
"""simple docstring"""






from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching




def       A_  (				):
 import os as original_os
 from os import path as original_path
 from os import rename as original_rename
 from os.path import dirname as original_dirname
 from os.path import join as original_join

 assert _test_patching.os is original_os
 assert _test_patching.path is original_path
 assert _test_patching.join is original_join

 assert _test_patching.renamed_os is original_os
 assert _test_patching.renamed_path is original_path
 assert _test_patching.renamed_join is original_join

 UpperCamelCase_     :					Tuple   	='__test_patch_submodule_mock__'
 with patch_submodule(_test_patching						,	'os.path.join'						,	__lowercase	):
  # Every way to access os.path.join must be patched, and the rest must stay untouched

  # check os.path.join
  assert isinstance(_test_patching.os						,	_PatchedModuleObj	)
  assert isinstance(_test_patching.os.path						,	_PatchedModuleObj	)
  assert _test_patching.os.path.join is mock

  # check path.join
  assert isinstance(_test_patching.path						,	_PatchedModuleObj	)
  assert _test_patching.path.join is mock

  # check join
  assert _test_patching.join is mock

  # check that the other attributes are untouched
  assert _test_patching.os.rename is original_rename
  assert _test_patching.path.dirname is original_dirname
  assert _test_patching.os.path.dirname is original_dirname

  # Even renamed modules or objects must be patched

  # check renamed_os.path.join
  assert isinstance(_test_patching.renamed_os						,	_PatchedModuleObj	)
  assert isinstance(_test_patching.renamed_os.path						,	_PatchedModuleObj	)
  assert _test_patching.renamed_os.path.join is mock

  # check renamed_path.join
  assert isinstance(_test_patching.renamed_path						,	_PatchedModuleObj	)
  assert _test_patching.renamed_path.join is mock

  # check renamed_join
  assert _test_patching.renamed_join is mock

  # check that the other attributes are untouched
  assert _test_patching.renamed_os.rename is original_rename
  assert _test_patching.renamed_path.dirname is original_dirname
  assert _test_patching.renamed_os.path.dirname is original_dirname

 # check that everthing is back to normal when the patch is over

 assert _test_patching.os is original_os
 assert _test_patching.path is original_path
 assert _test_patching.join is original_join

 assert _test_patching.renamed_os is original_os
 assert _test_patching.renamed_path is original_path
 assert _test_patching.renamed_join is original_join




def       A_  (				):
 assert _test_patching.open is open

 UpperCamelCase_     :					Optional[Any]   	='__test_patch_submodule_builtin_mock__'
 # _test_patching has "open" in its globals
 assert _test_patching.open is open
 with patch_submodule(_test_patching						,	'open'						,	__lowercase	):
  assert _test_patching.open is mock

 # check that everthing is back to normal when the patch is over

 assert _test_patching.open is open




def       A_  (				):
 # pandas.read_csv is not present in _test_patching
 UpperCamelCase_     :					str   	='__test_patch_submodule_missing_mock__'
 with patch_submodule(_test_patching						,	'pandas.read_csv'						,	__lowercase	):
  pass




def       A_  (				):
 # builtin should always be mocked even if they're not in the globals
 # in case they're loaded at one point
 UpperCamelCase_     :					List[Any]   	='__test_patch_submodule_missing_builtin_mock__'
 # _test_patching doesn't have "len" in its globals
 assert getattr(_test_patching						,	'len'						,	__lowercase	) is None
 with patch_submodule(_test_patching						,	'len'						,	__lowercase	):
  assert _test_patching.len is mock
 assert _test_patching.len is len




def       A_  (				):
 UpperCamelCase_     :					Tuple   	='__test_patch_submodule_start_and_stop_mock__'
 UpperCamelCase_     :					Union[str, Any]   	=patch_submodule(_test_patching						,	'open'						,	__lowercase	)
 assert _test_patching.open is open
 patch.start()
 assert _test_patching.open is mock
 patch.stop()
 assert _test_patching.open is open




def       A_  (				):
 from os import rename as original_rename
 from os.path import dirname as original_dirname
 from os.path import join as original_join

 UpperCamelCase_     :					Optional[int]   	='__test_patch_submodule_successive_join__'
 UpperCamelCase_     :					Any   	='__test_patch_submodule_successive_dirname__'
 UpperCamelCase_     :					Optional[int]   	='__test_patch_submodule_successive_rename__'
 assert _test_patching.os.path.join is original_join
 assert _test_patching.os.path.dirname is original_dirname
 assert _test_patching.os.rename is original_rename

 with patch_submodule(_test_patching						,	'os.path.join'						,	__lowercase	):
  with patch_submodule(_test_patching						,	'os.rename'						,	__lowercase	):
   with patch_submodule(_test_patching						,	'os.path.dirname'						,	__lowercase	):
    assert _test_patching.os.path.join is mock_join
    assert _test_patching.os.path.dirname is mock_dirname
    assert _test_patching.os.rename is mock_rename

    # try another order
 with patch_submodule(_test_patching						,	'os.rename'						,	__lowercase	):
  with patch_submodule(_test_patching						,	'os.path.join'						,	__lowercase	):
   with patch_submodule(_test_patching						,	'os.path.dirname'						,	__lowercase	):
    assert _test_patching.os.path.join is mock_join
    assert _test_patching.os.path.dirname is mock_dirname
    assert _test_patching.os.rename is mock_rename

 assert _test_patching.os.path.join is original_join
 assert _test_patching.os.path.dirname is original_dirname
 assert _test_patching.os.rename is original_rename





def       A_  (				):
 UpperCamelCase_     :					Optional[int]   	='__test_patch_submodule_doesnt_exist_mock__'
 with patch_submodule(_test_patching						,	'__module_that_doesn_exist__.__attribute_that_doesn_exist__'						,	__lowercase	):
  pass
 with patch_submodule(_test_patching						,	'os.__attribute_that_doesn_exist__'						,	__lowercase	):
  pass


"""simple docstring"""






import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__SCREAMING_SNAKE_CASE							=     datasets.utils.logging.get_logger(__name__)





@dataclass
class 				a__					(						datasets.BuilderConfig       ):
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      "utf-8"
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      None
     UpperCAmelCase__         =      True  # deprecated
     UpperCAmelCase__         =      None  # deprecated
     UpperCAmelCase__         =      10 << 20  # 10MB
     UpperCAmelCase__         =      None





class 				a__					(						datasets.ArrowBasedBuilder       ):
     UpperCAmelCase__         =      JsonConfig



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      if self.config.block_size is not None:
       logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'   )
       UpperCamelCase_     :					List[Any]   	=self.config.block_size
      if self.config.use_threads is not True:
       logger.warning(
           'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'   )
      if self.config.newlines_in_values is not None:
       raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported'   )
      return datasets.DatasetInfo(features=self.config.features   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[str]   ):







      '''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}'''   )
      UpperCamelCase_     :					int   	=dl_manager.download_and_extract(self.config.data_files   )
      if isinstance(_lowerCamelCase      ,  (str, list, tuple)   ):
       UpperCamelCase_     :					Tuple   	=data_files
       if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):
        UpperCamelCase_     :					Union[str, Any]   	=[files]
       UpperCamelCase_     :					List[str]   	=[dl_manager.iter_files(_lowerCamelCase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,  gen_kwargs={'files': files}   )]
      UpperCamelCase_     :					Optional[Any]   	=[]
      for split_name, files in data_files.items():
       if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):
        UpperCamelCase_     :					Tuple   	=[files]
       UpperCamelCase_     :					Optional[Any]   	=[dl_manager.iter_files(_lowerCamelCase   ) for file in files]
       splits.append(datasets.SplitGenerator(name=_lowerCamelCase      ,  gen_kwargs={'files': files}   )   )
      return splits



     def  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :pa.Table   ):







      '''simple docstring'''



      if self.config.features is not None:
       # adding missing columns
       for column_name in set(self.config.features   ) - set(pa_table.column_names   ):
        UpperCamelCase_     :					Dict   	=self.config.features.arrow_schema.field(_lowerCamelCase   ).type
        UpperCamelCase_     :					Optional[Any]   	=pa_table.append_column(_lowerCamelCase      ,  pa.array([None] * len(_lowerCamelCase   )      ,  type=_lowerCamelCase   )   )
       # more expensive cast to support nested structures with keys in a different order
       # allows str <-> int/float or str to Audio for example
       UpperCamelCase_     :					int   	=table_cast(_lowerCamelCase      ,  self.config.features.arrow_schema   )
      return pa_table




     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase   )   ):
       # If the file is one json object and if we need to look at the list of items in one specific field
       if self.config.field is not None:
        with open(_lowerCamelCase      ,  encoding=self.config.encoding      ,  errors=self.config.encoding_errors   ) as f:
         UpperCamelCase_     :					List[str]   	=json.load(_lowerCamelCase   )

        # We keep only the field we are interested in
        UpperCamelCase_     :					Tuple   	=dataset[self.config.field]

        # We accept two format: a list of dicts or a dict of lists
        if isinstance(_lowerCamelCase      ,  (list, tuple)   ):
         UpperCamelCase_     :					Tuple   	=set().union(*[row.keys() for row in dataset]   )
         UpperCamelCase_     :					str   	={col: [row.get(_lowerCamelCase   ) for row in dataset] for col in keys}
        else:
         UpperCamelCase_     :					Any   	=dataset
        UpperCamelCase_     :					List[Any]   	=pa.Table.from_pydict(_lowerCamelCase   )
        yield file_idx, self._cast_table(_lowerCamelCase   )

       # If the file has one json object per line
       else:
        with open(_lowerCamelCase      ,  'rb'   ) as f:
         UpperCamelCase_     :					Any   	=0
         # Use block_size equal to the chunk size divided by 32 to leverage multithreading
         # Set a default minimum value of 16kB if the chunk size is really small
         UpperCamelCase_     :					List[str]   	=max(self.config.chunksize // 32      ,  16 << 10   )
         UpperCamelCase_     :					Dict   	=(
             self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
         )
         while True:
          UpperCamelCase_     :					Optional[Any]   	=f.read(self.config.chunksize   )
          if not batch:
           break
          # Finish current line
          try:
           batch += f.readline()
          except (AttributeError, io.UnsupportedOperation):
           batch += readline(_lowerCamelCase   )
          # PyArrow only accepts utf-8 encoded bytes
          if self.config.encoding != "utf-8":
           UpperCamelCase_     :					List[Any]   	=batch.decode(self.config.encoding      ,  errors=_lowerCamelCase   ).encode('utf-8'   )
          try:
           while True:
            try:
             UpperCamelCase_     :					str   	=paj.read_json(
                 io.BytesIO(_lowerCamelCase   )      ,  read_options=paj.ReadOptions(block_size=_lowerCamelCase   )   )
             break
            except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
             if (
                 isinstance(_lowerCamelCase      ,  pa.ArrowInvalid   )
                 and "straddling" not in str(_lowerCamelCase   )
                 or block_size > len(_lowerCamelCase   )
             ):
              raise
             else:
              # Increase the block size in case it was too small.
              # The block size will be reset for the next file.
              logger.debug(
                  f'''Batch of {len(_lowerCamelCase   )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''   )
              block_size *= 2
          except pa.ArrowInvalid as e:
           try:
            with open(
                _lowerCamelCase      ,  encoding=self.config.encoding      ,  errors=self.config.encoding_errors   ) as f:
             UpperCamelCase_     :					List[Any]   	=json.load(_lowerCamelCase   )
           except json.JSONDecodeError:
            logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
            raise e
           # If possible, parse the file as a list of json objects and exit the loop
           if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):  # list is the only sequence type supported in JSON
            try:
             UpperCamelCase_     :					str   	=set().union(*[row.keys() for row in dataset]   )
             UpperCamelCase_     :					List[Any]   	={col: [row.get(_lowerCamelCase   ) for row in dataset] for col in keys}
             UpperCamelCase_     :					Optional[Any]   	=pa.Table.from_pydict(_lowerCamelCase   )
            except (pa.ArrowInvalid, AttributeError) as e:
             logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
             raise ValueError(f'''Not able to read records in the JSON file at {file}.'''   ) from None
            yield file_idx, self._cast_table(_lowerCamelCase   )
            break
           else:
            logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase   )}: {e}'''   )
            raise ValueError(
                f'''Not able to read records in the JSON file at {file}. '''
                f'''You should probably indicate the field of the JSON file containing your records. '''
                f'''This JSON file contain the following fields: {str(list(dataset.keys()   )   )}. '''
                f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''   ) from None
                        # 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   )
          batch_idx += 1
