


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      :			List[str]      				=   datasets.utils.logging.get_logger(__name__)

@dataclass
class   A__ (   datasets.BuilderConfig     ):







    """simple docstring"""

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






class   A__ (   datasets.ArrowBasedBuilder     ):







    """simple docstring"""

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




    def 		a_      (	self      , __snake_case   ):
         for file_idx, file in enumerate(itertools.chain.from_iterable(__snake_case   )   ):
              # 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(__snake_case      , encoding=self.config.encoding      , errors=self.config.encoding_errors   ) as f:
                        snake_case					=    json.load(__snake_case   )

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

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

              # If the file has one json object per line
              else:
                   with open(__snake_case      , '''rb'''   ) as f:
                        snake_case					=    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
                        snake_case					=    max(self.config.chunksize // 3_2      , 1_6 << 1_0   )
                        snake_case					=    (
                            self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                        )
                        while True:
                             snake_case					=    f.read(self.config.chunksize   )
                             if not batch:
                                  break
                             # Finish current line
                             try:
                                  batch += f.readline()
                             except (AttributeError, io.UnsupportedOperation):
                                  batch += readline(__snake_case   )
                             # PyArrow only accepts utf-8 encoded bytes
                             if self.config.encoding != "utf-8":
                                  snake_case					=    batch.decode(self.config.encoding      , errors=__snake_case   ).encode('''utf-8'''   )
                             try:
                                  while True:
                                       try:
                                            snake_case					=    paj.read_json(
                                                io.BytesIO(__snake_case   )      , read_options=paj.ReadOptions(block_size=__snake_case   )   )
                                            break
                                       except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                            if (
                                                isinstance(__snake_case      , pa.ArrowInvalid   )
                                                and "straddling" not in str(__snake_case   )
                                                or block_size > len(__snake_case   )
                                            ):
                                                 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(__snake_case   )} 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(
                                           __snake_case      , encoding=self.config.encoding      , errors=self.config.encoding_errors   ) as f:
                                            snake_case					=    json.load(__snake_case   )
                                  except json.JSONDecodeError:
                                       logger.error(F'''Failed to read file \'{file}\' with error {type(__snake_case   )}: {e}'''   )
                                       raise e
                                  # If possible, parse the file as a list of json objects and exit the loop
                                  if isinstance(__snake_case      , __snake_case   ):  # list is the only sequence type supported in JSON
                                       try:
                                            snake_case					=    set().union(*[row.keys() for row in dataset]   )
                                            snake_case					=    {col: [row.get(__snake_case   ) for row in dataset] for col in keys}
                                            snake_case					=    pa.Table.from_pydict(__snake_case   )
                                       except (pa.ArrowInvalid, AttributeError) as e:
                                            logger.error(F'''Failed to read file \'{file}\' with error {type(__snake_case   )}: {e}'''   )
                                            raise ValueError(F'''Not able to read records in the JSON file at {file}.'''   ) from None
                                       yield file_idx, self._cast_table(__snake_case   )
                                       break
                                  else:
                                       logger.error(F'''Failed to read file \'{file}\' with error {type(__snake_case   )}: {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(__snake_case   )
                             batch_idx += 1








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE      :			int      				=   {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE      :			Dict      				=   [
     "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TimesformerModel",
     "TimesformerForVideoClassification",
     "TimesformerPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_timesformer import (
      TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TimesformerForVideoClassification,
      TimesformerModel,
      TimesformerPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE      :			int      				=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




