



import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCAmelCase_							:      Optional[Any]      =  logging.get_logger(__name__)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[str]				=	"""linear"""
	lowerCAmelCase	:   int				=	"""cosine"""
	lowerCAmelCase	:   Dict				=	"""cosine_with_restarts"""
	lowerCAmelCase	:   Optional[Any]				=	"""polynomial"""
	lowerCAmelCase	:   Dict				=	"""constant"""
	lowerCAmelCase	:   Any				=	"""constant_with_warmup"""
	lowerCAmelCase	:   Union[str, Any]				=	"""piecewise_constant"""





def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int = -1    )->       Dict:





						"""simple docstring"""





						return LambdaLR(_A			,							lambda _A    : 1			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int = -1    )->       Optional[Any]:





						"""simple docstring"""





						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1.0			,							_A    )    )
												return 1.0

						return LambdaLR(_A			,							_A			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				str			,							_A			:				int = -1    )->       Dict:





						"""simple docstring"""





						A__							=       {}
						A__							=       step_rules.split(","    )
						for rule_str in rule_list[:-1]:
												A__						,       A__							=       rule_str.split(":"    )
												A__							=       int(_A    )
												A__							=       float(_A    )
												A__							=       value
						A__							=       float(rule_list[-1]    )

						def create_rules_function(_A			:				Any			,							_A			:				Optional[int]    ):
												def rule_func(_A			:				int    ) -> float:
																		A__							=       sorted(rules_dict.keys()    )
																		for i, sorted_step in enumerate(_A    ):
																								if steps < sorted_step:
																														return rules_dict[sorted_steps[i]]
																		return last_lr_multiple

												return rule_func

						A__							=       create_rules_function(_A			,							_A    )

						return LambdaLR(_A			,							_A			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Any			,							_A			:				Union[str, Any]			,							_A			:				str			,							_A			:				str=-1    )->       Tuple:





						"""simple docstring"""





						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												return max(
												    0.0			,							float(num_training_steps - current_step    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int			,							_A			:				float = 0.5			,							_A			:				int = -1    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       float(current_step - num_warmup_steps    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )
												return max(0.0			,							0.5 * (1.0 + math.cos(math.pi * float(_A    ) * 2.0 * progress    ))    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int			,							_A			:				int = 1			,							_A			:				int = -1    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       float(current_step - num_warmup_steps    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )
												if progress >= 1.0:
																		return 0.0
												return max(0.0			,							0.5 * (1.0 + math.cos(math.pi * ((float(_A    ) * progress) % 1.0)    ))    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				Union[str, Any]			,							_A			:				List[str]			,							_A			:				Tuple=1E-7			,							_A			:				Dict=1.0			,							_A			:				Union[str, Any]=-1    )->       Any:





						"""simple docstring"""





						A__							=       optimizer.defaults["lr"]
						if not (lr_init > lr_end):
												raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""    )

						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												elif current_step > num_training_steps:
																		return lr_end / lr_init  # as LambdaLR multiplies by lr_init
												else:
																		A__							=       lr_init - lr_end
																		A__							=       num_training_steps - num_warmup_steps
																		A__							=       1 - (current_step - num_warmup_steps) / decay_steps
																		A__							=       lr_range * pct_remaining**power + lr_end
																		return decay / lr_init  # as LambdaLR multiplies by lr_init

						return LambdaLR(_A			,							_A			,							_A    )


UpperCAmelCase_							:      Any      =  {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def  UpperCamelCase						(       _A			:				Union[str, SchedulerType]			,							_A			:				Optimizer			,							_A			:				Optional[str] = None			,							_A			:				Optional[int] = None			,							_A			:				Optional[int] = None			,							_A			:				int = 1			,							_A			:				float = 1.0			,							_A			:				int = -1			,							)->       Union[str, Any]:





						"""simple docstring"""





						A__							=       SchedulerType(_A    )
						A__							=       TYPE_TO_SCHEDULER_FUNCTION[name]
						if name == SchedulerType.CONSTANT:
												return schedule_func(_A			,							last_epoch=_A    )

						if name == SchedulerType.PIECEWISE_CONSTANT:
												return schedule_func(_A			,							step_rules=_A			,							last_epoch=_A    )

						# All other schedulers require `num_warmup_steps`
						if num_warmup_steps is None:
												raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""    )

						if name == SchedulerType.CONSTANT_WITH_WARMUP:
												return schedule_func(_A			,							num_warmup_steps=_A			,							last_epoch=_A    )

						# All other schedulers require `num_training_steps`
						if num_training_steps is None:
												raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""    )

						if name == SchedulerType.COSINE_WITH_RESTARTS:
												return schedule_func(
												    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							num_cycles=_A			,							last_epoch=_A			,							)

						if name == SchedulerType.POLYNOMIAL:
												return schedule_func(
												    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							power=_A			,							last_epoch=_A			,							)

						return schedule_func(
						    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							last_epoch=_A    )










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    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							super().__init__(
							    UpperCAmelCase__			,	split=UpperCAmelCase__			,	features=UpperCAmelCase__			,	cache_dir=UpperCAmelCase__			,	keep_in_memory=UpperCAmelCase__			,	streaming=UpperCAmelCase__			,	num_proc=UpperCAmelCase__			,	**UpperCAmelCase__			,	)
							A__							=       field
							A__							=       path_or_paths if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else {self.split: path_or_paths}
							A__							=       Json(
							    cache_dir=UpperCAmelCase__			,	data_files=UpperCAmelCase__			,	features=UpperCAmelCase__			,	field=UpperCAmelCase__			,	**UpperCAmelCase__			,	)



	def 			__A							(       self		):
							# Build iterable dataset
							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=UpperCAmelCase__			,	download_mode=UpperCAmelCase__			,	verification_mode=UpperCAmelCase__			,	base_path=UpperCAmelCase__			,	num_proc=self.num_proc			,	)
													A__							=       self.builder.as_dataset(
													    split=self.split			,	verification_mode=UpperCAmelCase__			,	in_memory=self.keep_in_memory		)
							return dataset






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							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 			__A							(       self		):
							A__							=       self.to_json_kwargs.pop("path_or_buf"			,	UpperCAmelCase__		)
							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"			,	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:
																			A__							=       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."		)
													A__							=       self._write(
													    file_obj=self.path_or_buf			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**self.to_json_kwargs		)
							return written



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__						,       A__						,       A__						,       A__						,       A__							=       args

							A__							=       query_table(
							    table=self.dataset.data			,	key=slice(UpperCAmelCase__			,	offset + self.batch_size		)			,	indices=self.dataset._indices			,	)
							A__							=       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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	**UpperCAmelCase__			,	):
							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(UpperCAmelCase__		)
							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			,	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





