





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


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



@dataclass
class 							_UpperCAmelCase       (       datasets.BuilderConfig       ):

  """simple docstring"""


  a_              =					None
  a_              =					"""utf-8"""
  a_              =					None
  a_              =					None
  a_              =					True  # deprecated
  a_              =					None  # deprecated
  a_              =					10 << 20  # 10MB
  a_              =					None





class 							_UpperCAmelCase       (       datasets.ArrowBasedBuilder       ):

  """simple docstring"""


  a_              =					JsonConfig






  def 							lowercase						(							self					:						List[str]	)				->		Optional[Any]:
     if self.config.block_size is not None:
        logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'	)
        __lowerCAmelCase				      =      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 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						Union[str, Any]	)				->		Union[str, Any]:
     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}"""	)
     __lowerCAmelCase				      =      dl_manager.download_and_extract(self.config.data_files	)
     if isinstance(_lowerCAmelCase   ,    (str, list, tuple)	):
        __lowerCAmelCase				      =      data_files
        if isinstance(_lowerCAmelCase   ,    _lowerCAmelCase	):
           __lowerCAmelCase				      =      [files]
        __lowerCAmelCase				      =      [dl_manager.iter_files(_lowerCAmelCase	) for file in files]
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,    gen_kwargs={'files': files}	)]
     __lowerCAmelCase				      =      []
     for split_name, files in data_files.items():
        if isinstance(_lowerCAmelCase   ,    _lowerCAmelCase	):
           __lowerCAmelCase				      =      [files]
        __lowerCAmelCase				      =      [dl_manager.iter_files(_lowerCAmelCase	) for file in files]
        splits.append(datasets.SplitGenerator(name=_lowerCAmelCase   ,    gen_kwargs={'files': files}	)	)
     return splits






  def 							lowercase						(							self					:						Optional[int]   ,    lowerCAmelCase_					:						pa.Table	)				->		pa.Table:
     if self.config.features is not None:
        # adding missing columns
        for column_name in set(self.config.features	) - set(pa_table.column_names	):
           __lowerCAmelCase				      =      self.config.features.arrow_schema.field(_lowerCAmelCase	).type
           __lowerCAmelCase				      =      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
        __lowerCAmelCase				      =      table_cast(_lowerCAmelCase   ,    self.config.features.arrow_schema	)
     return pa_table






  def 							lowercase						(							self					:						int   ,    lowerCAmelCase_					:						Optional[int]	)				->		Tuple:
     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:
              __lowerCAmelCase				      =      json.load(_lowerCAmelCase	)

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

           # We accept two format: a list of dicts or a dict of lists
           if isinstance(_lowerCAmelCase   ,    (list, tuple)	):
              __lowerCAmelCase				      =      set().union(*[row.keys() for row in dataset]	)
              __lowerCAmelCase				      =      {col: [row.get(_lowerCAmelCase	) for row in dataset] for col in keys}
           else:
              __lowerCAmelCase				      =      dataset
           __lowerCAmelCase				      =      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:
              __lowerCAmelCase				      =      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
              __lowerCAmelCase				      =      max(self.config.chunksize // 3_2   ,    1_6 << 1_0	)
              __lowerCAmelCase				      =      (
                  self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
              )
              while True:
                 __lowerCAmelCase				      =      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":
                    __lowerCAmelCase				      =      batch.decode(self.config.encoding   ,    errors=_lowerCAmelCase	).encode('utf-8'	)
                 try:
                    while True:
                       try:
                          __lowerCAmelCase				      =      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:
                          __lowerCAmelCase				      =      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:
                          __lowerCAmelCase				      =      set().union(*[row.keys() for row in dataset]	)
                          __lowerCAmelCase				      =      {col: [row.get(_lowerCAmelCase	) for row in dataset] for col in keys}
                          __lowerCAmelCase				      =      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





from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger(__name__)


class      __lowerCAmelCase  :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							42
  _SCREAMING_SNAKE_CASE		     =							None



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Any:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							Dict     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[str]	) ->			Optional[int]:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			List[str]:




     """simple docstring"""
     if not self.is_available():
        raise RuntimeError(
            F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''	)



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Dict	) ->			Tuple:




     """simple docstring"""
     return F'''`pip install {cls.pip_package or cls.name}`'''






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'optuna'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Optional[Any]:




     """simple docstring"""
     return is_optuna_available()



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Optional[int]	) ->			Optional[int]:




     """simple docstring"""
     return run_hp_search_optuna(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     return default_hp_space_optuna(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'ray'
  _SCREAMING_SNAKE_CASE		     =							'\'ray[tune]\''



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Optional[Any]:




     """simple docstring"""
     return is_ray_available()



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							List[Any]	) ->			List[str]:




     """simple docstring"""
     return run_hp_search_ray(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							List[Any]	) ->			Any:




     """simple docstring"""
     return default_hp_space_ray(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'sigopt'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			List[str]:




     """simple docstring"""
     return is_sigopt_available()



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Tuple	) ->			str:




     """simple docstring"""
     return run_hp_search_sigopt(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Tuple	) ->			Union[str, Any]:




     """simple docstring"""
     return default_hp_space_sigopt(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'wandb'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			List[Any]:




     """simple docstring"""
     return is_wandb_available()



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Any	) ->			Dict:




     """simple docstring"""
     return run_hp_search_wandb(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[str]	) ->			Any:




     """simple docstring"""
     return default_hp_space_wandb(_lowerCAmelCase	)






SCREAMING_SNAKE_CASE						:Union[str, Any]			=  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}



def       _lowerCAmelCase    (				)->str:
   '''simple docstring'''






   snake_case_            =       [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
   if len(lowerCAmelCase_				) > 0:
      snake_case_            =       available_backends[0].name
      if len(lowerCAmelCase_				) > 1:
         logger.info(
             F'''{len(lowerCAmelCase_				)} hyperparameter search backends available. Using {name} as the default.'''				)
      return name
   raise RuntimeError(
       "No hyperparameter search backend available.\n"
       + "\n".join(
           F''' - To install {backend.name} run {backend.pip_install()}'''
           for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()				)				)




