

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 						__UpperCamelCase    (					lowercase						):




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						NestedDataStructureLike[PathLike]		,	lowerCAmelCase	:						Optional[NamedSplit] = None		,	lowerCAmelCase	:						Optional[Features] = None		,	lowerCAmelCase	:						str = None		,	lowerCAmelCase	:						bool = False		,	lowerCAmelCase	:						bool = False		,	lowerCAmelCase	:						Optional[str] = None		,	lowerCAmelCase	:						Optional[int] = None		,	**lowerCAmelCase	:						Dict		,	):


		'''simple docstring'''







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




	def       __A					(		self	:						Any							):


		'''simple docstring'''







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

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


class 						__UpperCamelCase    :




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Dataset		,	lowerCAmelCase	:						Union[PathLike, BinaryIO]		,	lowerCAmelCase	:						Optional[int] = None		,	lowerCAmelCase	:						Optional[int] = None		,	**lowerCAmelCase	:						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."							)

		UpperCAmelCase_       				=			dataset
		UpperCAmelCase_       				=			path_or_buf
		UpperCAmelCase_       				=			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
		UpperCAmelCase_       				=			num_proc
		UpperCAmelCase_       				=			"utf-8"
		UpperCAmelCase_       				=			to_json_kwargs




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.to_json_kwargs.pop("path_or_buf"		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			self.to_json_kwargs.pop("orient"		,	"records"							)
		UpperCAmelCase_       				=			self.to_json_kwargs.pop("lines"		,	True if orient == "records" else False							)
		UpperCAmelCase_       				=			self.to_json_kwargs.pop("index"		,	False if orient in ["split", "table"] else True							)
		UpperCAmelCase_       				=			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:
				UpperCAmelCase_       				=			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."							)
			UpperCAmelCase_       				=			self._write(
			    file_obj=self.path_or_buf		,	orient=lowerCAmelCase		,	lines=lowerCAmelCase		,	index=lowerCAmelCase		,	**self.to_json_kwargs							)
		return written




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			args

		UpperCAmelCase_       				=			query_table(
		    table=self.dataset.data		,	key=slice(lowerCAmelCase		,	offset + self.batch_size							)		,	indices=self.dataset._indices		,	)
		UpperCAmelCase_       				=			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       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						BinaryIO		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Union[str, Any]		,	**lowerCAmelCase	:						Optional[int]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			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"		,	):
				UpperCAmelCase_       				=			self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
				written += file_obj.write(lowerCAmelCase							)
		else:
			UpperCAmelCase_				,   UpperCAmelCase_       				=			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_torch_available,
)


_a:    List[str] 							=			{
    """configuration_encodec""": [
        """ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EncodecConfig""",
    ],
    """feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    List[Any] 							=			[
				    """ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """EncodecModel""",
				    """EncodecPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

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