


'''simple docstring'''
from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							for param, grad_param in zip(model_a.parameters()						,  model_b.parameters() ):
														if not param.requires_grad:
																					continue
														if not did_step:
																					# Grads should not be in sync
																					assert (
																					    torch.allclose(param.grad						,  grad_param.grad ) is False
																					), f"Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"
														else:
																					# Grads should be in sync
																					assert (
																					    torch.allclose(param.grad						,  grad_param.grad ) is True
																					), f"Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=True ):
							model.train()
							A_			    =		model(UpperCamelCase_ )
							A_			    =		F.mse_loss(UpperCamelCase_						,  target.to(output.device ) )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=False ):
							set_seed(4_2 )
							A_			    =		RegressionModel()
							A_			    =		deepcopy(UpperCamelCase_ )
							A_			    =		RegressionDataset(length=8_0 )
							A_			    =		DataLoader(UpperCamelCase_						,  batch_size=1_6 )
							model.to(accelerator.device )
							if sched:
														A_			    =		AdamW(params=model.parameters()						,  lr=1e-3 )
														A_			    =		AdamW(params=ddp_model.parameters()						,  lr=1e-3 )
														A_			    =		LambdaLR(UpperCamelCase_						,  lr_lambda=lambda UpperCamelCase_ : epoch**0.65 )
														A_			    =		LambdaLR(UpperCamelCase_						,  lr_lambda=lambda UpperCamelCase_ : epoch**0.65 )
							# Make a copy of `model`
							if sched:
														A_    ,					A_    ,					A_    ,					A_			    =		accelerator.prepare(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
							else:
														A_    ,					A_			    =		accelerator.prepare(UpperCamelCase_						,  UpperCamelCase_ )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader







def       a_				(	UpperCamelCase_ ):
							# Test when on a single CPU or GPU that the context manager does nothing
							A_    ,					A_    ,					A_			    =		get_training_setup(UpperCamelCase_ )
							# Use a single batch
							A_    ,					A_			    =		next(iter(UpperCamelCase_ ) ).values()
							for iteration in range(3 ):
														# Gather the distributed inputs and targs for the base model
														A_    ,					A_			    =		accelerator.gather((ddp_input, ddp_target) )
														A_    ,					A_			    =		input.to(accelerator.device ), target.to(accelerator.device )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase_ ):
																												step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
														else:
																					# Sync grads
																					step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
														for param, ddp_param in zip(model.parameters()						,  ddp_model.parameters() ):
																					if not param.requires_grad:
																												continue
																					assert torch.allclose(
																					    param.grad						,  ddp_param.grad ), f"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration )
														A_			    =		ddp_input[torch.randperm(len(UpperCamelCase_ ) )]







def       a_				(	UpperCamelCase_ ):
							# Test on distributed setup that context manager behaves properly
							A_    ,					A_    ,					A_			    =		get_training_setup(UpperCamelCase_ )
							# Use a single batch
							A_    ,					A_			    =		next(iter(UpperCamelCase_ ) ).values()
							for iteration in range(3 ):
														# Gather the distributed inputs and targs for the base model
														A_    ,					A_			    =		accelerator.gather((ddp_input, ddp_target) )
														A_    ,					A_			    =		input.to(accelerator.device ), target.to(accelerator.device )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase_ ):
																												step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
														else:
																					# Sync grads
																					step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()						,  ddp_model.parameters() ):
																					if not param.requires_grad:
																												continue
																					if iteration % 2 == 0:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad						,  ddp_param.grad ) is False
																												), f"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
																					else:
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad						,  ddp_param.grad ) is True
																												), f"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration )
														A_			    =		ddp_input[torch.randperm(len(UpperCamelCase_ ) )]







def       a_				(	UpperCamelCase_=False						,  UpperCamelCase_=False ):
							A_			    =		Accelerator(
							    split_batches=UpperCamelCase_						,  dispatch_batches=UpperCamelCase_						,  gradient_accumulation_steps=2 )
							# Test that context manager behaves properly
							A_    ,					A_    ,					A_			    =		get_training_setup(UpperCamelCase_ )
							for iteration, batch in enumerate(UpperCamelCase_ ):
														A_    ,					A_			    =		batch.values()
														# Gather the distributed inputs and targs for the base model
														A_    ,					A_			    =		accelerator.gather((ddp_input, ddp_target) )
														A_    ,					A_			    =		input.to(accelerator.device ), target.to(accelerator.device )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(UpperCamelCase_ ):
																					step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()						,  ddp_model.parameters() ):
																					if not param.requires_grad:
																												continue
																					if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase_ ) - 1):
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad						,  ddp_param.grad ) is True
																												), f"Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
																					else:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad						,  ddp_param.grad ) is False
																												), f"Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration )
														A_			    =		ddp_input[torch.randperm(len(UpperCamelCase_ ) )]
							GradientState._reset_state()







def       a_				(	UpperCamelCase_=False						,  UpperCamelCase_=False ):
							A_			    =		Accelerator(
							    split_batches=UpperCamelCase_						,  dispatch_batches=UpperCamelCase_						,  gradient_accumulation_steps=2 )
							# Test that context manager behaves properly
							A_    ,					A_    ,					A_    ,					A_    ,					A_    ,					A_    ,					A_			    =		get_training_setup(UpperCamelCase_						,  UpperCamelCase_ )
							for iteration, batch in enumerate(UpperCamelCase_ ):
														A_    ,					A_			    =		batch.values()
														# Gather the distributed inputs and targs for the base model
														A_    ,					A_			    =		accelerator.gather((ddp_input, ddp_target) )
														A_    ,					A_			    =		input.to(accelerator.device ), target.to(accelerator.device )
														# Perform our initial ground truth step in non "DDP"
														model.train()
														ddp_model.train()
														step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
														opt.step()

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase_ )):
																					if split_batches:
																												sched.step()
																					else:
																												for _ in range(accelerator.num_processes ):
																																			sched.step()
														opt.zero_grad()
														# Perform gradient accumulation under wrapper
														with accelerator.accumulate(UpperCamelCase_ ):
																					step_model(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
																					ddp_opt.step()
																					ddp_sched.step()
																					ddp_opt.zero_grad()

														# Learning rates should be the same
														assert (
														    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
														), f"Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"
														A_			    =		(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase_ ))
														if accelerator.num_processes > 1:
																					check_model_parameters(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration )
							GradientState._reset_state()







def       a_				(	):
							A_			    =		Accelerator()

							A_			    =		RegressionDataset(length=8_0 )
							A_			    =		DataLoader(UpperCamelCase_						,  batch_size=1_6 )
							A_			    =		RegressionDataset(length=9_6 )
							A_			    =		DataLoader(UpperCamelCase_						,  batch_size=1_6 )
							A_    ,					A_			    =		accelerator.prepare(UpperCamelCase_						,  UpperCamelCase_ )
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(UpperCamelCase_ ):
														assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCamelCase_ )
														if iteration < len(UpperCamelCase_ ) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(UpperCamelCase_ ):
																																			assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCamelCase_ )
																																			if batch_num < len(UpperCamelCase_ ) - 1:
																																										assert not accelerator.gradient_state.end_of_dataloader
																																			else:
																																										assert accelerator.gradient_state.end_of_dataloader
														else:
																					assert accelerator.gradient_state.end_of_dataloader
							assert accelerator.gradient_state.active_dataloader is None







def       a_				(	):
							A_			    =		Accelerator()
							A_			    =		accelerator.state
							if state.local_process_index == 0:
														print("**Test `accumulate` gradient accumulation with dataloader break**" )
							test_dataloader_break()
							if state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print("**Test NOOP `no_sync` context manager**" )
														test_noop_sync(UpperCamelCase_ )
							if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
														if state.local_process_index == 0:
																					print("**Test Distributed `no_sync` context manager**" )
														test_distributed_sync(UpperCamelCase_ )
							if state.distributed_type == DistributedType.MULTI_GPU:
														for split_batch in [True, False]:
																					for dispatch_batches in [True, False]:
																												if state.local_process_index == 0:
																																			print(
																																			    "**Test `accumulate` gradient accumulation, "						,  f"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"						,  )
																												test_gradient_accumulation(UpperCamelCase_						,  UpperCamelCase_ )

    # Currently will break on torch 2.0 +, need to investigate why
							if is_torch_version("<"						,  "2.0" ) or state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print(
																					    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "						,  "`split_batches=False`, `dispatch_batches=False`**"						,  )
														test_gradient_accumulation_with_opt_and_scheduler()
														if state.distributed_type == DistributedType.MULTI_GPU:
																					for split_batch in [True, False]:
																												for dispatch_batches in [True, False]:
																																			if not split_batch and not dispatch_batches:
																																										continue
																																			if state.local_process_index == 0:
																																										print(
																																										    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "						,  f"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"						,  )
																																			test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase_						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_ ):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()









'''simple docstring'''
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


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE					:				Dict									=		'''cuda''' if torch.cuda.is_available() else '''cpu'''







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0_0						,  UpperCamelCase_=" " ):
							A_			    =		text.split(UpperCamelCase_ )
							return [character.join(text[i : i + n] ).strip() for i in range(0						,  len(UpperCamelCase_ )						,  UpperCamelCase_ )]







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







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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  ):
							######################################
							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
							A_			    =		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
							A_			    =		dataset.map(UpperCamelCase_						,  batched=UpperCamelCase_						,  num_proc=processing_args.num_proc )

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

							# And finally save your dataset
							A_			    =		os.path.join(rag_example_args.output_dir						,  "my_knowledge_dataset" )
							dataset.save_to_disk(UpperCamelCase_ )
							# 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
							A_			    =		faiss.IndexHNSWFlat(index_hnsw_args.d						,  index_hnsw_args.m						,  faiss.METRIC_INNER_PRODUCT )
							dataset.add_faiss_index("embeddings"						,  custom_index=UpperCamelCase_ )

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






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       str   						=field(
						    default=str(Path(lowercase						).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"						)				,			metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}				,			)
						_UpperCAmelCase		:       Optional[str]   						=field(
						    default=lowercase				,			metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}				,			)
						_UpperCAmelCase		:       str   						=field(
						    default="facebook/rag-sequence-nq"				,			metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}				,			)
						_UpperCAmelCase		:       str   						=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'"
						        )
						    }				,			)
						_UpperCAmelCase		:       Optional[str]   						=field(
						    default=str(Path(lowercase						).parent / "test_run" / "dummy-kb"						)				,			metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}				,			)






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[int]   						=field(
						    default=lowercase				,			metadata={
						        "help": "The number of processes to use to split the documents into passages. Default is single process."
						    }				,			)
						_UpperCAmelCase		:       int   						=field(
						    default=16				,			metadata={
						        "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
						    }				,			)






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       int   						=field(
						    default=768				,			metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}				,			)
						_UpperCAmelCase		:       int   						=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					:				Dict									=		parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														__SCREAMING_SNAKE_CASE					:				List[str]									=		rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





