import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			torch.FloatTensor
			__a  :			Optional[torch.FloatTensor]      			=						None





def     UpperCAmelCase						(   lowercase							,      lowercase=0.999							,      lowercase="cosine"							,      ):







				"""simple docstring"""



				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowercase							):
												return math.exp(t * -12.0							)

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

				__lowercase			      =							[]
				for i in range(lowercase							):
								__lowercase			      =							i / num_diffusion_timesteps
								__lowercase			      =							(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowercase							) / alpha_bar_fn(lowercase							)							,      lowercase							)							)
				return torch.tensor(lowercase							,      dtype=torch.floataa							)




class        _UpperCamelCase				(						_UpperCAmelCase						,_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			Dict      			=						[e.name for e in KarrasDiffusionSchedulers]
			__a  :			List[Any]      			=						1





			@register_to_config
			def __init__(       self				,       lowerCAmelCase__ = 10_00				,       lowerCAmelCase__ = 0.0001				,       lowerCAmelCase__ = 0.02				,       lowerCAmelCase__ = "linear"				,       lowerCAmelCase__ = None				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = "epsilon"				,       lowerCAmelCase__ = "linspace"				,       lowerCAmelCase__ = 0				,       )					-> List[Any]:

							'''simple docstring'''


							if trained_betas is not None:
											__lowercase			      =							torch.tensor(lowerCAmelCase__				,       dtype=torch.floataa						)
							elif beta_schedule == "linear":
											__lowercase			      =							torch.linspace(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       dtype=torch.floataa						)
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											__lowercase			      =							(
											    torch.linspace(beta_start**0.5				,       beta_end**0.5				,       lowerCAmelCase__				,       dtype=torch.floataa						) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											__lowercase			      =							betas_for_alpha_bar(lowerCAmelCase__						)
							else:
											raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"						)

							__lowercase			      =							1.0 - self.betas
							__lowercase			      =							torch.cumprod(self.alphas				,       dim=0						)

							__lowercase			      =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
							__lowercase			      =							np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
							__lowercase			      =							torch.from_numpy(lowerCAmelCase__						)

							# setable values
							__lowercase			      =							None
							__lowercase			      =							use_karras_sigmas
							self.set_timesteps(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							[]
							__lowercase			      =							False





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[int]:

							'''simple docstring'''


							if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

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





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> torch.FloatTensor:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       torch.Tensor						):
											__lowercase			      =							timestep.to(self.timesteps.device						)
							__lowercase			      =							(self.timesteps == timestep).nonzero().item()
							__lowercase			      =							self.sigmas[step_index]
							__lowercase			      =							sample / ((sigma**2 + 1) ** 0.5)
							__lowercase			      =							True
							return sample





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							def lms_derivative(lowerCAmelCase__						):
											__lowercase			      =							1.0
											for k in range(lowerCAmelCase__						):
															if current_order == k:
																			continue
															prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
											return prod

							__lowercase			      =							integrate.quad(lowerCAmelCase__				,       self.sigmas[t]				,       self.sigmas[t + 1]				,       epsrel=1E-4						)[0]

							return integrated_coeff





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = None						)					-> str:

							'''simple docstring'''


							__lowercase			      =							num_inference_steps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
											__lowercase			      =							np.linspace(0				,       self.config.num_train_timesteps - 1				,       lowerCAmelCase__				,       dtype=lowerCAmelCase__						)[
											    ::-1
											].copy()
							elif self.config.timestep_spacing == "leading":
											__lowercase			      =							self.config.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
											__lowercase			      =							(np.arange(0				,       lowerCAmelCase__						) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__						)
											timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
											__lowercase			      =							self.config.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
											__lowercase			      =							(np.arange(self.config.num_train_timesteps				,       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'."						)

							__lowercase			      =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
							__lowercase			      =							np.log(lowerCAmelCase__						)
							__lowercase			      =							np.interp(lowerCAmelCase__				,       np.arange(0				,       len(lowerCAmelCase__						)						)				,       lowerCAmelCase__						)

							if self.use_karras_sigmas:
											__lowercase			      =							self._convert_to_karras(in_sigmas=lowerCAmelCase__						)
											__lowercase			      =							np.array([self._sigma_to_t(lowerCAmelCase__				,       lowerCAmelCase__						) for sigma in sigmas]						)

							__lowercase			      =							np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

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

							__lowercase			      =							[]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							np.log(lowerCAmelCase__						)

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

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

							__lowercase			      =							log_sigmas[low_idx]
							__lowercase			      =							log_sigmas[high_idx]

							# interpolate sigmas
							__lowercase			      =							(low - log_sigma) / (low - high)
							__lowercase			      =							np.clip(lowerCAmelCase__				,       0				,       1						)

							# transform interpolation to time range
							__lowercase			      =							(1 - w) * low_idx + w * high_idx
							__lowercase			      =							t.reshape(sigma.shape						)
							return t





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> torch.FloatTensor:

							'''simple docstring'''


							__lowercase			      =							in_sigmas[-1].item()
							__lowercase			      =							in_sigmas[0].item()

							__lowercase			      =							7.0  # 7.0 is the value used in the paper
							__lowercase			      =							np.linspace(0				,       1				,       self.num_inference_steps						)
							__lowercase			      =							sigma_min ** (1 / rho)
							__lowercase			      =							sigma_max ** (1 / rho)
							__lowercase			      =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__ = 4				,       lowerCAmelCase__ = True				,       )					-> Union[LMSDiscreteSchedulerOutput, Tuple]:

							'''simple docstring'''


							if not self.is_scale_input_called:
											warnings.warn(
											    '''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						):
											__lowercase			      =							timestep.to(self.timesteps.device						)
							__lowercase			      =							(self.timesteps == timestep).nonzero().item()
							__lowercase			      =							self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
											__lowercase			      =							sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											__lowercase			      =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
							elif self.config.prediction_type == "sample":
											__lowercase			      =							model_output
							else:
											raise ValueError(
											    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

							# 2. Convert to an ODE derivative
							__lowercase			      =							(sample - pred_original_sample) / sigma
							self.derivatives.append(lowerCAmelCase__						)
							if len(self.derivatives						) > order:
											self.derivatives.pop(0						)

							# 3. Compute linear multistep coefficients
							__lowercase			      =							min(step_index + 1				,       lowerCAmelCase__						)
							__lowercase			      =							[self.get_lms_coefficient(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						) for curr_order in range(lowerCAmelCase__						)]

							# 4. Compute previous sample based on the derivatives path
							__lowercase			      =							sample + sum(
							    coeff * derivative for coeff, derivative in zip(lowerCAmelCase__				,       reversed(self.derivatives						)						)						)

							if not return_dict:
											return (prev_sample,)

							return LMSDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__				,       pred_original_sample=lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       )					-> torch.FloatTensor:

							'''simple docstring'''


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

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

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

							__lowercase			      =							original_samples + noise * sigma
							return noisy_samples





			def __len__(       self						)					-> Dict:

							'''simple docstring'''


							return self.config.num_train_timesteps
import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__a  :					Optional[Any]          =  logging.getLogger(__name__)




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							False





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							if not self.initialized:
											__lowercase			      =							RagRetriever(
											    lowerCAmelCase__				,       question_encoder_tokenizer=lowerCAmelCase__				,       generator_tokenizer=lowerCAmelCase__				,       index=lowerCAmelCase__				,       init_retrieval=lowerCAmelCase__				,       )
											__lowercase			      =							True





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							self.retriever.index.init_index()





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							del self.retriever
							__lowercase			      =							False





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							self.retriever._main_retrieve(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							self.retriever.index.get_doc_dicts(lowerCAmelCase__						)
							return doc_ids, retrieved_doc_embeds, doc_dicts




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> int:

							'''simple docstring'''


							if index is not None and index.is_initialized() and len(lowerCAmelCase__						) > 0:
											raise ValueError(
											    '''When using Ray for distributed fine-tuning, '''
											    '''you\'ll need to provide the paths instead, '''
											    '''as the dataset and the index are loaded '''
											    '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''						)

							super().__init__(
							    lowerCAmelCase__				,       question_encoder_tokenizer=lowerCAmelCase__				,       generator_tokenizer=lowerCAmelCase__				,       index=lowerCAmelCase__				,       init_retrieval=lowerCAmelCase__				,       )

							__lowercase			      =							retrieval_workers
							__lowercase			      =							question_encoder_tokenizer
							__lowercase			      =							generator_tokenizer
							if len(self.retrieval_workers						) > 0:
											ray.get(
											    [
											        worker.create_rag_retriever.remote(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)
											        for worker in self.retrieval_workers
											    ]						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							logger.info('''initializing retrieval'''						)

							if len(self.retrieval_workers						) > 0:
											ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]						)
							else:
											# Non-distributed training. Load index into this same process.
											self.index.init_index()





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

							'''simple docstring'''


							if len(self.retrieval_workers						) > 0:
											# Select a random retrieval actor.
											__lowercase			      =							self.retrieval_workers[random.randint(0				,       len(self.retrieval_workers						) - 1						)]
											__lowercase      ,					__lowercase      ,					__lowercase			      =							ray.get(
											    random_worker.retrieve.remote(lowerCAmelCase__				,       lowerCAmelCase__						)						)
							else:
											__lowercase      ,					__lowercase			      =							self._main_retrieve(lowerCAmelCase__				,       lowerCAmelCase__						)
											__lowercase			      =							self.index.get_doc_dicts(lowerCAmelCase__						)
							return retrieved_doc_embeds, doc_ids, doc_dicts





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							return super(lowerCAmelCase__				,       cls						).get_tokenizers(lowerCAmelCase__				,       lowerCAmelCase__				,       **lowerCAmelCase__						)





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							kwargs.pop('''config'''				,       lowerCAmelCase__						) or RagConfig.from_pretrained(lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							RagTokenizer.from_pretrained(lowerCAmelCase__				,       config=lowerCAmelCase__						)
							__lowercase			      =							rag_tokenizer.question_encoder
							__lowercase			      =							rag_tokenizer.generator

							if indexed_dataset is not None:
											__lowercase			      =							'''custom'''
											__lowercase			      =							CustomHFIndex(config.retrieval_vector_size				,       lowerCAmelCase__						)
							else:
											__lowercase			      =							cls._build_index(lowerCAmelCase__						)

							return cls(
							    lowerCAmelCase__				,       question_encoder_tokenizer=lowerCAmelCase__				,       generator_tokenizer=lowerCAmelCase__				,       retrieval_workers=lowerCAmelCase__				,       index=lowerCAmelCase__				,       )





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							logger.info('''re-loading the new dataset with embeddings'''						)
							# access from the training loop

							ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]						)

							# build the index object again
							__lowercase			      =							self._build_index(self.config						)

							ray.get(
							    [
							        worker.create_rag_retriever.remote(
							            self.config				,       self.question_encoder_tokenizer				,       self.generator_tokenizer				,       lowerCAmelCase__						)
							        for worker in self.retrieval_workers
							    ]						)