
'''simple docstring'''







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


_lowercase :  int    							=       datasets.utils.logging.get_logger(__name__)






@dataclass
class  lowerCAmelCase__   (  datasets.BuilderConfig     ):
					lowerCAmelCase_        =     None
					lowerCAmelCase_        =     "utf-8"
					lowerCAmelCase_        =     None
					lowerCAmelCase_        =     None
					lowerCAmelCase_        =     True  # deprecated
					lowerCAmelCase_        =     None  # deprecated
					lowerCAmelCase_        =     1_0 << 2_0  # 10MB
					lowerCAmelCase_        =     None







class  lowerCAmelCase__   (  datasets.ArrowBasedBuilder     ):
					lowerCAmelCase_        =     JsonConfig






					def 	_snake_case    (   self		):


											"""simple docstring"""





											if self.config.block_size is not None:
																	logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''		)
																	lowercase_	:     List[Any]													=       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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											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_	:     str													=       dl_manager.download_and_extract(self.config.data_files		)
											if isinstance(__SCREAMING_SNAKE_CASE				,							(str, list, tuple)		):
																	lowercase_	:     Union[str, Any]													=       data_files
																	if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																							lowercase_	:     Any													=       [files]
																	lowercase_	:     Union[str, Any]													=       [dl_manager.iter_files(__SCREAMING_SNAKE_CASE		) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,							gen_kwargs={'''files''': files}		)]
											lowercase_	:     List[Any]													=       []
											for split_name, files in data_files.items():
																	if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																							lowercase_	:     Dict													=       [files]
																	lowercase_	:     Tuple													=       [dl_manager.iter_files(__SCREAMING_SNAKE_CASE		) for file in files]
																	splits.append(datasets.SplitGenerator(name=__SCREAMING_SNAKE_CASE				,							gen_kwargs={'''files''': files}		)		)
											return splits






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if self.config.features is not None:
																	# adding missing columns
																	for column_name in set(self.config.features		) - set(pa_table.column_names		):
																							lowercase_	:     Tuple													=       self.config.features.arrow_schema.field(__SCREAMING_SNAKE_CASE		).type
																							lowercase_	:     Optional[Any]													=       pa_table.append_column(__SCREAMING_SNAKE_CASE				,							pa.array([None] * len(__SCREAMING_SNAKE_CASE		)				,							type=__SCREAMING_SNAKE_CASE		)		)
																	# more expensive cast to support nested structures with keys in a different order
																	# allows str <-> int/float or str to Audio for example
																	lowercase_	:     Optional[Any]													=       table_cast(__SCREAMING_SNAKE_CASE				,							self.config.features.arrow_schema		)
											return pa_table






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											for file_idx, file in enumerate(itertools.chain.from_iterable(__SCREAMING_SNAKE_CASE		)		):
																	# 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(__SCREAMING_SNAKE_CASE				,							encoding=self.config.encoding				,							errors=self.config.encoding_errors		) as f:
																													lowercase_	:     Any													=       json.load(__SCREAMING_SNAKE_CASE		)

																							# We keep only the field we are interested in
																							lowercase_	:     Optional[int]													=       dataset[self.config.field]

																							# We accept two format: a list of dicts or a dict of lists
																							if isinstance(__SCREAMING_SNAKE_CASE				,							(list, tuple)		):
																													lowercase_	:     Tuple													=       set().union(*[row.keys() for row in dataset]		)
																													lowercase_	:     Optional[int]													=       {col: [row.get(__SCREAMING_SNAKE_CASE		) for row in dataset] for col in keys}
																							else:
																													lowercase_	:     Dict													=       dataset
																							lowercase_	:     Dict													=       pa.Table.from_pydict(__SCREAMING_SNAKE_CASE		)
																							yield file_idx, self._cast_table(__SCREAMING_SNAKE_CASE		)

																	# If the file has one json object per line
																	else:
																							with open(__SCREAMING_SNAKE_CASE				,							'''rb'''		) as f:
																													lowercase_	:     Union[str, Any]													=       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_	:     Tuple													=       max(self.config.chunksize // 32				,							16 << 10		)
																													lowercase_	:     Optional[Any]													=       (
																													    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																													)
																													while True:
																																			lowercase_	:     List[str]													=       f.read(self.config.chunksize		)
																																			if not batch:
																																									break
																																			# Finish current line
																																			try:
																																									batch += f.readline()
																																			except (AttributeError, io.UnsupportedOperation):
																																									batch += readline(__SCREAMING_SNAKE_CASE		)
																																			# PyArrow only accepts utf-8 encoded bytes
																																			if self.config.encoding != "utf-8":
																																									lowercase_	:     int													=       batch.decode(self.config.encoding				,							errors=__SCREAMING_SNAKE_CASE		).encode('''utf-8'''		)
																																			try:
																																									while True:
																																															try:
																																																					lowercase_	:     Union[str, Any]													=       paj.read_json(
																																																					    io.BytesIO(__SCREAMING_SNAKE_CASE		)				,							read_options=paj.ReadOptions(block_size=__SCREAMING_SNAKE_CASE		)		)
																																																					break
																																															except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																																					if (
																																																					    isinstance(__SCREAMING_SNAKE_CASE				,							pa.ArrowInvalid		)
																																																					    and "straddling" not in str(__SCREAMING_SNAKE_CASE		)
																																																					    or block_size > len(__SCREAMING_SNAKE_CASE		)
																																																					):
																																																											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(__SCREAMING_SNAKE_CASE		)} 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(
																																															    __SCREAMING_SNAKE_CASE				,							encoding=self.config.encoding				,							errors=self.config.encoding_errors		) as f:
																																																					lowercase_	:     Tuple													=       json.load(__SCREAMING_SNAKE_CASE		)
																																									except json.JSONDecodeError:
																																															logger.error(F'''Failed to read file \'{file}\' with error {type(__SCREAMING_SNAKE_CASE		)}: {e}'''		)
																																															raise e
																																									# If possible, parse the file as a list of json objects and exit the loop
																																									if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):  # list is the only sequence type supported in JSON
																																															try:
																																																					lowercase_	:     List[Any]													=       set().union(*[row.keys() for row in dataset]		)
																																																					lowercase_	:     Dict													=       {col: [row.get(__SCREAMING_SNAKE_CASE		) for row in dataset] for col in keys}
																																																					lowercase_	:     str													=       pa.Table.from_pydict(__SCREAMING_SNAKE_CASE		)
																																															except (pa.ArrowInvalid, AttributeError) as e:
																																																					logger.error(F'''Failed to read file \'{file}\' with error {type(__SCREAMING_SNAKE_CASE		)}: {e}'''		)
																																																					raise ValueError(F'''Not able to read records in the JSON file at {file}.'''		) from None
																																															yield file_idx, self._cast_table(__SCREAMING_SNAKE_CASE		)
																																															break
																																									else:
																																															logger.error(F'''Failed to read file \'{file}\' with error {type(__SCREAMING_SNAKE_CASE		)}: {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(__SCREAMING_SNAKE_CASE		)
																																			batch_idx += 1





'''simple docstring'''







from __future__ import annotations

from collections import Counter
from random import random






class  lowerCAmelCase__   :






					def __init__(   self		):


											"""simple docstring"""





											lowercase_	:     int													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       probability






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return list(self.connections		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       0
											lowercase_	:     Tuple													=       random()

											for dest in self.connections[node]:
																	current_probability += self.connections[node][dest]
																	if current_probability > random_value:
																							return dest
											return ""







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: list[tuple[str, str, float]]      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       MarkovChainGraphUndirectedUnweighted()

						for nodea, nodea, probability in transitions:
												graph.add_transition_probability(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						lowercase_	:     str													=       Counter(graph.get_nodes()							)
						lowercase_	:     Any													=       start

						for _ in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     int													=       graph.transition(__SCREAMING_SNAKE_CASE							)
												visited[node] += 1

						return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()



