'''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			:	Union[str, Any]	=    datasets.utils.logging.get_logger(__name__)

@dataclass
class       lowerCamelCase_      (datasets.BuilderConfig       ):
      '''simple docstring'''

      __UpperCamelCase:    Union[str, Any]         =		None
      __UpperCamelCase:    List[Any]         =		"utf-8"
      __UpperCamelCase:    Dict         =		None
      __UpperCamelCase:    Dict         =		None
      __UpperCamelCase:    Dict         =		True  # deprecated
      __UpperCamelCase:    int         =		None  # deprecated
      __UpperCamelCase:    List[str]         =		1_0 << 2_0  # 10MB
      __UpperCamelCase:    Any         =		None

class       lowerCamelCase_      (datasets.ArrowBasedBuilder       ):
      '''simple docstring'''

      __UpperCamelCase:    List[Any]         =		JsonConfig




      def 	_A      (			self		:							Tuple			):



             if self.config.block_size is not None:
                    logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"			)
                    _UpperCAmelCase	:    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 	_A      (			self		:							Optional[Any]		,   A		:							str			):



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




      def 	_A      (			self		:							Dict		,   A		:							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			):
                           _UpperCAmelCase	:    Tuple				 =       self.config.features.arrow_schema.field(lowerCamelCase_			).type
                           _UpperCAmelCase	:    List[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 	_A      (			self		:							Optional[int]		,   A		:							Any			):



             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	:    Optional[int]				 =       json.load(lowerCamelCase_			)

                           # We keep only the field we are interested in
                           _UpperCAmelCase	:    List[Any]				 =       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	:    int				 =       {col: [row.get(lowerCamelCase_			) for row in dataset] for col in keys}
                           else:
                                  _UpperCAmelCase	:    int				 =       dataset
                           _UpperCAmelCase	:    str				 =       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	:    Dict				 =       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	:    str				 =       max(self.config.chunksize // 32		,   16 << 10			)
                                  _UpperCAmelCase	:    Union[str, Any]				 =       (
                                      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	:    Any				 =       batch.decode(self.config.encoding		,   errors=lowerCamelCase_			).encode("utf-8"			)
                                         try:
                                                while True:
                                                       try:
                                                              _UpperCAmelCase	:    Dict				 =       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	:    Optional[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	:    List[str]				 =       set().union(*[row.keys() for row in dataset]			)
                                                              _UpperCAmelCase	:    Optional[int]				 =       {col: [row.get(lowerCamelCase_			) for row in dataset] for col in keys}
                                                              _UpperCAmelCase	:    Union[str, 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








'''simple docstring'''




import unittest

from transformers import DonutProcessor


__UpperCAmelCase            =       """naver-clova-ix/donut-base"""




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			DonutProcessor.from_pretrained(lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			{
							    """name""": """John Doe""",
							    """age""": """99""",
							    """city""": """Atlanta""",
							    """state""": """GA""",
							    """zip""": """30301""",
							    """phone""": """123-4567""",
							    """nicknames""": [{"""nickname""": """Johnny"""}, {"""nickname""": """JD"""}],
							}

							SCREAMING_SNAKE_CASE :				List[Any]		=			(
							    """<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"""
							    """<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"""
							    """<s_nicknames><s_nickname>Johnny</s_nickname>"""
							    """<sep/><s_nickname>JD</s_nickname></s_nicknames>"""
							)
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.processor.tokenajson(lowerCamelCase_						)

							self.assertDictEqual(lowerCamelCase_      ,						lowerCamelCase_						)



