
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 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				Tuple=100   ,							SCREAMING_SNAKE_CASE	:				str=" "      )					->						Union[str, Any]:
				__lowercase								= text.split(SCREAMING_SNAKE_CASE      )
				return [character.join(text[i : i + n]      ).strip() for i in range(0   ,							len(SCREAMING_SNAKE_CASE      )   ,							SCREAMING_SNAKE_CASE      )]




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Union[str, Any]      )					->						Optional[Any]:
				__lowercase		, __lowercase								= [], []
				for title, text in zip(documents['title']   ,							documents['text']      ):
								if text is not None:
												for passage in split_text(SCREAMING_SNAKE_CASE      ):
																titles.append(title if title is not None else ''      )
																texts.append(SCREAMING_SNAKE_CASE      )
				return {"title": titles, "text": texts}




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Union[str, Any]   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]   ,							SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				str      )					->						int:
				__lowercase								= load_dataset(
				    'csv'   ,							data_files=[csv_path]   ,							split='train'   ,							delimiter='\t'   ,							column_names=['title', 'text']      )
				__lowercase								= kb_dataset.map(
				    SCREAMING_SNAKE_CASE   ,							batched=SCREAMING_SNAKE_CASE   ,							num_proc=1      )  # if you want you can load already splitted csv.
				__lowercase								= [kb_dataset.shard(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							contiguous=SCREAMING_SNAKE_CASE      ) for i in range(SCREAMING_SNAKE_CASE      )]
				__lowercase								= kb_list[process_num]

				__lowercase								= 'data_' + str(SCREAMING_SNAKE_CASE      )
				__lowercase								= os.path.join(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      )

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

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

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

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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Tuple   ,							SCREAMING_SNAKE_CASE	:				Tuple      )					->						Tuple:
				__lowercase								= []

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

				__lowercase								= concatenate_datasets(SCREAMING_SNAKE_CASE      )
				faiss.omp_set_num_threads(96      )

				__lowercase								= faiss.IndexHNSWFlat(768   ,							128   ,							faiss.METRIC_INNER_PRODUCT      )
				concat.add_faiss_index('embeddings'   ,							custom_index=SCREAMING_SNAKE_CASE      )
				concat.get_index('embeddings'      ).save(
				    SCREAMING_SNAKE_CASE      )  # since we load the index in to memory,we can directly update the index in the disk







import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Optional[Any]:
				if isinstance(SCREAMING_SNAKE_CASE   ,							(list, tuple)      ):
								return honor_type(SCREAMING_SNAKE_CASE   ,							(move_to_device(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for t in state)      )
				elif isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								return type(SCREAMING_SNAKE_CASE      )({k: move_to_device(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for k, v in state.items()}      )
				elif isinstance(SCREAMING_SNAKE_CASE   ,							torch.Tensor      ):
								return state.to(SCREAMING_SNAKE_CASE      )
				return state
class      A__       (						torch.optim.Optimizer    ):





							def __init__(			self  :       Optional[Any]					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Optional[int]=True					,			_UpperCAmelCase  :       int=None    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= optimizer
											__lowercase								= scaler
											__lowercase								= AcceleratorState()
											__lowercase								= GradientState()
											__lowercase								= device_placement
											__lowercase								= False
											__lowercase								= None

											# Handle device placement
											if device_placement:
															__lowercase								= self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(_UpperCAmelCase					,			self.accelerator_state.device    )
															else:
																			__lowercase								= move_to_device(_UpperCAmelCase					,			self.accelerator_state.device    )
															self.optimizer.load_state_dict(_UpperCAmelCase    )





							@property
							def       a__			(			self  :       Union[str, Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											return self.optimizer.state





							@state.setter
							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= state





							@property
							def       a__			(			self  :       str    )						->		int:



											"""simple docstring"""


											return self.optimizer.param_groups





							@param_groups.setter
							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       List[Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= param_groups





							@property
							def       a__			(			self  :       List[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											return self.optimizer.defaults





							@defaults.setter
							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       int    )						->		Any:



											"""simple docstring"""


											__lowercase								= defaults





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[Any]    )						->		Any:



											"""simple docstring"""


											self.optimizer.add_param_group(_UpperCAmelCase    )





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(_UpperCAmelCase					,			self.accelerator_state.device    )
											self.optimizer.load_state_dict(_UpperCAmelCase    )





							def       a__			(			self  :       str    )						->		Any:



											"""simple docstring"""


											return self.optimizer.state_dict()





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       List[str]=None    )						->		Tuple:



											"""simple docstring"""


											if self.gradient_state.sync_gradients:
															__lowercase								= 'set_to_none' in inspect.signature(self.optimizer.zero_grad    ).parameters
															if accept_arg:
																			if set_to_none is None:
																							__lowercase								= False
																			self.optimizer.zero_grad(set_to_none=_UpperCAmelCase    )
															else:
																			if set_to_none is not None:
																							raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'    )
																			self.optimizer.zero_grad()





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       Optional[Any]=None    )						->		int:



											"""simple docstring"""


											if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			__lowercase								= {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer					,			optimizer_args=_UpperCAmelCase    )
															elif self.scaler is not None:
																			__lowercase								= False
																			if self._last_scale is None:
																							# `get_scale` is an async operation requiring full synchronization
																							# on CPU and GPUs before finishing. As a result, we store away
																							# the prior one to reduce the call overhead
																							__lowercase								= self.scaler.get_scale()
																							__lowercase								= True
																			self.scaler.step(self.optimizer					,			_UpperCAmelCase    )
																			self.scaler.update()
																			__lowercase								= self.scaler.get_scale()
																			if not new_scale:
																							# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																							__lowercase								= scale_after < self._last_scale
																			__lowercase								= scale_after

															else:
																			self.optimizer.step(_UpperCAmelCase    )





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       int    )						->		Tuple:



											"""simple docstring"""


											for param_group in self.optimizer.param_groups:
															__lowercase								= [parameters_map.get(_UpperCAmelCase					,			_UpperCAmelCase    ) for p in param_group['params']]





							@property
							def       a__			(			self  :       Optional[Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											warnings.warn(
											    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
											    '`optimizer.step_was_skipped` instead.'					,			_UpperCAmelCase					,			)
											return self._is_overflow





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



											"""simple docstring"""


											return self._is_overflow





							def __getstate__(			self  :       Optional[int]    )						->		str:



											"""simple docstring"""


											return self.__dict__.copy()





							def __setstate__(			self  :       List[Any]					,			_UpperCAmelCase  :       Optional[int]    )						->		Any:



											"""simple docstring"""


											self.__dict__.update(_UpperCAmelCase    )





