



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 SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        super().__init__(
            lowerCamelCase__,		split=lowerCamelCase__,		features=lowerCamelCase__,		cache_dir=lowerCamelCase__,		keep_in_memory=lowerCamelCase__,		streaming=lowerCamelCase__,		num_proc=lowerCamelCase__,		**lowerCamelCase__,		)
        A			:	Union[str, Any]										=       field
        A			:	str										=       path_or_paths if isinstance(lowerCamelCase__,		lowerCamelCase__		) else {self.split: path_or_paths}
        A			:	str										=       Json(
            cache_dir=lowerCamelCase__,		data_files=lowerCamelCase__,		features=lowerCamelCase__,		field=lowerCamelCase__,		**lowerCamelCase__,		)






    def       _lowerCAmelCase (	self		):
        # Build iterable dataset
        if self.streaming:
            A			:	Tuple										=       self.builder.as_streaming_dataset(split=self.split		)
        # Build regular (map-style) dataset
        else:
            A			:	Tuple										=       None
            A			:	Any										=       None
            A			:	int										=       None
            A			:	List[str]										=       None

            self.builder.download_and_prepare(
                download_config=lowerCamelCase__,		download_mode=lowerCamelCase__,		verification_mode=lowerCamelCase__,		base_path=lowerCamelCase__,		num_proc=self.num_proc,		)
            A			:	List[Any]										=       self.builder.as_dataset(
                split=self.split,		verification_mode=lowerCamelCase__,		in_memory=self.keep_in_memory		)
        return dataset






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        if num_proc is not None and num_proc <= 0:
            raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''		)

        A			:	Optional[int]										=       dataset
        A			:	int										=       path_or_buf
        A			:	str										=       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        A			:	Tuple										=       num_proc
        A			:	Tuple										=       """utf-8"""
        A			:	List[str]										=       to_json_kwargs






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       self.to_json_kwargs.pop("""path_or_buf""",		lowerCamelCase__		)
        A			:	Optional[Any]										=       self.to_json_kwargs.pop("""orient""",		"""records"""		)
        A			:	Optional[Any]										=       self.to_json_kwargs.pop("""lines""",		True if orient == """records""" else False		)
        A			:	Any										=       self.to_json_kwargs.pop("""index""",		False if orient in ["""split""", """table"""] else True		)
        A			:	Optional[int]										=       self.to_json_kwargs.pop("""compression""",		lowerCamelCase__		)

        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=lowerCamelCase__		) as buffer:
                A			:	Any										=       self._write(file_obj=lowerCamelCase__,		orient=lowerCamelCase__,		lines=lowerCamelCase__,		index=lowerCamelCase__,		**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			:	Union[str, Any]										=       self._write(
                file_obj=self.path_or_buf,		orient=lowerCamelCase__,		lines=lowerCamelCase__,		index=lowerCamelCase__,		**self.to_json_kwargs		)
        return written






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A					,							A					,							A					,							A					,							A			:	Any										=       args

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






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__,		):
        A			:	Optional[int]										=       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			:	List[Any]										=       self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
                written += file_obj.write(lowerCamelCase__		)
        else:
            A					,							A			:	List[Any]										=       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,		lowerCamelCase__,		lowerCamelCase__		)],		),		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(lowerCamelCase__		)

        return written







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[int]					=						[
         """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TapasForMaskedLM""",
         """TapasForQuestionAnswering""",
         """TapasForSequenceClassification""",
         """TapasModel""",
         """TapasPreTrainedModel""",
         """load_tf_weights_in_tapas""",
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						[
         """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFTapasForMaskedLM""",
         """TFTapasForQuestionAnswering""",
         """TFTapasForSequenceClassification""",
         """TFTapasModel""",
         """TFTapasPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:str					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


