






"""simple docstring"""




import json
import os
import pickle
import subprocess
from hashlib import mda
from pathlib import Path
from tempfile import gettempdir
from textwrap import dedent
from types import CodeType, FunctionType
from unittest import TestCase
from unittest.mock import patch

import pytest
from multiprocess import Pool
from packaging import version

import datasets
from datasets.fingerprint import Hasher, fingerprint_transform
from datasets.table import InMemoryTable

from .utils import (
    require_regex,
    require_spacy,
    require_spacy_model,
    require_tiktoken,
    require_torch,
    require_transformers,
)






class  UpperCamelCase    :


 def __init__(  self	:		int							,		UpperCAmelCase__	:		Union[str, Any]							)      -> int:
   _a						: Union[str, Any]    =			foo







 def __call__(  self	:		str							)      -> List[str]:
   return self.foo







class  UpperCamelCase    (			datasets.Dataset   ):


 @fingerprint_transform(inplace=UpperCAmelCase__							)
 def _lowercase			(  self	:		List[str]							,		UpperCAmelCase__	:		int							,		*UpperCAmelCase__	:		Optional[Any]							,		**UpperCAmelCase__	:		Optional[int]							)      -> Dict:
   return DatasetChild(self.data							,		fingerprint=UpperCAmelCase__							)







 @fingerprint_transform(inplace=UpperCAmelCase__							)
 def _lowercase			(  self	:		List[str]							,		UpperCAmelCase__	:		Dict							,		*UpperCAmelCase__	:		Union[str, Any]							,		**UpperCAmelCase__	:		Dict							)      -> str:
   return DatasetChild(self.data							,		fingerprint=UpperCAmelCase__							)







class  UpperCamelCase    :


 def __init__(  self	:		Dict							,		UpperCAmelCase__	:		Dict							)      -> List[Any]:
   _a						: Union[str, Any]    =			callable


 def __call__(  self	:		List[str]							,		*UpperCAmelCase__	:		Union[str, Any]							,		**UpperCAmelCase__	:		List[str]							)      -> Any:
   if self.callable is not None:
     return self.callable(*UpperCAmelCase__							,		**UpperCAmelCase__							)







 def __getstate__(  self	:		Optional[int]							)      -> Dict:
   raise pickle.PicklingError()







class  UpperCamelCase    (			snake_case_   ):


 @require_transformers
 @pytest.mark.integration
 def _lowercase			(  self	:		Dict							)      -> Optional[Any]:
   from transformers import AutoTokenizer

   def encode(UpperCAmelCase__	:		List[str]							):
     return tokenizer(UpperCAmelCase__							)

   # TODO: add hash consistency tests across sessions
   _a						: int    =			AutoTokenizer.from_pretrained("""bert-base-uncased"""							)
   _a						: int    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Any    =			mda(datasets.utils.py_utils.dumps(lambda UpperCAmelCase__							: tokenizer(UpperCAmelCase__							)							)							).hexdigest()
   _a						: Optional[int]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: List[Any]    =			AutoTokenizer.from_pretrained("""bert-base-cased"""							)
   _a						: Any    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Dict    =			mda(datasets.utils.py_utils.dumps(lambda UpperCAmelCase__							: tokenizer(UpperCAmelCase__							)							)							).hexdigest()
   _a						: Union[str, Any]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Any    =			AutoTokenizer.from_pretrained("""bert-base-uncased"""							)
   _a						: Tuple    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Tuple    =			mda(datasets.utils.py_utils.dumps(lambda UpperCAmelCase__							: tokenizer(UpperCAmelCase__							)							)							).hexdigest()
   _a						: List[str]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 @require_transformers
 @pytest.mark.integration
 def _lowercase			(  self	:		Dict							)      -> Optional[int]:
   from transformers import AutoTokenizer

   _a						: int    =			AutoTokenizer.from_pretrained("""gpt2"""							)
   _a						: Tuple    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   tokenizer("""Hello world !"""							)  # call once to change the tokenizer's cache
   _a						: int    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)







 @require_regex
 def _lowercase			(  self	:		Optional[Any]							)      -> List[Any]:
   import regex

   _a						: Optional[int]    =			regex.Regex("""foo"""							)
   _a						: Any    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Tuple    =			regex.Regex("""bar"""							)
   _a						: Any    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Tuple    =			regex.Regex("""foo"""							)
   _a						: Tuple    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)







class  UpperCamelCase    (			snake_case_   ):


 def _lowercase			(  self	:		Dict							)      -> List[str]:
   def func():
     return foo

   _a						: Tuple    =			[0]
   _a						: Optional[int]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: List[str]    =			[1]
   _a						: str    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: str    =			[0]
   _a						: List[str]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 def _lowercase			(  self	:		Union[str, Any]							)      -> List[Any]:
   def func():
     pass

   _a						: Tuple    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()

   def func():
     pass

   _a						: Dict    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							)      -> int:
   _a						: int    =			mda(datasets.utils.py_utils.dumps(Foo([0]							)							)							).hexdigest()
   _a						: List[str]    =			mda(datasets.utils.py_utils.dumps(Foo([1]							)							)							).hexdigest()
   _a						: str    =			mda(datasets.utils.py_utils.dumps(Foo([0]							)							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 def _lowercase			(  self	:		str							)      -> Optional[Any]:
   _a						: Optional[Any]    =			mda(datasets.utils.py_utils.dumps(Foo([0]							).__call__							)							).hexdigest()
   _a						: int    =			mda(datasets.utils.py_utils.dumps(Foo([1]							).__call__							)							).hexdigest()
   _a						: Union[str, Any]    =			mda(datasets.utils.py_utils.dumps(Foo([0]							).__call__							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 def _lowercase			(  self	:		Tuple							)      -> int:
   _a						: Optional[int]    =			(
       """co_argcount""",
       """co_kwonlyargcount""",
       """co_nlocals""",
       """co_stacksize""",
       """co_flags""",
       """co_code""",
       """co_consts""",
       """co_names""",
       """co_varnames""",
       """co_filename""",
       """co_name""",
       """co_firstlineno""",
       """co_lnotab""",
       """co_freevars""",
       """co_cellvars""",
   )

   def _create_code_pyaa(*UpperCAmelCase__	:		Tuple							):
     if hasattr(args[-3]							,		"""encode"""							):
       _a						: Tuple    =			list(UpperCAmelCase__							)
       _a						: List[Any]    =			args[-3].encode()  # co_lnotab
       _a						: List[str]    =			args[-10].encode()  # co_code
     if hasattr(UpperCAmelCase__							,		"""co_posonlyargcount"""							):
       if len(UpperCAmelCase__							) == 16:
         return CodeType(*UpperCAmelCase__							)
       elif len(UpperCAmelCase__							) == 15:
         return CodeType(args[0]							,		0							,		*args[1:]							)
       return CodeType(args[0]							,		0							,		0							,		*args[1:]							)
     elif hasattr(UpperCAmelCase__							,		"""co_kwonlyargcount"""							):
       if len(UpperCAmelCase__							) == 16:
         return CodeType(args[0]							,		*args[2:]							)
       elif len(UpperCAmelCase__							) == 15:
         return CodeType(*UpperCAmelCase__							)
       return CodeType(args[0]							,		0							,		*args[1:]							)
     if len(UpperCAmelCase__							) == 16:
       return CodeType(args[0]							,		*args[3:]							)
     elif len(UpperCAmelCase__							) == 15:
       return CodeType(args[0]							,		*args[2:]							)
     return CodeType(*UpperCAmelCase__							)

   def create_ipython_func(UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		int							):
     def func():
       return returned_obj

     _a						: str    =			func.__code__
     # Use _create_code from dill in order to make it work for different python versions
     if datasets.config.PY_VERSION < version.parse("""3.8"""							):
       _a						: Any    =			_create_code_pyaa(
           *[getattr(UpperCAmelCase__							,		UpperCAmelCase__							) if k != """co_filename""" else co_filename for k in code_args_pyaa]							)
     else:
       _a						: Tuple    =			code.replace(co_filename=UpperCAmelCase__							)
     return FunctionType(UpperCAmelCase__							,		func.__globals__							,		func.__name__							,		func.__defaults__							,		func.__closure__							)

   _a   ,						_a						: Union[str, Any]    =			"""<ipython-input-2-e0383a102aae>""", [0]
   _a						: Dict    =			mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase__							,		UpperCAmelCase__							)							)							).hexdigest()
   _a   ,						_a						: Dict    =			"""<ipython-input-2-e0383a102aae>""", [1]
   _a						: Any    =			mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase__							,		UpperCAmelCase__							)							)							).hexdigest()
   _a   ,						_a						: str    =			"""<ipython-input-5-713f6613acf3>""", [0]
   _a						: int    =			mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase__							,		UpperCAmelCase__							)							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)

   _a   ,						_a						: Optional[Any]    =			os.path.join(gettempdir()							,		"""ipykernel_12345"""							,		"""321456789.py"""							), [0]
   _a						: Dict    =			mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase__							,		UpperCAmelCase__							)							)							).hexdigest()
   _a   ,						_a						: Tuple    =			os.path.join(gettempdir()							,		"""ipykernel_12345"""							,		"""321456789.py"""							), [1]
   _a						: Optional[int]    =			mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase__							,		UpperCAmelCase__							)							)							).hexdigest()
   _a   ,						_a						: List[str]    =			os.path.join(gettempdir()							,		"""ipykernel_12345"""							,		"""654123987.py"""							), [0]
   _a						: str    =			mda(datasets.utils.py_utils.dumps(create_ipython_func(UpperCAmelCase__							,		UpperCAmelCase__							)							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)







 def _lowercase			(  self	:		List[Any]							)      -> Optional[int]:
   _a   ,						_a						: Any    =			[0], [1]

   def func():
     return foo, bar

   _a						: Optional[int]    =			"""__main__"""

   def globalvars_mocka_side_effect(UpperCAmelCase__	:		str							,		*UpperCAmelCase__	:		Any							,		**UpperCAmelCase__	:		int							):
     return {"foo": foo, "bar": bar}

   def globalvars_mocka_side_effect(UpperCAmelCase__	:		Optional[Any]							,		*UpperCAmelCase__	:		Dict							,		**UpperCAmelCase__	:		Optional[int]							):
     return {"bar": bar, "foo": foo}

   with patch("""dill.detect.globalvars"""							,		side_effect=UpperCAmelCase__							) as globalvars_mocka:
     _a						: Tuple    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
     self.assertGreater(globalvars_mocka.call_count							,		0							)
   with patch("""dill.detect.globalvars"""							,		side_effect=UpperCAmelCase__							) as globalvars_mocka:
     _a						: Dict    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
     self.assertGreater(globalvars_mocka.call_count							,		0							)
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)







class  UpperCamelCase    (			snake_case_   ):


 def _lowercase			(  self	:		List[str]							)      -> List[str]:
   _a						: Any    =			Hasher.hash("""hello"""							)
   _a						: List[Any]    =			Hasher.hash("""hello"""							)
   _a						: Union[str, Any]    =			Hasher.hash("""there"""							)
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 def _lowercase			(  self	:		Union[str, Any]							)      -> List[Any]:
   _a						: Any    =			Hasher.hash(Foo("""hello"""							)							)
   _a						: Optional[int]    =			Hasher.hash(Foo("""hello"""							)							)
   _a						: List[Any]    =			Hasher.hash(Foo("""there"""							)							)
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 def _lowercase			(  self	:		int							)      -> List[str]:
   _a						: Optional[int]    =			Hasher()
   for x in ["hello", Foo("""hello"""							)]:
     hasher.update(UpperCAmelCase__							)
   _a						: Dict    =			hasher.hexdigest()
   _a						: Any    =			Hasher()
   for x in ["hello", Foo("""hello"""							)]:
     hasher.update(UpperCAmelCase__							)
   _a						: Any    =			hasher.hexdigest()
   _a						: Dict    =			Hasher()
   for x in ["there", Foo("""there"""							)]:
     hasher.update(UpperCAmelCase__							)
   _a						: Any    =			hasher.hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 def _lowercase			(  self	:		Optional[Any]							)      -> Optional[Any]:
   with self.assertRaises(pickle.PicklingError							):
     Hasher.hash(UnpicklableCallable(Foo("""hello"""							)							)							)


 def _lowercase			(  self	:		Union[str, Any]							)      -> str:
   _a						: Any    =			"""abc"""
   _a						: List[str]    =			[string, string]  # two strings have the same ids
   _a						: Optional[Any]    =			[string, string]
   _a						: Tuple    =			json.loads(f"""[\"{string}\", \"{string}\"]"""							)  # two strings have different ids
   self.assertIs(obja[0]							,		UpperCAmelCase__							)
   self.assertIs(obja[0]							,		obja[1]							)
   self.assertIs(obja[0]							,		UpperCAmelCase__							)
   self.assertIs(obja[0]							,		obja[1]							)
   self.assertIsNot(obja[0]							,		UpperCAmelCase__							)
   self.assertIsNot(obja[0]							,		obja[1]							)
   _a						: Any    =			Hasher.hash(UpperCAmelCase__							)
   _a						: Dict    =			Hasher.hash(UpperCAmelCase__							)
   _a						: List[str]    =			Hasher.hash(UpperCAmelCase__							)
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 @require_tiktoken
 def _lowercase			(  self	:		Union[str, Any]							)      -> Optional[int]:
   import tiktoken

   _a						: Union[str, Any]    =			tiktoken.get_encoding("""gpt2"""							)
   _a						: List[str]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Union[str, Any]    =			tiktoken.get_encoding("""r50k_base"""							)
   _a						: int    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Tuple    =			tiktoken.get_encoding("""gpt2"""							)
   _a						: Union[str, Any]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)


 @require_torch
 def _lowercase			(  self	:		List[Any]							)      -> Optional[int]:
   import torch

   _a						: Union[str, Any]    =			torch.tensor([1.0]							)
   _a						: Optional[int]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Dict    =			torch.tensor([2.0]							)
   _a						: Dict    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: List[Any]    =			torch.tensor([1.0]							)
   _a						: Optional[Any]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)







 @require_spacy
 @require_spacy_model("""en_core_web_sm"""							)
 @require_spacy_model("""fr_core_news_sm"""							)
 @pytest.mark.integration
 def _lowercase			(  self	:		Any							)      -> Optional[Any]:
   import spacy

   _a						: Union[str, Any]    =			spacy.load("""en_core_web_sm"""							)
   _a						: Union[str, Any]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: int    =			spacy.load("""fr_core_news_sm"""							)
   _a						: Any    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   _a						: Dict    =			spacy.load("""en_core_web_sm"""							)
   _a						: List[str]    =			mda(datasets.utils.py_utils.dumps(UpperCAmelCase__							)							).hexdigest()
   self.assertEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
   self.assertNotEqual(UpperCAmelCase__							,		UpperCAmelCase__							)







@pytest.mark.integration
def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Optional[Any]    =			tmp_path / """dir1"""
  _a						: Optional[Any]    =			tmp_path / """dir2"""
  dira.mkdir()
  dira.mkdir()
  _a						: int    =			"""script.py"""
  _a						: Tuple    =			dedent(
      """
    from datasets.fingerprint import Hasher
    def foo():
        pass
    print(Hasher.hash(foo))
    """       )
  _a						: List[str]    =			dira / script_filename
  _a						: int    =			dira / script_filename
  with script_patha.open("""w"""       ) as f:
    f.write(UpperCamelCase__       )
  with script_patha.open("""w"""       ) as f:
    f.write(UpperCamelCase__       )
  _a						: Optional[Any]    =			subprocess.check_output(["""python""", str(UpperCamelCase__       )]       )
  _a						: Union[str, Any]    =			subprocess.check_output(["""python""", str(UpperCamelCase__       )]       )
  assert fingerprinta == fingerprinta

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: int    =			{"""a""": [0, 1, 2]}
  _a						: Tuple    =			DatasetChild(InMemoryTable.from_pydict(UpperCamelCase__       )       )
  _a						: int    =			dataset.funca()._fingerprint
  assert expected_fingerprint == dataset.funca()._fingerprint
  assert expected_fingerprint != dataset.funca()._fingerprint

  with Pool(2       ) as p:
    assert expected_fingerprint == p.apply_async(dataset.funca       ).get()._fingerprint
    assert expected_fingerprint != p.apply_async(dataset.funca       ).get()._fingerprint

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Tuple    =			{"""a""": [0, 1, 2]}






  class  UpperCamelCase    (			datasets.Dataset   ):


   @fingerprint_transform(inplace=UpperCAmelCase__							)
   def _lowercase			(  self	:		str							,		UpperCAmelCase__	:		Tuple							)      -> Any:
     return DummyDatasetChild(self.data							,		fingerprint=UpperCAmelCase__							)







  _a						: Any    =			DummyDatasetChild(InMemoryTable.from_pydict(UpperCamelCase__       )       ).func()






  class  UpperCamelCase    (			datasets.Dataset   ):


   @fingerprint_transform(inplace=UpperCAmelCase__							,		version="""1.0.0"""							)
   def _lowercase			(  self	:		Optional[int]							,		UpperCAmelCase__	:		Optional[int]							)      -> Dict:
     return DummyDatasetChild(self.data							,		fingerprint=UpperCAmelCase__							)







  _a						: int    =			DummyDatasetChild(InMemoryTable.from_pydict(UpperCamelCase__       )       ).func()






  class  UpperCamelCase    (			datasets.Dataset   ):


   @fingerprint_transform(inplace=UpperCAmelCase__							,		version="""2.0.0"""							)
   def _lowercase			(  self	:		int							,		UpperCAmelCase__	:		List[str]							)      -> str:
     return DummyDatasetChild(self.data							,		fingerprint=UpperCAmelCase__							)







  _a						: Tuple    =			DummyDatasetChild(InMemoryTable.from_pydict(UpperCamelCase__       )       ).func()

  assert len({fingeprint_no_version, fingeprint_a, fingeprint_a}       ) == 3



def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  # AttributeError: module 'dill._dill' has no attribute 'stack'
  _a						: Dict    =			Hasher()
  hasher.update(lambda UpperCamelCase__       : x       )









"""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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


_snake_case					   =     logging.getLogger(__name__)
torch.set_grad_enabled(False)
_snake_case					   =     'cuda' if torch.cuda.is_available() else 'cpu'

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__=1_0_0  ,     UpperCamelCase__=" "       ):



  '''simple docstring'''
  _a						: str    =			text.split(UpperCamelCase__       )
  return [character.join(text[i : i + n]       ).strip() for i in range(0  ,     len(UpperCamelCase__       )  ,     UpperCamelCase__       )]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a   ,						_a						: Optional[int]    =			[], []
  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     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			ctx_tokenizer(
      documents["""title"""]  ,     documents["""text"""]  ,     truncation=UpperCamelCase__  ,     padding="""longest"""  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Optional[int]    =			ctx_encoder(input_ids.to(device=UpperCamelCase__       )  ,     return_dict=UpperCamelCase__       ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     ):



  '''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
  _a						: 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
  _a						: Any    =			dataset.map(UpperCamelCase__  ,     batched=UpperCamelCase__  ,     num_proc=processing_args.num_proc       )

  # And compute the embeddings
  _a						: Optional[Any]    =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=UpperCamelCase__       )
  _a						: str    =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
  _a						: int    =			Features(
      {"""text""": Value("""string"""       ), """title""": Value("""string"""       ), """embeddings""": Sequence(Value("""float32"""       )       )}       )  # optional, save as float32 instead of float64 to save space
  _a						: str    =			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						: List[Any]    =			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						: List[str]    =			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						: List[str]    =			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

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

  # Easy way to load the model
  _a						: str    =			RagRetriever.from_pretrained(
      rag_example_args.rag_model_name  ,     index_name="""custom"""  ,     indexed_dataset=UpperCamelCase__       )
  _a						: Any    =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name  ,     retriever=UpperCamelCase__       )
  _a						: List[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"""       )
  ######################################

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






@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : str    =      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\''''}	,  )
 UpperCamelCase      : Optional[str]    =      field(
     default=snake_case_	,  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=snake_case_	,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}	,  )







@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : Optional[int]    =      field(
     default=snake_case_	,  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  UpperCamelCase    :
 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)

 _snake_case					   =     HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 _snake_case   ,				_snake_case   ,				_snake_case					   =     parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  _snake_case					   =     rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)

