

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(				self,    lowercase_,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = False,    lowercase_ = False,    lowercase_ = None,    lowercase_ = None,    **lowercase_,    )							->    int:






     """simple docstring"""






     super().__init__(
         lowercase_,    split=lowercase_,    features=lowercase_,    cache_dir=lowercase_,    keep_in_memory=lowercase_,    streaming=lowercase_,    num_proc=lowercase_,    **lowercase_,    )
     a__  =field
     a__  =path_or_paths if isinstance(lowercase_,    lowercase_       ) else {self.split: path_or_paths}
     a__  =Json(
         cache_dir=lowercase_,    data_files=lowercase_,    features=lowercase_,    field=lowercase_,    **lowercase_,    )



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     if self.streaming:
       a__  =self.builder.as_streaming_dataset(split=self.split       )
     # Build regular (map-style) dataset
     else:
       a__  =None
       a__  =None
       a__  =None
       a__  =None

       self.builder.download_and_prepare(
           download_config=lowercase_,    download_mode=lowercase_,    verification_mode=lowercase_,    base_path=lowercase_,    num_proc=self.num_proc,    )
       a__  =self.builder.as_dataset(
           split=self.split,    verification_mode=lowercase_,    in_memory=self.keep_in_memory       )
     return dataset







class 						__magic_name__			:

   '''simple docstring'''




   def __init__(				self,    lowercase_,    lowercase_,    lowercase_ = None,    lowercase_ = None,    **lowercase_,    )							->    Any:






     """simple docstring"""






     if num_proc is not None and num_proc <= 0:
       raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""       )

     a__  =dataset
     a__  =path_or_buf
     a__  =batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     a__  =num_proc
     a__  ='''utf-8'''
     a__  =to_json_kwargs



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__  =self.to_json_kwargs.pop('''path_or_buf''',    lowercase_       )
     a__  =self.to_json_kwargs.pop('''orient''',    '''records'''       )
     a__  =self.to_json_kwargs.pop('''lines''',    True if orient == '''records''' else False       )
     a__  =self.to_json_kwargs.pop('''index''',    False if orient in ['''split''', '''table'''] else True       )
     a__  =self.to_json_kwargs.pop('''compression''',    lowercase_       )

     if compression not in [None, "infer", "gzip", "bz2", "xz"]:
       raise NotImplementedError(F"""`datasets` currently does not support {compression} compression"""       )

     if isinstance(self.path_or_buf,    (str, bytes, os.PathLike)       ):
       with fsspec.open(self.path_or_buf,    '''wb''',    compression=lowercase_       ) as buffer:
         a__  =self._write(file_obj=lowercase_,    orient=lowercase_,    lines=lowercase_,    index=lowercase_,    **self.to_json_kwargs       )
     else:
       if compression:
         raise NotImplementedError(
             F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
             ''' was passed. Please provide a local path instead.'''       )
       a__  =self._write(
           file_obj=self.path_or_buf,    orient=lowercase_,    lines=lowercase_,    index=lowercase_,    **self.to_json_kwargs       )
     return written



   def  _UpperCAmelCase (				self,    lowercase_       )							->    str:






     """simple docstring"""






     a__,      a__,      a__,      a__,      a__  =args

     a__  =query_table(
         table=self.dataset.data,    key=slice(lowercase_,    offset + self.batch_size       ),    indices=self.dataset._indices,    )
     a__  =batch.to_pandas().to_json(
         path_or_buf=lowercase_,    orient=lowercase_,    lines=lowercase_,    index=lowercase_,    **lowercase_       )
     if not json_str.endswith('''\n'''       ):
       json_str += "\n"
     return json_str.encode(self.encoding       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    **lowercase_,    )							->    int:






     """simple docstring"""






     a__  =0

     if self.num_proc is None or self.num_proc == 1:
       for offset in logging.tqdm(
           range(0,    len(self.dataset       ),    self.batch_size       ),    unit='''ba''',    disable=not logging.is_progress_bar_enabled(),    desc='''Creating json from Arrow format''',    ):
         a__  =self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
         written += file_obj.write(lowercase_       )
     else:
       a__,      a__  =len(self.dataset       ), self.batch_size
       with multiprocessing.Pool(self.num_proc       ) as pool:
         for json_str in logging.tqdm(
             pool.imap(
                 self._batch_json,    [(offset, orient, lines, index, to_json_kwargs) for offset in range(0,    lowercase_,    lowercase_       )],    ),    total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,    unit='''ba''',    disable=not logging.is_progress_bar_enabled(),    desc='''Creating json from Arrow format''',    ):
           written += file_obj.write(lowercase_       )

     return written



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase           =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''DebertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaForMaskedLM''',
           '''DebertaForQuestionAnswering''',
           '''DebertaForSequenceClassification''',
           '''DebertaForTokenClassification''',
           '''DebertaModel''',
           '''DebertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaForMaskedLM''',
           '''TFDebertaForQuestionAnswering''',
           '''TFDebertaForSequenceClassification''',
           '''TFDebertaForTokenClassification''',
           '''TFDebertaModel''',
           '''TFDebertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

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