import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__lowerCamelCase    :				Optional[Any]							     =	datasets.utils.logging.get_logger(__name__)

__lowerCamelCase    :				Union[str, Any]							     =	['''names''', '''prefix''']
__lowerCamelCase    :				Any							     =	['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__lowerCamelCase    :				Any							     =	['''encoding_errors''', '''on_bad_lines''']
__lowerCamelCase    :				List[str]							     =	['''date_format''']







@dataclass
class __snake_case       (		datasets.BuilderConfig	):
   lowerCAmelCase_														=							","
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							"infer"
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							"."
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							'"'
   lowerCAmelCase_														=							0
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							0
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							1_00_00
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							"strict"
   lowerCAmelCase_														=							"error"
   lowerCAmelCase_														=							None




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     if self.delimiter is not None:
       SCREAMING_SNAKE_CASE__    							=   self.delimiter
     if self.column_names is not None:
       SCREAMING_SNAKE_CASE__    							=   self.column_names




   @property
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {
         "sep": self.sep,
         "header": self.header,
         "names": self.names,
         "index_col": self.index_col,
         "usecols": self.usecols,
         "prefix": self.prefix,
         "mangle_dupe_cols": self.mangle_dupe_cols,
         "engine": self.engine,
         "converters": self.converters,
         "true_values": self.true_values,
         "false_values": self.false_values,
         "skipinitialspace": self.skipinitialspace,
         "skiprows": self.skiprows,
         "nrows": self.nrows,
         "na_values": self.na_values,
         "keep_default_na": self.keep_default_na,
         "na_filter": self.na_filter,
         "verbose": self.verbose,
         "skip_blank_lines": self.skip_blank_lines,
         "thousands": self.thousands,
         "decimal": self.decimal,
         "lineterminator": self.lineterminator,
         "quotechar": self.quotechar,
         "quoting": self.quoting,
         "escapechar": self.escapechar,
         "comment": self.comment,
         "encoding": self.encoding,
         "dialect": self.dialect,
         "error_bad_lines": self.error_bad_lines,
         "warn_bad_lines": self.warn_bad_lines,
         "skipfooter": self.skipfooter,
         "doublequote": self.doublequote,
         "memory_map": self.memory_map,
         "float_precision": self.float_precision,
         "chunksize": self.chunksize,
         "encoding_errors": self.encoding_errors,
         "on_bad_lines": self.on_bad_lines,
         "date_format": self.date_format,
     }

     # some kwargs must not be passed if they don't have a default value
     # some others are deprecated and we can also not pass them if they are the default value
     for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
       if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() ,			lowerCAmelCase__   ):
         del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
     if not (datasets.config.PANDAS_VERSION.major >= 2):
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
         del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
     if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
         del pd_read_csv_kwargs[pd_read_csv_parameter]

     return pd_read_csv_kwargs







class __snake_case       (		datasets.ArrowBasedBuilder	):
   lowerCAmelCase_														=							CsvConfig




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     return datasets.DatasetInfo(features=self.config.features   )




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     if not self.config.data_files:
       raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
     SCREAMING_SNAKE_CASE__    							=   dl_manager.download_and_extract(self.config.data_files   )
     if isinstance(lowerCAmelCase__ ,			(str, list, tuple)   ):
       SCREAMING_SNAKE_CASE__    							=   data_files
       if isinstance(lowerCAmelCase__ ,			lowerCAmelCase__   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(lowerCAmelCase__   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,			gen_kwargs={"""files""": files}   )]
     SCREAMING_SNAKE_CASE__    							=   []
     for split_name, files in data_files.items():
       if isinstance(lowerCAmelCase__ ,			lowerCAmelCase__   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(lowerCAmelCase__   ) for file in files]
       splits.append(datasets.SplitGenerator(name=lowerCAmelCase__ ,			gen_kwargs={"""files""": files}   )   )
     return splits




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     if self.config.features is not None:
       SCREAMING_SNAKE_CASE__    							=   self.config.features.arrow_schema
       if all(not require_storage_cast(lowerCAmelCase__   ) for feature in self.config.features.values()   ):
         # cheaper cast
         SCREAMING_SNAKE_CASE__    							=   pa.Table.from_arrays([pa_table[field.name] for field in schema] ,			schema=lowerCAmelCase__   )
       else:
         # more expensive cast; allows str <-> int/float or str to Audio for example
         SCREAMING_SNAKE_CASE__    							=   table_cast(lowerCAmelCase__ ,			lowerCAmelCase__   )
     return pa_table




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.config.features.arrow_schema if self.config.features else None
     # dtype allows reading an int column as str
     SCREAMING_SNAKE_CASE__    							=   (
         {
             name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__   ) else object
             for name, dtype, feature in zip(schema.names ,			schema.types ,			self.config.features.values()   )
         }
         if schema is not None
         else None
     )
     for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__   )   ):
       SCREAMING_SNAKE_CASE__    							=   pd.read_csv(lowerCAmelCase__ ,			iterator=lowerCAmelCase__ ,			dtype=lowerCAmelCase__ ,			**self.config.pd_read_csv_kwargs   )
       try:
         for batch_idx, df in enumerate(lowerCAmelCase__   ):
           SCREAMING_SNAKE_CASE__    							=   pa.Table.from_pandas(lowerCAmelCase__   )
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__   )
       except ValueError as e:
         logger.error(f"""Failed to read file '{file}' with error {type(lowerCAmelCase__   )}: {e}"""   )
         raise

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 __snake_case       (		lowerCamelCase_	):




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3]   )   )
     self.assertEqual(arr.type ,			pa.intaa()   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     with self.assertRaises(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3]   ) ,			type=pa.intaa()   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     with self.assertRaises(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3] ,			try_type=Value("""bool"""   ) ,			type=Value("""int64"""   )   )   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3] ,			type=Value("""int32"""   )   )   )
     self.assertEqual(arr.type ,			pa.intaa()   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			type=Value("""int64"""   )   )   )




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3] ,			try_type=Value("""int32"""   )   )   )
     self.assertEqual(arr.type ,			pa.intaa()   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			try_type=Value("""int64"""   )   )   )
     self.assertEqual(arr.type ,			pa.string()   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([[[1, 2, 3]]] ,			type=ArrayaD((1, 3) ,			"""int64"""   )   )   )
     self.assertEqual(arr.type ,			ArrayaDExtensionType((1, 3) ,			"""int64"""   )   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			type=ArrayaD((1, 3) ,			"""int64"""   )   )   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([[[1, 2, 3]]] ,			try_type=ArrayaD((1, 3) ,			"""int64"""   )   )   )
     self.assertEqual(arr.type ,			ArrayaDExtensionType((1, 3) ,			"""int64"""   )   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






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




   @require_pil
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     import PIL.Image

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





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: int			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(__UpperCamelCase			) if isinstance(__UpperCamelCase				,  pa.Buffer			) else pa.memory_map(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   pa.ipc.open_stream(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: int			)							->      List[str]:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      )							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]			)}			)
  with ArrowWriter(stream=__UpperCamelCase				,  features=__UpperCamelCase			) as writer:
    writer.write({"""labels""": 0}			)
    writer.write({"""labels""": 1}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == features.arrow_schema
  assert writer._schema.metadata == features.arrow_schema.metadata
  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(output.getvalue()			)
  SCREAMING_SNAKE_CASE__    							=   pa.ipc.open_stream(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   f.read_all()
  SCREAMING_SNAKE_CASE__    							=   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(__UpperCamelCase			)



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







  """simple docstring"""





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



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







  """simple docstring"""





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



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ArrowWriter(
      stream=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase				,  hash_salt="""split_name"""				,  check_duplicates=__UpperCamelCase				,  ) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}				,  key=1			)
    writer.write({"""col_1""": """bar""", """col_2""": 2}				,  key=2			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[int]:







  """simple docstring"""





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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) as writer:
    writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}			)			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: List[str]			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) 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]}			)			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      List[Any]:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      Tuple:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict				,  __UpperCamelCase					: Union[str, Any]			)							->      Tuple:







  """simple docstring"""





  if isinstance(lst[0]				,  __UpperCamelCase			):
    change_first_primitive_element_in_list(lst[0]				,  __UpperCamelCase			)
  else:
    SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Dict			)							->      Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(__UpperCamelCase				,  optimized_int_type=__UpperCamelCase			)			)
  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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: List[str]			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.array(OptimizedTypedSequence(__UpperCamelCase				,  col=__UpperCamelCase			)			)
  assert get_base_dtype(arr.type			) == expected_dtype

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



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







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      str:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      )							->      str:







  """simple docstring"""





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



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







  """simple docstring"""





  import PIL.Image

  SCREAMING_SNAKE_CASE__    							=   str(tmp_path / """test_image_rgb.jpg"""			)
  PIL.Image.fromarray(np.zeros((5, 5)				,  dtype=np.uinta			)			).save(__UpperCamelCase				,  format="""png"""			)
  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ParquetWriter(
      stream=__UpperCamelCase				,  features=Features({"""image""": Image()}			)				,  embed_local_files=__UpperCamelCase			) as writer:
    writer.write({"""image""": image_path}			)
    writer.finalize()
  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(output.getvalue()			)
  SCREAMING_SNAKE_CASE__    							=   pq.read_table(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   pa_table.to_pydict()
  if embed_local_files:
    assert isinstance(out["""image"""][0]["""path"""]				,  __UpperCamelCase			)
    with open(__UpperCamelCase				,  """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 			__SCREAMING_SNAKE_CASE						(      )							->      List[Any]:







  """simple docstring"""





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