"""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        UpperCamelCase		(			datasets.BuilderConfig  ):
   lowercase							=  None
   lowercase							=  "utf-8"
   lowercase							=  None
   lowercase							=  None
   lowercase							=  True  # deprecated
   lowercase							=  None  # deprecated
   lowercase							=  1_0 << 2_0  # 10MB
   lowercase							=  None







class        UpperCamelCase		(			datasets.ArrowBasedBuilder  ):
   lowercase							=  JsonConfig
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        if self.config.block_size is not None:
             logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'			)
             lowercase_	:				Union[str, 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 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      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}'''			)
        lowercase_	:				Any													=	dl_manager.download_and_extract(self.config.data_files			)
        if isinstance(__UpperCamelCase     ,(str, list, tuple)			):
             lowercase_	:				Optional[int]													=	data_files
             if isinstance(__UpperCamelCase     ,__UpperCamelCase			):
                  lowercase_	:				Any													=	[files]
             lowercase_	:				Union[str, Any]													=	[dl_manager.iter_files(__UpperCamelCase			) for file in files]
             return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,gen_kwargs={'files': files}			)]
        lowercase_	:				int													=	[]
        for split_name, files in data_files.items():
             if isinstance(__UpperCamelCase     ,__UpperCamelCase			):
                  lowercase_	:				str													=	[files]
             lowercase_	:				Optional[int]													=	[dl_manager.iter_files(__UpperCamelCase			) for file in files]
             splits.append(datasets.SplitGenerator(name=__UpperCamelCase     ,gen_kwargs={'files': files}			)			)
        return splits
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      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			):
                  lowercase_	:				Optional[int]													=	self.config.features.arrow_schema.field(__UpperCamelCase			).type
                  lowercase_	:				List[str]													=	pa_table.append_column(__UpperCamelCase     ,pa.array([None] * len(__UpperCamelCase			)     ,type=__UpperCamelCase			)			)
             # more expensive cast to support nested structures with keys in a different order
             # allows str <-> int/float or str to Audio for example
             lowercase_	:				Dict													=	table_cast(__UpperCamelCase     ,self.config.features.arrow_schema			)
        return pa_table







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCamelCase			)			):
             # 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(__UpperCamelCase     ,encoding=self.config.encoding     ,errors=self.config.encoding_errors			) as f:
                       lowercase_	:				Union[str, Any]													=	json.load(__UpperCamelCase			)

                  # We keep only the field we are interested in
                  lowercase_	:				Optional[Any]													=	dataset[self.config.field]

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

             # If the file has one json object per line
             else:
                  with open(__UpperCamelCase     ,'rb'			) as f:
                       lowercase_	:				Tuple													=	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
                       lowercase_	:				List[Any]													=	max(self.config.chunksize // 32     ,16 << 10			)
                       lowercase_	:				List[str]													=	(
                           self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
                       )
                       while True:
                            lowercase_	:				List[str]													=	f.read(self.config.chunksize			)
                            if not batch:
                                 break
                            # Finish current line
                            try:
                                 batch += f.readline()
                            except (AttributeError, io.UnsupportedOperation):
                                 batch += readline(__UpperCamelCase			)
                            # PyArrow only accepts utf-8 encoded bytes
                            if self.config.encoding != "utf-8":
                                 lowercase_	:				Any													=	batch.decode(self.config.encoding     ,errors=__UpperCamelCase			).encode('utf-8'			)
                            try:
                                 while True:
                                      try:
                                           lowercase_	:				Optional[int]													=	paj.read_json(
                                               io.BytesIO(__UpperCamelCase			)     ,read_options=paj.ReadOptions(block_size=__UpperCamelCase			)			)
                                           break
                                      except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                           if (
                                               isinstance(__UpperCamelCase     ,pa.ArrowInvalid			)
                                               and "straddling" not in str(__UpperCamelCase			)
                                               or block_size > len(__UpperCamelCase			)
                                           ):
                                                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(__UpperCamelCase			)} 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(
                                          __UpperCamelCase     ,encoding=self.config.encoding     ,errors=self.config.encoding_errors			) as f:
                                           lowercase_	:				int													=	json.load(__UpperCamelCase			)
                                 except json.JSONDecodeError:
                                      logger.error(f'''Failed to read file \'{file}\' with error {type(__UpperCamelCase			)}: {e}'''			)
                                      raise e
                                 # If possible, parse the file as a list of json objects and exit the loop
                                 if isinstance(__UpperCamelCase     ,__UpperCamelCase			):  # list is the only sequence type supported in JSON
                                      try:
                                           lowercase_	:				List[str]													=	set().union(*[row.keys() for row in dataset]			)
                                           lowercase_	:				Tuple													=	{col: [row.get(__UpperCamelCase			) for row in dataset] for col in keys}
                                           lowercase_	:				List[Any]													=	pa.Table.from_pydict(__UpperCamelCase			)
                                      except (pa.ArrowInvalid, AttributeError) as e:
                                           logger.error(f'''Failed to read file \'{file}\' with error {type(__UpperCamelCase			)}: {e}'''			)
                                           raise ValueError(f'''Not able to read records in the JSON file at {file}.'''			) from None
                                      yield file_idx, self._cast_table(__UpperCamelCase			)
                                      break
                                 else:
                                      logger.error(f'''Failed to read file \'{file}\' with error {type(__UpperCamelCase			)}: {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(__UpperCamelCase			)
                            batch_idx += 1


"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   def __init__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      None:


        '''simple docstring'''


        warnings.warn(
            'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use BeitImageProcessor instead.'     ,__UpperCamelCase     ,)
        super().__init__(*__UpperCamelCase     ,**__UpperCamelCase			)

