






"""simple docstring"""




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

import numpy as np
import torch

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




@dataclass
class 			UpperCamelCase_				(      snake_case_    ):



	'''simple docstring'''




	lowerCAmelCase													=			4_2
	lowerCAmelCase													=			4_2






def 						__UpperCAmelCase      (					a_							,					a_=0.9_99							,					a_="cosine"							,					):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(a_):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2) ** 2

							elif alpha_transform_type == "exp":

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

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

							snake_case_           =							[]
							for i in range(a_):
														snake_case_           =							i / num_diffusion_timesteps
														snake_case_           =							(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 			UpperCamelCase_				(      snake_case_	, snake_case_    ):



	'''simple docstring'''




	lowerCAmelCase													=			1




	@register_to_config
	def __init__(	self	,      a = 10_00	,      a = 0.0_001	,      a = 0.02	,      a = "linear"	,      a = 0.0	,      a = None	,      a = True	,      )	->   Union[str, Any]:
								if trained_betas is not None:
															snake_case_           =							torch.from_numpy(a )
								elif beta_schedule == "linear":
															snake_case_           =							torch.linspace(a	,      a	,      a	,      dtype=torch.floataa )
								elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															snake_case_           =							(
															    torch.linspace(beta_start**0.5	,      beta_end**0.5	,      a	,      dtype=torch.floataa ) ** 2
															)
								elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															snake_case_           =							betas_for_alpha_bar(a )
								elif beta_schedule == "sigmoid":
															# GeoDiff sigmoid schedule
															snake_case_           =							torch.linspace(-6	,      6	,      a )
															snake_case_           =							torch.sigmoid(a ) * (beta_end - beta_start) + beta_start
								else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

								snake_case_           =							torch.tensor(1.0 )

								# standard deviation of the initial noise distribution
								snake_case_           =							1.0

								# setable values
								snake_case_           =							None
								snake_case_           =							torch.from_numpy(np.arange(0	,      a )[::-1].copy() )

								snake_case_           =							eta




	def 							_UpperCamelCase      (	self	,      a	,      a = None )	->   torch.FloatTensor:
								return sample




	def 							_UpperCamelCase      (	self	,      a	,      a = 10	,      a = 10	,      a = None	,      )	->   List[Any]:
								snake_case_           =							min(self.config.num_train_timesteps	,      a )
								snake_case_           =							num_inference_steps

								snake_case_           =							[]

								snake_case_           =							{}
								for j in range(0	,      num_inference_steps - jump_length	,      a ):
															snake_case_           =							jump_n_sample - 1

								snake_case_           =							num_inference_steps
								while t >= 1:
															snake_case_           =							t - 1
															timesteps.append(a )

															if jumps.get(a	,      0 ) > 0:
																						snake_case_           =							jumps[t] - 1
																						for _ in range(a ):
																													snake_case_           =							t + 1
																													timesteps.append(a )

								snake_case_           =							np.array(a ) * (self.config.num_train_timesteps // self.num_inference_steps)
								snake_case_           =							torch.from_numpy(a ).to(a )




	def 							_UpperCamelCase      (	self	,      a )	->   int:
								snake_case_           =							t - self.config.num_train_timesteps // self.num_inference_steps

								snake_case_           =							self.alphas_cumprod[t]
								snake_case_           =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								snake_case_           =							1 - alpha_prod_t
								snake_case_           =							1 - alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from
								# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
								# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
								# variance to pred_sample
								# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
								# without eta.
								# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
								snake_case_           =							(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a	,      a	,      a = None	,      a = True	,      )	->   Union[RePaintSchedulerOutput, Tuple]:
								snake_case_           =							timestep
								snake_case_           =							timestep - self.config.num_train_timesteps // self.num_inference_steps

								# 1. compute alphas, betas
								snake_case_           =							self.alphas_cumprod[t]
								snake_case_           =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								snake_case_           =							1 - alpha_prod_t

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								snake_case_           =							(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
															snake_case_           =							torch.clamp(a	,      -1	,      1 )

								# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
								# substitute formula (7) in the algorithm coming from DDPM paper
								# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
								# DDIM schedule gives the same results as DDPM with eta = 1.0
								# Noise is being reused in 7. and 8., but no impact on quality has
								# been observed.

								# 5. Add noise
								snake_case_           =							model_output.device
								snake_case_           =							randn_tensor(model_output.shape	,      generator=a	,      device=a	,      dtype=model_output.dtype )
								snake_case_           =							self.eta * self._get_variance(a ) ** 0.5

								snake_case_           =							0
								if t > 0 and self.eta > 0:
															snake_case_           =							std_dev_t * noise

								# 6. compute "direction pointing to x_t" of formula (12)
								# from https://arxiv.org/pdf/2010.02502.pdf
								snake_case_           =							(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

								# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								snake_case_           =							alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

								# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
								snake_case_           =							(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

								# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
								snake_case_           =							mask * prev_known_part + (1.0 - mask) * prev_unknown_part

								if not return_dict:
															return (
															    pred_prev_sample,
															    pred_original_sample,
															)

								return RePaintSchedulerOutput(prev_sample=a	,      pred_original_sample=a )




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a=None )	->   int:
								snake_case_           =							self.config.num_train_timesteps // self.num_inference_steps

								for i in range(a ):
															snake_case_           =							self.betas[timestep + i]
															if sample.device.type == "mps":
																						# randn does not work reproducibly on mps
																						snake_case_           =							randn_tensor(sample.shape	,      dtype=sample.dtype	,      generator=a )
																						snake_case_           =							noise.to(sample.device )
															else:
																						snake_case_           =							randn_tensor(sample.shape	,      generator=a	,      device=sample.device	,      dtype=sample.dtype )

															# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
															snake_case_           =							(1 - beta) ** 0.5 * sample + beta**0.5 * noise

								return sample




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      )	->   torch.FloatTensor:
								raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.' )






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













import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast





def 						__UpperCAmelCase      (					a_							,					a_=1_00							,					a_=" "):
							snake_case_           =							text.split(a_)
							return [character.join(text[i : i + n]).strip() for i in range(0							,					len(a_)							,					a_)]





def 						__UpperCAmelCase      (					a_):
							snake_case_      ,		snake_case_           =							[], []
							for title, text in zip(documents['title']							,					documents['text']):
														if text is not None:
																					for passage in split_text(a_):
																												titles.append(title if title is not None else '')
																												texts.append(a_)
							return {"title": titles, "text": texts}





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_           =							load_dataset(
							    'csv'							,					data_files=[csv_path]							,					split='train'							,					delimiter='\t'							,					column_names=['title', 'text'])
							snake_case_           =							kb_dataset.map(
							    a_							,					batched=a_							,					num_proc=1)  # if you want you can load already splitted csv.
							snake_case_           =							[kb_dataset.shard(a_							,					a_							,					contiguous=a_) for i in range(a_)]
							snake_case_           =							kb_list[process_num]

							snake_case_           =							'data_' + str(a_)
							snake_case_           =							os.path.join(a_							,					a_)

							snake_case_           =							DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base')
							snake_case_           =							ctx_encoder.to(device=a_)

							def embed(
							    a_							,					a_							,					a_							,					a_) -> dict:
														snake_case_           =							ctx_tokenizer(
														    documents['title']							,					documents['text']							,					truncation=a_							,					padding='longest'							,					return_tensors='pt')['input_ids']
														snake_case_           =							ctx_encoder(input_ids.to(device=a_)							,					return_dict=a_).pooler_output
														return {"embeddings": embeddings.detach().cpu().numpy()}

							snake_case_           =							Features(
							    {'text': Value('string'), 'title': Value('string'), 'embeddings': Sequence(Value('float32'))})  # optional, save as float32 instead of float64 to save space

							snake_case_           =							data_shrad.map(
							    partial(a_							,					ctx_encoder=a_							,					ctx_tokenizer=a_							,					device=a_)							,					batched=a_							,					batch_size=16							,					features=a_							,					)
							dataset.save_to_disk(a_)





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							[]

							for shard_address in glob(str(a_) + '/*/'):
														data_shard_list.append(load_from_disk(a_))

							snake_case_           =							concatenate_datasets(a_)
							faiss.omp_set_num_threads(96)

							snake_case_           =							faiss.IndexHNSWFlat(7_68							,					1_28							,					faiss.METRIC_INNER_PRODUCT)
							concat.add_faiss_index('embeddings'							,					custom_index=a_)
							concat.get_index('embeddings').save(
							    a_)  # since we load the index in to memory,we can directly update the index in the disk





