


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


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







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







class       lowerCamelCase__   (       datasets.ArrowBasedBuilder):
     SCREAMING_SNAKE_CASE__      			=			JsonConfig





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





     def      __A					(self		,     UpperCAmelCase       )     ->   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       ):
        _lowercase							      =self.config.features.arrow_schema.field(UpperCAmelCase       ).type
        _lowercase							      =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							      =table_cast(UpperCAmelCase		,     self.config.features.arrow_schema       )
      return pa_table





     def      __A					(self		,     UpperCAmelCase       )     ->   int:
      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							      =json.load(UpperCAmelCase       )

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

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''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

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

