



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_       (  _snake_case  :str  ,      _snake_case  :str=100  ,      _snake_case  :Optional[Any]=" "							)     ->    Tuple:
   _A						     =					text.split(_snake_case							)
   return [character.join(text[i : i + n]							).strip() for i in range(0  ,      len(_snake_case							)  ,      _snake_case							)]

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]							)     ->    Dict:
   _A     ,						_A						     =					[], []
   for title, text in zip(documents['''title''']  ,      documents['''text''']							):
      if text is not None:
         for passage in split_text(_snake_case							):
            titles.append(title if title is not None else ''''''							)
            texts.append(_snake_case							)
   return {"title": titles, "text": texts}

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

   _A						     =					'''data_''' + str(_snake_case							)
   _A						     =					os.path.join(_snake_case  ,      _snake_case							)

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

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

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

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


def   SCREAMING_SNAKE_CASE_       (  _snake_case  :str  ,      _snake_case  :Dict							)     ->    List[str]:
   _A						     =					[]

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

   _A						     =					concatenate_datasets(_snake_case							)
   faiss.omp_set_num_threads(96							)

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








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


UpperCAmelCase_		   =				os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
UpperCAmelCase_		   =				os.path.join(hf_cache_home, """accelerate""")
UpperCAmelCase_		   =				os.path.join(cache_dir, """default_config.yaml""")
UpperCAmelCase_		   =				os.path.join(cache_dir, """default_config.yaml""")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
   UpperCAmelCase_		   =				default_yaml_config_file
else:
   UpperCAmelCase_		   =				default_json_config_file

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[str]							)     ->    Union[str, Any]:
   if config_file is not None:
      if not os.path.isfile(_snake_case							):
         raise FileNotFoundError(
             F'''The passed configuration file `{config_file}` does not exist. '''
             '''Please pass an existing file to `accelerate launch`, or use the the default one '''
             '''created through `accelerate config` and run `accelerate launch` '''
             '''without the `--config_file` argument.'''							)
   else:
      _A						     =					default_config_file
   with open(_snake_case  ,      '''r'''  ,      encoding='''utf-8'''							) as f:
      if config_file.endswith('''.json'''							):
         if (
             json.load(_snake_case							).get('''compute_environment'''  ,      ComputeEnvironment.LOCAL_MACHINE							)
             == ComputeEnvironment.LOCAL_MACHINE
         ):
            _A						     =					ClusterConfig
         else:
            _A						     =					SageMakerConfig
         return config_class.from_json_file(json_file=_snake_case							)
      else:
         if (
             yaml.safe_load(_snake_case							).get('''compute_environment'''  ,      ComputeEnvironment.LOCAL_MACHINE							)
             == ComputeEnvironment.LOCAL_MACHINE
         ):
            _A						     =					ClusterConfig
         else:
            _A						     =					SageMakerConfig
         return config_class.from_yaml_file(yaml_file=_snake_case							)


@dataclass
class   lowerCamelCase__  :



 """simple docstring"""

 a__				:   ComputeEnvironment
 a__				:   Union[DistributedType, SageMakerDistributedType]
 a__				:   str
 a__				:   bool

 def 						snake_case_			(						self				:				List[str]			)      ->  Any:
    _A						     =					self.__dict__
    # For serialization, it's best to convert Enums to strings (or their underlying value type).
    for key, value in result.items():
       if isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
          _A						     =					value.value
       if isinstance(__lowerCAmelCase ,	__lowerCAmelCase			) and not bool(__lowerCAmelCase			):
          _A						     =					None
    _A						     =					{k: v for k, v in result.items() if v is not None}
    return result

 @classmethod
 def 						snake_case_			(						cls				:				List[str] ,	__lowerCAmelCase				:				int=None			)      ->  List[str]:
    _A						     =					default_json_config_file if json_file is None else json_file
    with open(__lowerCAmelCase ,	'''r''' ,	encoding='''utf-8'''			) as f:
       _A						     =					json.load(__lowerCAmelCase			)
    if "compute_environment" not in config_dict:
       _A						     =					ComputeEnvironment.LOCAL_MACHINE
    if "mixed_precision" not in config_dict:
       _A						     =					'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
    if "fp16" in config_dict:  # Convert the config to the new format.
       del config_dict["fp16"]
    if "dynamo_backend" in config_dict:  # Convert the config to the new format.
       _A						     =					config_dict.pop('''dynamo_backend'''			)
       _A						     =					{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
    if "use_cpu" not in config_dict:
       _A						     =					False
    return cls(**__lowerCAmelCase			)

 def 						snake_case_			(						self				:				Any ,	__lowerCAmelCase				:				List[str]			)      ->  str:
    with open(__lowerCAmelCase ,	'''w''' ,	encoding='''utf-8'''			) as f:
       _A						     =					json.dumps(self.to_dict() ,	indent=2 ,	sort_keys=__lowerCAmelCase			) + '''\n'''
       f.write(__lowerCAmelCase			)

 @classmethod
 def 						snake_case_			(						cls				:				int ,	__lowerCAmelCase				:				str=None			)      ->  Any:
    _A						     =					default_yaml_config_file if yaml_file is None else yaml_file
    with open(__lowerCAmelCase ,	'''r''' ,	encoding='''utf-8'''			) as f:
       _A						     =					yaml.safe_load(__lowerCAmelCase			)
    if "compute_environment" not in config_dict:
       _A						     =					ComputeEnvironment.LOCAL_MACHINE

    if "mixed_precision" not in config_dict:
       _A						     =					'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
    if isinstance(config_dict['''mixed_precision'''] ,	__lowerCAmelCase			) and not config_dict["mixed_precision"]:
       _A						     =					'''no'''
    if "fp16" in config_dict:  # Convert the config to the new format.
       del config_dict["fp16"]
    if "dynamo_backend" in config_dict:  # Convert the config to the new format.
       _A						     =					config_dict.pop('''dynamo_backend'''			)
       _A						     =					{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
    if "use_cpu" not in config_dict:
       _A						     =					False
    return cls(**__lowerCAmelCase			)

 def 						snake_case_			(						self				:				Union[str, Any] ,	__lowerCAmelCase				:				str			)      ->  int:
    with open(__lowerCAmelCase ,	'''w''' ,	encoding='''utf-8'''			) as f:
       yaml.safe_dump(self.to_dict() ,	__lowerCAmelCase			)

 def 						snake_case_			(						self				:				Optional[int]			)      ->  Dict:
    if isinstance(self.compute_environment ,	__lowerCAmelCase			):
       _A						     =					ComputeEnvironment(self.compute_environment			)
    if isinstance(self.distributed_type ,	__lowerCAmelCase			):
       if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
          _A						     =					SageMakerDistributedType(self.distributed_type			)
       else:
          _A						     =					DistributedType(self.distributed_type			)
    if self.dynamo_config is None:
       _A						     =					{}


@dataclass
class   lowerCamelCase__  (					_A):



 """simple docstring"""

 a__				:   int
 a__				:   int   =     0
 a__				:   int   =     1
 a__				:   Optional[str]   =     None
 a__				:   Optional[str]   =     None
 a__				:   Optional[int]   =     None
 a__				:   Optional[str]   =     "static"
 a__				:   Optional[bool]   =     False
 a__				:   str   =     "main"

 # args for deepspeed_plugin
 a__				:   dict   =     None
 # args for fsdp
 a__				:   dict   =     None
 # args for megatron_lm
 a__				:   dict   =     None
 # args for ipex
 a__				:   dict   =     None
 # args for TPU
 a__				:   bool   =     False

 # args for TPU pods
 a__				:   str   =     None
 a__				:   str   =     None
 a__				:   bool   =     False
 a__				:   bool   =     False
 a__				:   str   =     None
 a__				:   List[str]   =     None
 a__				:   List[str]   =     None
 a__				:   List[str]   =     None

 # args for dynamo
 a__				:   dict   =     None

 def 						snake_case_			(						self				:				str			)      ->  Any:
    if self.deepspeed_config is None:
       _A						     =					{}
    if self.fsdp_config is None:
       _A						     =					{}
    if self.megatron_lm_config is None:
       _A						     =					{}
    if self.ipex_config is None:
       _A						     =					{}
    return super().__post_init__()


@dataclass
class   lowerCamelCase__  (					_A):



 """simple docstring"""

 a__				:   str
 a__				:   str
 a__				:   Optional[str]   =     None
 a__				:   Optional[str]   =     None
 a__				:   str   =     "us-east-1"
 a__				:   int   =     1
 a__				:   str   =     "all"
 a__				:   str   =     f'''accelerate-sagemaker-{num_machines}'''
 a__				:   str   =     SAGEMAKER_PYTORCH_VERSION
 a__				:   str   =     SAGEMAKER_TRANSFORMERS_VERSION
 a__				:   str   =     SAGEMAKER_PYTHON_VERSION
 a__				:   str   =     None
 a__				:   str   =     None
 a__				:   dict   =     None
 a__				:   dict   =     None



