
'''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil







class  __A				( _SCREAMING_SNAKE_CASE			):
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     Dict												=		pa.array(TypedSequence([1, 2, 3]       )       )
   self.assertEqual(arr.type				,   pa.intaa()       )
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   with self.assertRaises(lowerCamelCase       ):
     __A		:     Optional[int]												=		pa.array(TypedSequence([1, 2, 3]       )				,   type=pa.intaa()       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   with self.assertRaises(lowerCamelCase       ):
     __A		:     List[str]												=		pa.array(TypedSequence([1, 2, 3]				,   try_type=Value("""bool"""       )				,   type=Value("""int64"""       )       )       )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     Any												=		pa.array(TypedSequence([1, 2, 3]				,   type=Value("""int32"""       )       )       )
   self.assertEqual(arr.type				,   pa.intaa()       )
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   with self.assertRaises((TypeError, pa.lib.ArrowInvalid)       ):
     __A		:     List[Any]												=		pa.array(TypedSequence(["""foo""", """bar"""]				,   type=Value("""int64"""       )       )       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		pa.array(TypedSequence([1, 2, 3]				,   try_type=Value("""int32"""       )       )       )
   self.assertEqual(arr.type				,   pa.intaa()       )
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     Tuple												=		pa.array(TypedSequence(["""foo""", """bar"""]				,   try_type=Value("""int64"""       )       )       )
   self.assertEqual(arr.type				,   pa.string()       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   __A		:     List[Any]												=		pa.array(TypedSequence([[[1, 2, 3]]]				,   type=ArrayaD((1, 3)				,   """int64"""       )       )       )
   self.assertEqual(arr.type				,   ArrayaDExtensionType((1, 3)				,   """int64"""       )       )
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   with self.assertRaises((TypeError, pa.lib.ArrowInvalid)       ):
     __A		:     List[Any]												=		pa.array(TypedSequence(["""foo""", """bar"""]				,   type=ArrayaD((1, 3)				,   """int64"""       )       )       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     List[Any]												=		pa.array(TypedSequence([[[1, 2, 3]]]				,   try_type=ArrayaD((1, 3)				,   """int64"""       )       )       )
   self.assertEqual(arr.type				,   ArrayaDExtensionType((1, 3)				,   """int64"""       )       )
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     Any												=		pa.array(TypedSequence(["""foo""", """bar"""]				,   try_type=ArrayaD((1, 3)				,   """int64"""       )       )       )
   self.assertEqual(arr.type				,   pa.string()       )






 @require_pil
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   import PIL.Image

   __A		:     str												=		PIL.Image.fromarray(np.arange(10				,   dtype=np.uinta       ).reshape(2				,   5       )       )
   with patch(
       """datasets.arrow_writer.cast_to_python_objects"""				,   side_effect=lowerCamelCase       ) as mock_cast_to_python_objects:
     __A		:     Optional[int]												=		pa.array(TypedSequence([{"""path""": None, """bytes""": B"""image_bytes"""}, pil_image]				,   type=Image()       )       )
     __A							,					__A		:     List[Any]												=		mock_cast_to_python_objects.call_args_list[-1]
     self.assertIn("""optimize_list_casting"""				,   lowerCamelCase       )
     self.assertFalse(kwargs["""optimize_list_casting"""]       )







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  int )				->       str:





  """simple docstring"""

  __A		:     int												=		pa.BufferReader(__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE			,     pa.Buffer ) else pa.memory_map(__SCREAMING_SNAKE_CASE )
  __A		:     Dict												=		pa.ipc.open_stream(__SCREAMING_SNAKE_CASE )
  __A		:     pa.Table												=		f.read_all()
  assert len(pa_table.to_batches() ) == expected_num_chunks
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
  del pa_table







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
@pytest.mark.parametrize(
    """fields"""			,     [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       List[str]:





  """simple docstring"""

  __A		:     Optional[int]												=		pa.BufferOutputStream()
  __A		:     List[str]												=		pa.schema(__SCREAMING_SNAKE_CASE ) if fields else None
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     schema=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1} )
    writer.write({"""col_1""": """bar""", """col_2""": 2} )
    __A							,					__A		:     str												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __A		:     List[Any]												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







def        A_     (						)				->       Optional[Any]:





  """simple docstring"""

  __A		:     List[str]												=		pa.BufferOutputStream()
  __A		:     List[Any]												=		Features({"""labels""": ClassLabel(names=["""neg""", """pos"""] )} )
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     features=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write({"""labels""": 0} )
    writer.write({"""labels""": 1} )
    __A							,					__A		:     Any												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == features.arrow_schema
  assert writer._schema.metadata == features.arrow_schema.metadata
  __A		:     Tuple												=		pa.BufferReader(output.getvalue() )
  __A		:     List[Any]												=		pa.ipc.open_stream(__SCREAMING_SNAKE_CASE )
  __A		:     pa.Table												=		f.read_all()
  __A		:     Dict												=		pa_table.schema
  assert pa_table.num_rows == 2
  assert schema == features.arrow_schema
  assert schema.metadata == features.arrow_schema.metadata
  assert features == Features.from_arrow_schema(__SCREAMING_SNAKE_CASE )







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any] )				->       Dict:





  """simple docstring"""

  __A		:     List[str]												=		pa.BufferOutputStream()
  with ArrowWriter(
      stream=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE			,     hash_salt="""split_name"""			,     check_duplicates=__SCREAMING_SNAKE_CASE			,     ) as writer:
    with pytest.raises(__SCREAMING_SNAKE_CASE ):
      writer.write({"""col_1""": """foo""", """col_2""": 1}			,     key=[1, 2] )
      __A							,					__A		:     int												=		writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 2, 10] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str] )				->       Optional[Any]:





  """simple docstring"""

  __A		:     int												=		pa.BufferOutputStream()
  with ArrowWriter(
      stream=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE			,     hash_salt="""split_name"""			,     check_duplicates=__SCREAMING_SNAKE_CASE			,     ) as writer:
    with pytest.raises(__SCREAMING_SNAKE_CASE ):
      writer.write({"""col_1""": """foo""", """col_2""": 1}			,     key=10 )
      writer.write({"""col_1""": """bar""", """col_2""": 2}			,     key=10 )
      __A							,					__A		:     str												=		writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 2, 10] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       int:





  """simple docstring"""

  __A		:     Union[str, Any]												=		pa.BufferOutputStream()
  with ArrowWriter(
      stream=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE			,     hash_salt="""split_name"""			,     check_duplicates=__SCREAMING_SNAKE_CASE			,     ) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}			,     key=1 )
    writer.write({"""col_1""": """bar""", """col_2""": 2}			,     key=2 )
    __A							,					__A		:     Tuple												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
@pytest.mark.parametrize(
    """fields"""			,     [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  Any )				->       List[str]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		pa.BufferOutputStream()
  __A		:     List[Any]												=		pa.schema(__SCREAMING_SNAKE_CASE ) if fields else None
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     schema=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
    writer.write_batch({"""col_1""": [], """col_2""": []} )
    __A							,					__A		:     List[str]												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __A		:     Any												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
@pytest.mark.parametrize(
    """fields"""			,     [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  List[Any] )				->       Optional[Any]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		pa.BufferOutputStream()
  __A		:     Optional[int]												=		pa.schema(__SCREAMING_SNAKE_CASE ) if fields else None
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     schema=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} ) )
    __A							,					__A		:     Optional[Any]												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __A		:     Union[str, Any]												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







@pytest.mark.parametrize("""writer_batch_size"""			,     [None, 1, 10] )
@pytest.mark.parametrize(
    """fields"""			,     [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       Optional[int]:





  """simple docstring"""

  __A		:     Any												=		pa.BufferOutputStream()
  __A		:     Optional[Any]												=		pa.schema(__SCREAMING_SNAKE_CASE ) if fields else None
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE			,     schema=__SCREAMING_SNAKE_CASE			,     writer_batch_size=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]} ) )
    writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]} ) )
    __A							,					__A		:     Optional[int]												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __A		:     Tuple												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
  _check_output(output.getvalue()			,     expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )







def        A_     (						)				->       str:





  """simple docstring"""

  with tempfile.TemporaryDirectory() as tmp_dir:
    __A		:     Optional[Any]												=		{"""col_1""": pa.string(), """col_2""": pa.intaa()}
    __A		:     List[Any]												=		os.path.join(__SCREAMING_SNAKE_CASE			,     """test.arrow""" )
    with ArrowWriter(path=__SCREAMING_SNAKE_CASE			,     schema=pa.schema(__SCREAMING_SNAKE_CASE ) ) as writer:
      writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
      __A							,					__A		:     Any												=		writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == pa.schema(__SCREAMING_SNAKE_CASE			,     metadata=writer._schema.metadata )
    _check_output(__SCREAMING_SNAKE_CASE			,     1 )







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[int] )				->       int:





  """simple docstring"""

  if pa.types.is_list(__SCREAMING_SNAKE_CASE ):
    return get_base_dtype(arr_type.value_type )
  else:
    return arr_type







def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  List[Any] )				->       Tuple:





  """simple docstring"""

  if isinstance(lst[0]			,     __SCREAMING_SNAKE_CASE ):
    change_first_primitive_element_in_list(lst[0]			,     __SCREAMING_SNAKE_CASE )
  else:
    __A		:     int												=		value







@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""			,     [(None, pa.intaa()), (Value("""int32""" ), pa.intaa())] )
@pytest.mark.parametrize("""sequence"""			,     [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  List[Any] )				->       Union[str, Any]:





  """simple docstring"""

  __A		:     List[str]												=		pa.array(TypedSequence(__SCREAMING_SNAKE_CASE			,     optimized_int_type=__SCREAMING_SNAKE_CASE ) )
  assert get_base_dtype(arr.type ) == expected_dtype







@pytest.mark.parametrize(
    """col, expected_dtype"""			,     [
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]			,     )
@pytest.mark.parametrize("""sequence"""			,     [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Union[str, Any]:





  """simple docstring"""

  __A		:     int												=		pa.array(OptimizedTypedSequence(__SCREAMING_SNAKE_CASE			,     col=__SCREAMING_SNAKE_CASE ) )
  assert get_base_dtype(arr.type ) == expected_dtype

  # not in range
  if col != "other":
    # avoids errors due to in-place modifications
    __A		:     Optional[Any]												=		copy.deepcopy(__SCREAMING_SNAKE_CASE )
    __A		:     List[Any]												=		np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
    change_first_primitive_element_in_list(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    __A		:     Dict												=		pa.array(OptimizedTypedSequence(__SCREAMING_SNAKE_CASE			,     col=__SCREAMING_SNAKE_CASE ) )
    assert get_base_dtype(arr.type ) == pa.intaa()







@pytest.mark.parametrize("""raise_exception"""			,     [False, True] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       Tuple:





  """simple docstring"""

  __A		:     int												=		str(tmp_path / """dataset-train.arrow""" )
  try:
    with ArrowWriter(path=__SCREAMING_SNAKE_CASE ) as writer:
      if raise_exception:
        raise pa.lib.ArrowInvalid()
      else:
        writer.stream.close()
  except pa.lib.ArrowInvalid:
    pass
  finally:
    assert writer.stream.closed







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Tuple:





  """simple docstring"""

  __A		:     Tuple												=		"""mock://dataset-train.arrow"""
  with ArrowWriter(path=__SCREAMING_SNAKE_CASE			,     storage_options=mockfs.storage_options ) as writer:
    assert isinstance(writer._fs			,     type(__SCREAMING_SNAKE_CASE ) )
    assert writer._fs.storage_options == mockfs.storage_options
    writer.write({"""col_1""": """foo""", """col_2""": 1} )
    writer.write({"""col_1""": """bar""", """col_2""": 2} )
    __A							,					__A		:     Any												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert mockfs.exists(__SCREAMING_SNAKE_CASE )







def        A_     (						)				->       Dict:





  """simple docstring"""

  __A		:     List[str]												=		pa.BufferOutputStream()
  with ParquetWriter(stream=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1} )
    writer.write({"""col_1""": """bar""", """col_2""": 2} )
    __A							,					__A		:     Tuple												=		writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  __A		:     Dict												=		pa.BufferReader(output.getvalue() )
  __A		:     pa.Table												=		pq.read_table(__SCREAMING_SNAKE_CASE )
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}







@require_pil
@pytest.mark.parametrize("""embed_local_files"""			,     [False, True] )
def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Optional[Any]:





  """simple docstring"""

  import PIL.Image

  __A		:     Optional[int]												=		str(tmp_path / """test_image_rgb.jpg""" )
  PIL.Image.fromarray(np.zeros((5, 5)			,     dtype=np.uinta ) ).save(__SCREAMING_SNAKE_CASE			,     format="""png""" )
  __A		:     Any												=		pa.BufferOutputStream()
  with ParquetWriter(
      stream=__SCREAMING_SNAKE_CASE			,     features=Features({"""image""": Image()} )			,     embed_local_files=__SCREAMING_SNAKE_CASE ) as writer:
    writer.write({"""image""": image_path} )
    writer.finalize()
  __A		:     str												=		pa.BufferReader(output.getvalue() )
  __A		:     pa.Table												=		pq.read_table(__SCREAMING_SNAKE_CASE )
  __A		:     Optional[Any]												=		pa_table.to_pydict()
  if embed_local_files:
    assert isinstance(out["""image"""][0]["""path"""]			,     __SCREAMING_SNAKE_CASE )
    with open(__SCREAMING_SNAKE_CASE			,     """rb""" ) as f:
      assert out["image"][0]["bytes"] == f.read()
  else:
    assert out["image"][0]["path"] == image_path
    assert out["image"][0]["bytes"] is None







def        A_     (						)				->       List[Any]:





  """simple docstring"""

  __A		:     Any												=		pa.schema([pa.field("""col_1"""			,     pa.string()			,     nullable=__SCREAMING_SNAKE_CASE )] )
  __A		:     List[Any]												=		pa.BufferOutputStream()
  with ArrowWriter(stream=__SCREAMING_SNAKE_CASE ) as writer:
    writer._build_writer(inferred_schema=__SCREAMING_SNAKE_CASE )
    assert writer._schema == pa.schema([pa.field("""col_1"""			,     pa.string() )] )





'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__   :		int 		=logging.get_logger(__name__)

A__   :		Union[str, Any] 		={
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''dpr'''
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    Tuple=3_05_22				,   lowerCamelCase			:    List[str]=7_68				,   lowerCamelCase			:    Union[str, Any]=12				,   lowerCamelCase			:    Tuple=12				,   lowerCamelCase			:    Dict=30_72				,   lowerCamelCase			:    List[Any]="gelu"				,   lowerCamelCase			:    List[Any]=0.1				,   lowerCamelCase			:    Union[str, Any]=0.1				,   lowerCamelCase			:    Optional[Any]=5_12				,   lowerCamelCase			:    List[Any]=2				,   lowerCamelCase			:    int=0.02				,   lowerCamelCase			:    Union[str, Any]=1e-1_2				,   lowerCamelCase			:    int=0				,   lowerCamelCase			:    List[str]="absolute"				,   lowerCamelCase			:    int = 0				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   super().__init__(pad_token_id=lowerCamelCase				,   **lowerCamelCase       )

   __A		:     Dict												=		vocab_size
   __A		:     int												=		hidden_size
   __A		:     str												=		num_hidden_layers
   __A		:     Union[str, Any]												=		num_attention_heads
   __A		:     Union[str, Any]												=		hidden_act
   __A		:     Optional[Any]												=		intermediate_size
   __A		:     str												=		hidden_dropout_prob
   __A		:     int												=		attention_probs_dropout_prob
   __A		:     int												=		max_position_embeddings
   __A		:     Dict												=		type_vocab_size
   __A		:     Dict												=		initializer_range
   __A		:     Union[str, Any]												=		layer_norm_eps
   __A		:     int												=		projection_dim
   __A		:     List[Any]												=		position_embedding_type



