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   lowercase      (       a       ):




							def __init__(  self				:   Optional[int]		,							_UpperCamelCase				:   NestedDataStructureLike[PathLike]		,							_UpperCamelCase				:   Optional[NamedSplit] = None		,							_UpperCamelCase				:   Optional[Features] = None		,							_UpperCamelCase				:   str = None		,							_UpperCamelCase				:   bool = False		,							_UpperCamelCase				:   bool = False		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[int] = None		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			Optional[Any]:



												'''simple docstring'''

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





							def 		__snake_case(  self				:   List[Any] )   ->			Any:



												'''simple docstring'''

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

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



class   lowercase      :




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

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




							def 		__snake_case(  self				:   Optional[int] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.to_json_kwargs.pop("path_or_buf"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.to_json_kwargs.pop("orient"		,							"records" )
												SCREAMING_SNAKE_CASE           =    self.to_json_kwargs.pop("lines"		,							True if orient == "records" else False )
												SCREAMING_SNAKE_CASE           =    self.to_json_kwargs.pop("index"		,							False if orient in ["split", "table"] else True )
												SCREAMING_SNAKE_CASE           =    self.to_json_kwargs.pop("compression"		,							_UpperCamelCase )

												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=_UpperCamelCase ) as buffer:
																						SCREAMING_SNAKE_CASE           =    self._write(file_obj=_UpperCamelCase		,							orient=_UpperCamelCase		,							lines=_UpperCamelCase		,							index=_UpperCamelCase		,							**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." )
																	SCREAMING_SNAKE_CASE           =    self._write(
																	    file_obj=self.path_or_buf		,							orient=_UpperCamelCase		,							lines=_UpperCamelCase		,							index=_UpperCamelCase		,							**self.to_json_kwargs )
												return written




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    args

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





							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   BinaryIO		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   List[Any]		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    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"		,							):
																						SCREAMING_SNAKE_CASE           =    self._batch_json((offset, orient, lines, index, to_json_kwargs) )
																						written += file_obj.write(_UpperCamelCase )
												else:
																	SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    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		,							_UpperCamelCase		,							_UpperCamelCase )]		,							)		,							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(_UpperCamelCase )

												return written





# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowerCamelCase   :				Union[str, Any]     			=						TypeVar('''T''')


class   lowercase      (       Generic[T]       ):




							def __init__(  self				:   int		,							_UpperCamelCase				:   bool = True )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {}  # dictionary of lists
												SCREAMING_SNAKE_CASE           =    directed




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   T		,							_UpperCamelCase				:   T )   ->			GraphAdjacencyList[T]:



												'''simple docstring'''

												if not self.directed:  # For undirected graphs
																	# if both source vertex and destination vertex are both present in the
																	# adjacency list, add destination vertex to source vertex list of adjacent
																	# vertices and add source vertex to destination vertex list of adjacent
																	# vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination vertex
																	# to source vertex list of adjacent vertices, then create a new vertex with
																	# destination vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
																	# if only destination vertex is present in adjacency list, add source vertex
																	# to destination vertex list of adjacent vertices, then create a new vertex
																	# with source vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif destination_vertex in self.adj_list:
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and assign a list
																	# containing the destination vertex as it's first adjacent vertex also
																	# create a new vertex with destination vertex as key and assign a list
																	# containing the source vertex as it's first adjacent vertex.
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
												else:  # For directed graphs
																	# if both source vertex and destination vertex are present in adjacency
																	# list, add destination vertex to source vertex list of adjacent vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination
																	# vertex to source vertex list of adjacent vertices and create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    []
																	# if only destination vertex is present in adjacency list, create a new
																	# vertex with source vertex as key and assign a list containing destination
																	# vertex as first adjacent vertex
																	elif destination_vertex in self.adj_list:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and a list containing
																	# destination vertex as it's first adjacent vertex. Then create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    []

												return self





							def __repr__(  self				:   Union[str, Any] )   ->			str:



												'''simple docstring'''

												return pformat(self.adj_list )




