




"""simple docstring"""
import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    Adafactor,
					    AdamW,
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_inverse_sqrt_schedule,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					)





def 						UpperCAmelCase				(			A__:    Any							,				A__:    str=10     )  ->     Union[str, Any]:
			__lowerCamelCase					:	str	       =				[]
			for _ in range(A__     ):
						lrs.append(scheduler.get_lr()[0]     )
						scheduler.step()
			return lrs





def 						UpperCAmelCase				(			A__:    List[str]							,				A__:    str=10     )  ->     Optional[int]:
			__lowerCamelCase					:	str	       =				[]
			for step in range(A__     ):
						lrs.append(scheduler.get_lr()[0]     )
						scheduler.step()
						if step == num_steps // 2:
									with tempfile.TemporaryDirectory() as tmpdirname:
												__lowerCamelCase					:	Dict	       =				os.path.join(A__							,				'schedule.bin'     )
												torch.save(scheduler.state_dict()							,				A__     )

												__lowerCamelCase					:	Union[str, Any]	       =				torch.load(A__     )
												scheduler.load_state_dict(A__     )
			return lrs

@require_torch
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					def     snake_case_				(    self						,	__a						,	__a						,	__a     ):
								self.assertEqual(len(__a     )						,	len(__a     )     )
								for a, b in zip(__a						,	__a     ):
											self.assertAlmostEqual(__a						,	__a						,	delta=__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				torch.tensor([0.1, -0.2, -0.1]						,	requires_grad=__a     )
								__lowerCamelCase					:	Any	       =				torch.tensor([0.4, 0.2, -0.5]     )
								__lowerCamelCase					:	Any	       =				nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								__lowerCamelCase					:	Optional[Any]	       =				AdamW(params=[w]						,	lr=2E-1						,	weight_decay=0.0     )
								for _ in range(100     ):
											__lowerCamelCase					:	str	       =				criterion(__a						,	__a     )
											loss.backward()
											optimizer.step()
											w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
											w.grad.zero_()
								self.assertListAlmostEqual(w.tolist()						,	[0.4, 0.2, -0.5]						,	tol=1E-2     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				torch.tensor([0.1, -0.2, -0.1]						,	requires_grad=__a     )
								__lowerCamelCase					:	int	       =				torch.tensor([0.4, 0.2, -0.5]     )
								__lowerCamelCase					:	str	       =				nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								__lowerCamelCase					:	str	       =				Adafactor(
								    params=[w]						,	lr=1E-2						,	eps=(1E-30, 1E-3)						,	clip_threshold=1.0						,	decay_rate=-0.8						,	betaa=__a						,	weight_decay=0.0						,	relative_step=__a						,	scale_parameter=__a						,	warmup_init=__a						,	)
								for _ in range(1000     ):
											__lowerCamelCase					:	int	       =				criterion(__a						,	__a     )
											loss.backward()
											optimizer.step()
											w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
											w.grad.zero_()
								self.assertListAlmostEqual(w.tolist()						,	[0.4, 0.2, -0.5]						,	tol=1E-2     )



@require_torch
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					__a						:						Dict     		= nn.Linear(50	,	50				) if is_torch_available() else None
					__a						:						List[str]     		= AdamW(m.parameters()	,	lr=10.0				) if is_torch_available() else None
					__a						:						List[Any]     		= 10
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a=None     ):
								self.assertEqual(len(__a     )						,	len(__a     )     )
								for a, b in zip(__a						,	__a     ):
											self.assertAlmostEqual(__a						,	__a						,	delta=__a						,	msg=__a     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				{'num_warmup_steps': 2, 'num_training_steps': 10}
								# schedulers doct format
								# function: (sched_args_dict, expected_learning_rates)
								__lowerCamelCase					:	str	       =				{
								    get_constant_schedule: ({}, [10.0] * self.num_steps),
								    get_constant_schedule_with_warmup: (
								        {'num_warmup_steps': 4},
								        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
								    ),
								    get_linear_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
								    ),
								    get_cosine_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
								    ),
								    get_cosine_with_hard_restarts_schedule_with_warmup: (
								        {**common_kwargs, 'num_cycles': 2},
								        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
								    ),
								    get_polynomial_decay_schedule_with_warmup: (
								        {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
								        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
								    ),
								    get_inverse_sqrt_schedule: (
								        {'num_warmup_steps': 2},
								        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
								    ),
								}

								for scheduler_func, data in scheds.items():
											__lowerCamelCase					:	int	       =				data

											__lowerCamelCase					:	List[Any]	       =				scheduler_func(self.optimizer						,	**__a     )
											self.assertEqual(len([scheduler.get_lr()[0]]     )						,	1     )
											__lowerCamelCase					:	Tuple	       =				unwrap_schedule(__a						,	self.num_steps     )
											self.assertListAlmostEqual(
											    __a						,	__a						,	tol=1E-2						,	msg=f'''failed for {scheduler_func} in normal scheduler'''						,	)

											__lowerCamelCase					:	Any	       =				scheduler_func(self.optimizer						,	**__a     )
											if scheduler_func.__name__ != "get_constant_schedule":
														LambdaScheduleWrapper.wrap_scheduler(__a     )  # wrap to test picklability of the schedule
											__lowerCamelCase					:	str	       =				unwrap_and_save_reload_schedule(__a						,	self.num_steps     )
											self.assertListEqual(__a						,	__a						,	msg=f'''failed for {scheduler_func} in save and reload'''     )



class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				fn
					def __call__(    self						,	*__a						,	**__a     ):
								return self.fn(*__a						,	**__a     )





					@classmethod
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				list(map(self						,	scheduler.lr_lambdas     )     )








"""simple docstring"""
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(      lowercase__				):




					'''simple docstring'''


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





					def     snake_case_				(    self     ):
								# Build iterable dataset
								if self.streaming:
											__lowerCamelCase					:	str	       =				self.builder.as_streaming_dataset(split=self.split     )
								# Build regular (map-style) dataset
								else:
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	str	       =				None
											__lowerCamelCase					:	int	       =				None

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



class __lowercase:




					'''simple docstring'''


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

								__lowerCamelCase					:	Optional[int]	       =				dataset
								__lowerCamelCase					:	Optional[int]	       =				path_or_buf
								__lowerCamelCase					:	List[str]	       =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								__lowerCamelCase					:	str	       =				num_proc
								__lowerCamelCase					:	Optional[Any]	       =				'utf-8'
								__lowerCamelCase					:	Optional[int]	       =				to_json_kwargs
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('path_or_buf'						,	__a     )
								__lowerCamelCase					:	Any	       =				self.to_json_kwargs.pop('orient'						,	'records'     )
								__lowerCamelCase					:	List[Any]	       =				self.to_json_kwargs.pop('lines'						,	True if orient == 'records' else False     )
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('index'						,	False if orient in ['split', 'table'] else True     )
								__lowerCamelCase					:	int	       =				self.to_json_kwargs.pop('compression'						,	__a     )

								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=__a     ) as buffer:
														__lowerCamelCase					:	int	       =				self._write(file_obj=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**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.'     )
											__lowerCamelCase					:	Dict	       =				self._write(
											    file_obj=self.path_or_buf						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								return written
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				args

								__lowerCamelCase					:	int	       =				query_table(
								    table=self.dataset.data						,	key=slice(__a						,	offset + self.batch_size     )						,	indices=self.dataset._indices						,	)
								__lowerCamelCase					:	List[str]	       =				batch.to_pandas().to_json(
								    path_or_buf=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**__a     )
								if not json_str.endswith('\n'     ):
											json_str += "\n"
								return json_str.encode(self.encoding     )





					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	**__a						,	):
								__lowerCamelCase					:	Any	       =				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'						,	):
														__lowerCamelCase					:	List[str]	       =				self._batch_json((offset, orient, lines, index, to_json_kwargs)     )
														written += file_obj.write(__a     )
								else:
											__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				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						,	__a						,	__a     )]						,	)						,	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(__a     )

								return written



