lowerCamelCase_       ,						alpha_transform_type='''cosine'''  )
								elif beta_schedule == "exp":
													__magic_name__							:	Any    							=							betas_for_alpha_bar(lowerCamelCase_       ,						alpha_transform_type='''exp'''  )
								else:
													raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

								__magic_name__							:	List[Any]    							=							1.0 - self.betas
								__magic_name__							:	Optional[int]    							=							torch.cumprod(self.alphas       ,						dim=0  )

								#  set all values
								self.set_timesteps(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							:	Any    							=							use_karras_sigmas

			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   List[Any]=None  )						->   Tuple:
								if schedule_timesteps is None:
													__magic_name__							:	List[Any]    							=							self.timesteps

								__magic_name__							:	Dict    							=							(schedule_timesteps == timestep).nonzero()

								# The sigma index that is taken for the **very** first `step`
								# is always the second index (or the last index if there is only 1)
								# This way we can ensure we don't accidentally skip a sigma in
								# case we start in the middle of the denoising schedule (e.g. for image-to-image)
								if len(self._index_counter  ) == 0:
													__magic_name__							:	Optional[int]    							=							1 if len(lowerCamelCase_  ) > 1 else 0
								else:
													__magic_name__							:	Dict    							=							timestep.cpu().item() if torch.is_tensor(lowerCamelCase_  ) else timestep
													__magic_name__							:	List[Any]    							=							self._index_counter[timestep_int]

								return indices[pos].item()

			@property
			def 							UpperCAmelCase__       ( self			:   int  )						->   Union[str, Any]:
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   Union[float, torch.FloatTensor]       ,						)						->   torch.FloatTensor:
								__magic_name__							:	Any    							=							self.index_for_timestep(lowerCamelCase_  )

								__magic_name__							:	str    							=							self.sigmas[step_index]
								__magic_name__							:	Optional[int]    							=							sample / ((sigma**2 + 1) ** 0.5)
								return sample

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, torch.device] = None       ,						lowerCamelCase_			:   Optional[int] = None       ,						)						->   int:
								__magic_name__							:	Tuple    							=							num_inference_steps

								__magic_name__							:	Optional[Any]    							=							num_train_timesteps or self.config.num_train_timesteps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
													__magic_name__							:	List[str]    							=							np.linspace(0       ,						num_train_timesteps - 1       ,						lowerCamelCase_       ,						dtype=lowerCamelCase_  )[::-1].copy()
								elif self.config.timestep_spacing == "leading":
													__magic_name__							:	Optional[int]    							=							num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__magic_name__							:	Optional[int]    							=							(np.arange(0       ,						lowerCamelCase_  ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_  )
													timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
													__magic_name__							:	Dict    							=							num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__magic_name__							:	Union[str, Any]    							=							(np.arange(lowerCamelCase_       ,						0       ,						-step_ratio  )).round().copy().astype(lowerCamelCase_  )
													timesteps -= 1
								else:
													raise ValueError(
													    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''  )

								__magic_name__							:	Tuple    							=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
								__magic_name__							:	Any    							=							np.log(lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							np.interp(lowerCamelCase_       ,						np.arange(0       ,						len(lowerCamelCase_  )  )       ,						lowerCamelCase_  )

								if self.config.use_karras_sigmas:
													__magic_name__							:	str    							=							self._convert_to_karras(in_sigmas=lowerCamelCase_       ,						num_inference_steps=self.num_inference_steps  )
													__magic_name__							:	Union[str, Any]    							=							np.array([self._sigma_to_t(lowerCamelCase_       ,						lowerCamelCase_  ) for sigma in sigmas]  )

								__magic_name__							:	Union[str, Any]    							=							np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
								__magic_name__							:	int    							=							torch.from_numpy(lowerCamelCase_  ).to(device=lowerCamelCase_  )
								__magic_name__							:	Dict    							=							torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2  ), sigmas[-1:]]  )

								__magic_name__							:	List[Any]    							=							torch.from_numpy(lowerCamelCase_  )
								__magic_name__							:	Dict    							=							torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2  )]  )

								if str(lowerCamelCase_  ).startswith('''mps'''  ):
													# mps does not support float64
													__magic_name__							:	List[Any]    							=							timesteps.to(lowerCamelCase_       ,						dtype=torch.floataa  )
								else:
													__magic_name__							:	List[Any]    							=							timesteps.to(device=lowerCamelCase_  )

								# empty dt and derivative
								__magic_name__							:	Tuple    							=							None
								__magic_name__							:	str    							=							None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								__magic_name__							:	Any    							=							defaultdict(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Dict  )						->   Optional[int]:
								# get log sigma
								__magic_name__							:	Any    							=							np.log(lowerCamelCase_  )

								# get distribution
								__magic_name__							:	Tuple    							=							log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								__magic_name__							:	Optional[int]    							=							np.cumsum((dists >= 0)       ,						axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
								__magic_name__							:	int    							=							low_idx + 1

								__magic_name__							:	Optional[int]    							=							log_sigmas[low_idx]
								__magic_name__							:	Optional[Any]    							=							log_sigmas[high_idx]

								# interpolate sigmas
								__magic_name__							:	Optional[Any]    							=							(low - log_sigma) / (low - high)
								__magic_name__							:	Optional[Any]    							=							np.clip(lowerCamelCase_       ,						0       ,						1  )

								# transform interpolation to time range
								__magic_name__							:	Union[str, Any]    							=							(1 - w) * low_idx + w * high_idx
								__magic_name__							:	Optional[int]    							=							t.reshape(sigma.shape  )
								return t

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   Union[str, Any]  )						->   torch.FloatTensor:
								__magic_name__							:	float    							=							in_sigmas[-1].item()
								__magic_name__							:	float    							=							in_sigmas[0].item()

								__magic_name__							:	List[str]    							=							7.0  # 7.0 is the value used in the paper
								__magic_name__							:	Optional[Any]    							=							np.linspace(0       ,						1       ,						lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							sigma_min ** (1 / rho)
								__magic_name__							:	Optional[int]    							=							sigma_max ** (1 / rho)
								__magic_name__							:	int    							=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas

			@property
			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   str:
								return self.dt is None

			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Union[torch.FloatTensor, np.ndarray]       ,						lowerCamelCase_			:   Union[float, torch.FloatTensor]       ,						lowerCamelCase_			:   Union[torch.FloatTensor, np.ndarray]       ,						lowerCamelCase_			:   bool = True       ,						)						->   Union[SchedulerOutput, Tuple]:
								__magic_name__							:	Tuple    							=							self.index_for_timestep(lowerCamelCase_  )

								# advance index counter by 1
								__magic_name__							:	List[str]    							=							timestep.cpu().item() if torch.is_tensor(lowerCamelCase_  ) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
													__magic_name__							:	Optional[int]    							=							self.sigmas[step_index]
													__magic_name__							:	Optional[int]    							=							self.sigmas[step_index + 1]
								else:
													# 2nd order / Heun's method
													__magic_name__							:	Union[str, Any]    							=							self.sigmas[step_index - 1]
													__magic_name__							:	Optional[Any]    							=							self.sigmas[step_index]

								# currently only gamma=0 is supported. This usually works best anyways.
								# We can support gamma in the future but then need to scale the timestep before
								# passing it to the model which requires a change in API
								__magic_name__							:	Any    							=							0
								__magic_name__							:	int    							=							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
													__magic_name__							:	int    							=							sigma_hat if self.state_in_first_order else sigma_next
													__magic_name__							:	List[str]    							=							sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
													__magic_name__							:	List[str]    							=							sigma_hat if self.state_in_first_order else sigma_next
													__magic_name__							:	Dict    							=							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**2 + 1)
													)
								elif self.config.prediction_type == "sample":
													__magic_name__							:	Any    							=							model_output
								else:
													raise ValueError(
													    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

								if self.config.clip_sample:
													__magic_name__							:	Dict    							=							pred_original_sample.clamp(
													    -self.config.clip_sample_range       ,						self.config.clip_sample_range  )

								if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													__magic_name__							:	Dict    							=							(sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													__magic_name__							:	Tuple    							=							sigma_next - sigma_hat

													# store for 2nd order step
													__magic_name__							:	Any    							=							derivative
													__magic_name__							:	Optional[Any]    							=							dt
													__magic_name__							:	List[Any]    							=							sample
								else:
													# 2. 2nd order / Heun's method
													__magic_name__							:	List[Any]    							=							(sample - pred_original_sample) / sigma_next
													__magic_name__							:	Tuple    							=							(self.prev_derivative + derivative) / 2

													# 3. take prev timestep & sample
													__magic_name__							:	Optional[int]    							=							self.dt
													__magic_name__							:	Optional[int]    							=							self.sample

													# free dt and derivative
													# Note, this puts the scheduler in "first order mode"
													__magic_name__							:	List[str]    							=							None
													__magic_name__							:	Tuple    							=							None
													__magic_name__							:	Union[str, Any]    							=							None

								__magic_name__							:	Optional[int]    							=							sample + derivative * dt

								if not return_dict:
													return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   torch.FloatTensor       ,						lowerCamelCase_			:   torch.FloatTensor       ,						)						->   torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								__magic_name__							:	List[str]    							=							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
													__magic_name__							:	Union[str, Any]    							=							self.timesteps.to(original_samples.device       ,						dtype=torch.floataa  )
													__magic_name__							:	Dict    							=							timesteps.to(original_samples.device       ,						dtype=torch.floataa  )
								else:
													__magic_name__							:	Optional[int]    							=							self.timesteps.to(original_samples.device  )
													__magic_name__							:	str    							=							timesteps.to(original_samples.device  )

								__magic_name__							:	Dict    							=							[self.index_for_timestep(lowerCamelCase_       ,						lowerCamelCase_  ) for t in timesteps]

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

								__magic_name__							:	Dict    							=							original_samples + noise * sigma
								return noisy_samples


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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict				=			{}


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''llama'''
			UpperCamelCase__								=['''past_key_values''']

			def __init__( self			:   Dict       ,						lowerCamelCase_			:   List[str]=32000       ,						lowerCamelCase_			:   List[Any]=4096       ,						lowerCamelCase_			:   Any=11008       ,						lowerCamelCase_			:   Optional[Any]=32       ,						lowerCamelCase_			:   Dict=32       ,						lowerCamelCase_			:   Optional[Any]=None       ,						lowerCamelCase_			:   Dict="silu"       ,						lowerCamelCase_			:   Optional[int]=2048       ,						lowerCamelCase_			:   int=0.0_2       ,						lowerCamelCase_			:   Tuple=1E-6       ,						lowerCamelCase_			:   Tuple=True       ,						lowerCamelCase_			:   List[str]=0       ,						lowerCamelCase_			:   List[str]=1       ,						lowerCamelCase_			:   Optional[int]=2       ,						lowerCamelCase_			:   List[str]=1       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   int=None       ,						**lowerCamelCase_			:   Tuple       ,						)						->   Tuple:
								__magic_name__							:	Any    							=							vocab_size
								__magic_name__							:	Optional[int]    							=							max_position_embeddings
								__magic_name__							:	Union[str, Any]    							=							hidden_size
								__magic_name__							:	Dict    							=							intermediate_size
								__magic_name__							:	Union[str, Any]    							=							num_hidden_layers
								__magic_name__							:	Tuple    							=							num_attention_heads

								# for backward compatibility
								if num_key_value_heads is None:
													__magic_name__							:	Optional[int]    							=							num_attention_heads

								__magic_name__							:	Optional[int]    							=							num_key_value_heads
								__magic_name__							:	List[str]    							=							hidden_act
								__magic_name__							:	List[str]    							=							initializer_range
								__magic_name__							:	Tuple    							=							rms_norm_eps
								__magic_name__							:	Optional[int]    							=							pretraining_tp
								__magic_name__							:	Any    							=							use_cache
								__magic_name__							:	int    							=							rope_scaling
								self._rope_scaling_validation()

								super().__init__(
								    pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						tie_word_embeddings=lowerCamelCase_       ,						**lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   str  )						->   Tuple:
								if self.rope_scaling is None:
													return

								if not isinstance(self.rope_scaling       ,						lowerCamelCase_  ) or len(self.rope_scaling  ) != 2:
													raise ValueError(
													    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
													    F'''got {self.rope_scaling}'''  )
								__magic_name__							:	List[str]    							=							self.rope_scaling.get('''type'''       ,						lowerCamelCase_  )
								__magic_name__							:	str    							=							self.rope_scaling.get('''factor'''       ,						lowerCamelCase_  )
								if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''  )
								if rope_scaling_factor is None or not isinstance(lowerCamelCase_       ,						lowerCamelCase_  ) or rope_scaling_factor <= 1.0:
													raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''  )



