# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class    lowercase_    :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42

							# setable values
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		None



							@classmethod
							def        UpperCamelCase       (						cls      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													return cls(
													    common=lowercase_      ,				final_alpha_cumprod=lowercase_      ,				init_noise_sigma=lowercase_      ,				timesteps=lowercase_      ,				)




@dataclass
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		42




class    lowercase_    (     __snake_case						, __snake_case		):
							_lowerCamelCase						=		[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowerCamelCase						=		42



							@property
							def        UpperCamelCase       (						self				):
													return True



							@register_to_config
							def __init__(						self      ,				lowercase_ = 1_000      ,				lowercase_ = 0.0_001      ,				lowercase_ = 0.02      ,				lowercase_ = "linear"      ,				lowercase_ = None      ,				lowercase_ = True      ,				lowercase_ = 0      ,				lowercase_ = "epsilon"      ,				lowercase_ = jnp.floataa      ,				):
													_snake_case						:     List[str]				=    dtype



							def        UpperCamelCase       (						self      ,				lowercase_ = None				):
													if common is None:
																			_snake_case						:     Any				=    CommonSchedulerState.create(self				)

													# At every step in ddim, we are looking into the previous alphas_cumprod
													# For the final step, there is no previous alphas_cumprod because we are already at 0
													# `set_alpha_to_one` decides whether we set this parameter simply to one or
													# whether we use the final alpha of the "non-previous" one.
													_snake_case						:     Union[str, Any]				=    (
													    jnp.array(1.0      ,				dtype=self.dtype				) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
													)

													# standard deviation of the initial noise distribution
													_snake_case						:     Optional[Any]				=    jnp.array(1.0      ,				dtype=self.dtype				)

													_snake_case						:     Dict				=    jnp.arange(0      ,				self.config.num_train_timesteps				).round()[::-1]

													return DDIMSchedulerState.create(
													    common=lowercase_      ,				final_alpha_cumprod=lowercase_      ,				init_noise_sigma=lowercase_      ,				timesteps=lowercase_      ,				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_ = None				):
													return sample



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_ = ()				):
													_snake_case						:     int				=    self.config.num_train_timesteps // num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# rounding to avoid issues when num_inference_step is power of 3
													_snake_case						:     Optional[Any]				=    (jnp.arange(0      ,				lowercase_				) * step_ratio).round()[::-1] + self.config.steps_offset

													return state.replace(
													    num_inference_steps=lowercase_      ,				timesteps=lowercase_      ,				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     str				=    state.common.alphas_cumprod[timestep]
													_snake_case						:     int				=    jnp.where(
													    prev_timestep >= 0      ,				state.common.alphas_cumprod[prev_timestep]      ,				state.final_alpha_cumprod				)
													_snake_case						:     Optional[int]				=    1 - alpha_prod_t
													_snake_case						:     List[str]				=    1 - alpha_prod_t_prev

													_snake_case						:     List[Any]				=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

													return variance



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_ = 0.0      ,				lowercase_ = True      ,				):
													if state.num_inference_steps is None:
																			raise ValueError(
																			    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"				)

													# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
													# Ideally, read DDIM paper in-detail understanding

													# Notation (<variable name> -> <name in paper>
													# - pred_noise_t -> e_theta(x_t, t)
													# - pred_original_sample -> f_theta(x_t, t) or x_0
													# - std_dev_t -> sigma_t
													# - eta -> η
													# - pred_sample_direction -> "direction pointing to x_t"
													# - pred_prev_sample -> "x_t-1"

													# 1. get previous step value (=t-1)
													_snake_case						:     Union[str, Any]				=    timestep - self.config.num_train_timesteps // state.num_inference_steps

													_snake_case						:     Tuple				=    state.common.alphas_cumprod
													_snake_case						:     Optional[Any]				=    state.final_alpha_cumprod

													# 2. compute alphas, betas
													_snake_case						:     Dict				=    alphas_cumprod[timestep]
													_snake_case						:     List[Any]				=    jnp.where(prev_timestep >= 0      ,				alphas_cumprod[prev_timestep]      ,				lowercase_				)

													_snake_case						:     int				=    1 - alpha_prod_t

													# 3. compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													if self.config.prediction_type == "epsilon":
																			_snake_case						:     Tuple				=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																			_snake_case						:     Optional[int]				=    model_output
													elif self.config.prediction_type == "sample":
																			_snake_case						:     Optional[Any]				=    model_output
																			_snake_case						:     List[str]				=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
													elif self.config.prediction_type == "v_prediction":
																			_snake_case						:     Dict				=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																			_snake_case						:     Optional[Any]				=    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																			    " `v_prediction`"				)

													# 4. compute variance: "sigma_t(η)" -> see formula (16)
													# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
													_snake_case						:     int				=    self._get_variance(lowercase_      ,				lowercase_      ,				lowercase_				)
													_snake_case						:     Any				=    eta * variance ** 0.5

													# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													_snake_case						:     Union[str, Any]				=    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

													# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													_snake_case						:     Optional[int]				=    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

													if not return_dict:
																			return (prev_sample, state)

													return FlaxDDIMSchedulerOutput(prev_sample=lowercase_      ,				state=lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													return add_noise_common(state.common      ,				lowercase_      ,				lowercase_      ,				lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													return get_velocity_common(state.common      ,				lowercase_      ,				lowercase_      ,				lowercase_				)



							def __len__(						self				):
													return self.config.num_train_timesteps
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


__SCREAMING_SNAKE_CASE						:    Optional[Any]     = logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE						:    Any     = 'cuda' if torch.cuda.is_available() else 'cpu'
def 					snake_case						(__lowercase					,			__lowercase=100					,			__lowercase=" "						)       ->       List[str]:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    text.split(__lowercase						)
						return [character.join(text[i : i + n]						).strip() for i in range(0					,			len(__lowercase						)					,			__lowercase						)]
def 					snake_case						(__lowercase						)       ->       dict:

						'''simple docstring'''



						_snake_case				,_snake_case						:     str				=    [], []
						for title, text in zip(documents["title"]					,			documents["text"]						):
												if text is not None:
																		for passage in split_text(__lowercase						):
																								titles.append(title if title is not None else ""						)
																								texts.append(__lowercase						)
						return {"title": titles, "text": texts}
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       dict:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    ctx_tokenizer(
						    documents["title"]					,			documents["text"]					,			truncation=__lowercase					,			padding="longest"					,			return_tensors="pt"						)["input_ids"]
						_snake_case						:     Tuple				=    ctx_encoder(input_ids.to(device=__lowercase						)					,			return_dict=__lowercase						).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			)       ->       str:

						'''simple docstring'''



						logger.info("Step 1 - Create the dataset"						)
						######################################

						# The dataset needed for RAG must have three columns:
						# - title (string): title of the document
						# - text (string): text of a passage of the document
						# - embeddings (array of dimension d): DPR representation of the passage

						# Let's say you have documents in tab-separated csv files with columns "title" and "text"
						assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

						# You can load a Dataset object this way
						_snake_case						:     Dict				=    load_dataset(
						    "csv"					,			data_files=[rag_example_args.csv_path]					,			split="train"					,			delimiter="\t"					,			column_names=["title", "text"]						)

						# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

						# Then split the documents into passages of 100 words
						_snake_case						:     Optional[int]				=    dataset.map(__lowercase					,			batched=__lowercase					,			num_proc=processing_args.num_proc						)

						# And compute the embeddings
						_snake_case						:     Optional[int]				=    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__lowercase						)
						_snake_case						:     int				=    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
						_snake_case						:     Union[str, Any]				=    Features(
						    {"text": Value("string"						), "title": Value("string"						), "embeddings": Sequence(Value("float32"						)						)}						)  # optional, save as float32 instead of float64 to save space
						_snake_case						:     Union[str, Any]				=    dataset.map(
						    partial(__lowercase					,			ctx_encoder=__lowercase					,			ctx_tokenizer=__lowercase						)					,			batched=__lowercase					,			batch_size=processing_args.batch_size					,			features=__lowercase					,			)

						# And finally save your dataset
						_snake_case						:     Dict				=    os.path.join(rag_example_args.output_dir					,			"my_knowledge_dataset"						)
						dataset.save_to_disk(__lowercase						)
						# from datasets import load_from_disk
						# dataset = load_from_disk(passages_path)  # to reload the dataset

						######################################
						logger.info("Step 2 - Index the dataset"						)
						######################################

						# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
						_snake_case						:     Union[str, Any]				=    faiss.IndexHNSWFlat(index_hnsw_args.d					,			index_hnsw_args.m					,			faiss.METRIC_INNER_PRODUCT						)
						dataset.add_faiss_index("embeddings"					,			custom_index=__lowercase						)

						# And save the index
						_snake_case						:     Any				=    os.path.join(rag_example_args.output_dir					,			"my_knowledge_dataset_hnsw_index.faiss"						)
						dataset.get_index("embeddings"						).save(__lowercase						)
						# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

						######################################
						logger.info("Step 3 - Load RAG"						)
						######################################

						# Easy way to load the model
						_snake_case						:     Optional[int]				=    RagRetriever.from_pretrained(
						    rag_example_args.rag_model_name					,			index_name="custom"					,			indexed_dataset=__lowercase						)
						_snake_case						:     List[Any]				=    RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name					,			retriever=__lowercase						)
						_snake_case						:     Optional[Any]				=    RagTokenizer.from_pretrained(rag_example_args.rag_model_name						)

						# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
						# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

						######################################
						logger.info("Step 4 - Have fun"						)
						######################################

						_snake_case						:     Optional[Any]				=    rag_example_args.question or "What does Moses' rod turn into ?"
						_snake_case						:     Optional[int]				=    tokenizer.question_encoder(__lowercase					,			return_tensors="pt"						)["input_ids"]
						_snake_case						:     Union[str, Any]				=    model.generate(__lowercase						)
						_snake_case						:     int				=    tokenizer.batch_decode(__lowercase					,			skip_special_tokens=__lowercase						)[0]
						logger.info("Q: " + question						)
						logger.info("A: " + generated_string						)



@dataclass
class    lowercase_    :
							_lowerCamelCase						=		field(
							    default=str(Path(__snake_case		).parent / 'test_data' / 'my_knowledge_dataset.csv'		)						, metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}						, )
							_lowerCamelCase						=		field(
							    default=__snake_case						, metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}						, )
							_lowerCamelCase						=		field(
							    default='facebook/rag-sequence-nq'						, metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}						, )
							_lowerCamelCase						=		field(
							    default='facebook/dpr-ctx_encoder-multiset-base'						, metadata={
							        'help': (
							            'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
							            ' \'facebook/dpr-ctx_encoder-multiset-base\''
							        )
							    }						, )
							_lowerCamelCase						=		field(
							    default=__snake_case						, metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}						, )




@dataclass
class    lowercase_    :
							_lowerCamelCase						=		field(
							    default=__snake_case						, metadata={
							        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
							    }						, )
							_lowerCamelCase						=		field(
							    default=16						, metadata={
							        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
							    }						, )




@dataclass
class    lowercase_    :
							_lowerCamelCase						=		field(
							    default=768						, metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						, )
							_lowerCamelCase						=		field(
							    default=128						, metadata={
							        'help': (
							            'The number of bi-directional links created for every new element during the HNSW index construction.'
							        )
							    }						, )


if __name__ == "__main__":
				logging.basicConfig(level=logging.WARNING)
				logger.setLevel(logging.INFO)

				__SCREAMING_SNAKE_CASE						:    List[str]     = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
				__SCREAMING_SNAKE_CASE   ,__SCREAMING_SNAKE_CASE   ,__SCREAMING_SNAKE_CASE						:    int     = parser.parse_args_into_dataclasses()
				with TemporaryDirectory() as tmp_dir:
								__SCREAMING_SNAKE_CASE						:    int     = rag_example_args.output_dir or tmp_dir
								main(rag_example_args, processing_args, index_hnsw_args)