





'''simple docstring'''



import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			def       a	(					self			,     A_			,     A_=False   ):
								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   (
								    filename_skip_take["filename"],
								    filename_skip_take["skip"] if "skip" in filename_skip_take else None,
								    filename_skip_take["take"] if "take" in filename_skip_take else None,
								)
								open(os.path.join(A_   )			,     "wb"   ).close()
								_UpperCamelCase											=   pa.Table.from_pydict({"filename": [Path(A_   ).name] * 1_00}   )
								if take == -1:
													_UpperCamelCase											=   len(A_   ) - skip
								if skip is not None and take is not None:
													_UpperCamelCase											=   pa_table.slice(A_			,     A_   )
								return pa_table




class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			def       a	(					self   ):
								_UpperCamelCase											=   "my_name"
								_UpperCamelCase											=   SplitInfo(name="train"			,     num_examples=1_00   )
								_UpperCamelCase											=   SplitInfo(name="test"			,     num_examples=1_00   )
								_UpperCamelCase											=   [train_info, test_info]
								_UpperCamelCase											=   SplitDict()
								split_dict.add(A_   )
								split_dict.add(A_   )
								_UpperCamelCase											=   DatasetInfo(splits=A_   )

								with tempfile.TemporaryDirectory() as tmp_dir:
													_UpperCamelCase											=   ReaderTest(A_			,     A_   )

													_UpperCamelCase											=   "test[:33%]"
													_UpperCamelCase											=   Dataset(**reader.read(A_			,     A_			,     A_   )   )
													self.assertEqual(dset["filename"][0]			,     F"{name}-test"   )
													self.assertEqual(dset.num_rows			,     33   )
													self.assertEqual(dset.num_columns			,     1   )

													_UpperCamelCase											=   ["train", "test[:33%]"]
													_UpperCamelCase											=   [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"   )]
													for instructions in [instructionsa, instructionsa]:
																		_UpperCamelCase											=   [reader.read(A_			,     A_			,     A_   ) for instr in instructions]
																		_UpperCamelCase				,	_UpperCamelCase											=   (Dataset(**A_   ) for dataset_kwargs in datasets_kwargs)
																		self.assertEqual(train_dset["filename"][0]			,     F"{name}-train"   )
																		self.assertEqual(train_dset.num_rows			,     1_00   )
																		self.assertEqual(train_dset.num_columns			,     1   )
																		self.assertIsInstance(train_dset.split			,     A_   )
																		self.assertEqual(str(train_dset.split   )			,     "train"   )
																		self.assertEqual(test_dset["filename"][0]			,     F"{name}-test"   )
																		self.assertEqual(test_dset.num_rows			,     33   )
																		self.assertEqual(test_dset.num_columns			,     1   )
																		self.assertIsInstance(test_dset.split			,     A_   )
																		self.assertEqual(str(test_dset.split   )			,     "test[:33%]"   )
																		del train_dset, test_dset




			def       a	(					self   ):
								_UpperCamelCase											=   "my_name"
								_UpperCamelCase											=   SplitInfo(name="train"			,     num_examples=10_00			,     shard_lengths=[1_00] * 10   )
								_UpperCamelCase											=   [train_info]
								_UpperCamelCase											=   SplitDict()
								split_dict.add(A_   )
								_UpperCamelCase											=   DatasetInfo(splits=A_   )

								with tempfile.TemporaryDirectory() as tmp_dir:
													_UpperCamelCase											=   ReaderTest(A_			,     A_   )

													_UpperCamelCase											=   "train[:33%]"
													_UpperCamelCase											=   Dataset(**reader.read(A_			,     A_			,     A_   )   )
													self.assertEqual(dset["filename"][0]			,     F"{name}-train-00000-of-00010"   )
													self.assertEqual(dset["filename"][-1]			,     F"{name}-train-00003-of-00010"   )
													self.assertEqual(dset.num_rows			,     3_30   )
													self.assertEqual(dset.num_columns			,     1   )




			def       a	(					self   ):
								_UpperCamelCase											=   SplitInfo(name="train"			,     num_examples=1_00   )
								_UpperCamelCase											=   SplitInfo(name="test"			,     num_examples=1_00   )
								_UpperCamelCase											=   SplitDict()
								split_dict.add(A_   )
								split_dict.add(A_   )
								_UpperCamelCase											=   DatasetInfo(splits=A_   )

								with tempfile.TemporaryDirectory() as tmp_dir:
													_UpperCamelCase											=   ReaderTest(A_			,     A_   )

													_UpperCamelCase											=   [
													    {"filename": os.path.join(A_			,     "train"   )},
													    {"filename": os.path.join(A_			,     "test"   ), "skip": 10, "take": 10},
													]
													_UpperCamelCase											=   Dataset(**reader.read_files(A_			,     original_instructions="train+test[10:20]"   )   )
													self.assertEqual(dset.num_rows			,     1_10   )
													self.assertEqual(dset.num_columns			,     1   )
													del dset




@pytest.mark.parametrize("in_memory" ,   [False, True]				)
def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : Tuple				)->   List[Any]:






					"""simple docstring"""

					_UpperCamelCase											=   arrow_file
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										_UpperCamelCase											=   ArrowReader.read_table(_UpperCamelCase ,   in_memory=_UpperCamelCase				)
					assert table.shape == dataset.data.shape
					assert set(table.column_names				) == set(dataset.data.column_names				)
					assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



@pytest.mark.parametrize("in_memory" ,   [False, True]				)
def  lowercase__(		_UpperCamelCase  : Any ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : str				)->   Tuple:






					"""simple docstring"""

					_UpperCamelCase											=   arrow_file
					_UpperCamelCase											=   ArrowReader("" ,   _UpperCamelCase				)
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										_UpperCamelCase											=   reader.read_files([{"filename": filename}] ,   in_memory=_UpperCamelCase				)
					assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
					_UpperCamelCase											=   dataset_kwargs["arrow_table"]
					assert table.shape == dataset.data.shape
					assert set(table.column_names				) == set(dataset.data.column_names				)
					assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



def  lowercase__(		)->   Any:






					"""simple docstring"""

					assert ReadInstruction("train" ,   to=10 ,   unit="abs"				).to_spec() == "train[:10]"
					assert ReadInstruction("train" ,   from_=-80 ,   to=10 ,   unit="%"				).to_spec() == "train[-80%:10%]"

					_UpperCamelCase											=   "train+test"
					assert ReadInstruction.from_spec(_UpperCamelCase				).to_spec() == spec_train_test

					_UpperCamelCase											=   "train[2:10]"
					assert ReadInstruction.from_spec(_UpperCamelCase				).to_spec() == spec_train_abs

					_UpperCamelCase											=   "train[15%:-20%]"
					assert ReadInstruction.from_spec(_UpperCamelCase				).to_spec() == spec_train_pct

					_UpperCamelCase											=   "train[:10%](closest)"
					assert ReadInstruction.from_spec(_UpperCamelCase				).to_spec() == "train[:10%]"

					_UpperCamelCase											=   "train[:10%](pct1_dropremainder)"
					assert ReadInstruction.from_spec(_UpperCamelCase				).to_spec() == spec_train_pct_rounding

					_UpperCamelCase											=   "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
					assert ReadInstruction.from_spec(_UpperCamelCase				).to_spec() == spec_train_test_pct_rounding



def  lowercase__(		)->   Optional[int]:






					"""simple docstring"""

					_UpperCamelCase											=   "dummy"
					_UpperCamelCase											=   [SplitInfo(name="train" ,   num_examples=100				)]
					_UpperCamelCase											=   "train[:33%]"
					_UpperCamelCase											=   "arrow"
					_UpperCamelCase											=   "prefix"

					_UpperCamelCase											=   make_file_instructions(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)
					assert isinstance(_UpperCamelCase ,   _UpperCamelCase				)
					assert file_instructions.num_examples == 33
					assert file_instructions.file_instructions == [
					    {"filename": os.path.join(_UpperCamelCase ,   f"{name}-train.arrow"				), "skip": 0, "take": 33}
					]

					_UpperCamelCase											=   [SplitInfo(name="train" ,   num_examples=100 ,   shard_lengths=[10] * 10				)]
					_UpperCamelCase											=   make_file_instructions(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)
					assert isinstance(_UpperCamelCase ,   _UpperCamelCase				)
					assert file_instructions.num_examples == 33
					assert file_instructions.file_instructions == [
					    {"filename": os.path.join(_UpperCamelCase ,   f"{name}-train-00000-of-00010.arrow"				), "skip": 0, "take": -1},
					    {"filename": os.path.join(_UpperCamelCase ,   f"{name}-train-00001-of-00010.arrow"				), "skip": 0, "take": -1},
					    {"filename": os.path.join(_UpperCamelCase ,   f"{name}-train-00002-of-00010.arrow"				), "skip": 0, "take": -1},
					    {"filename": os.path.join(_UpperCamelCase ,   f"{name}-train-00003-of-00010.arrow"				), "skip": 0, "take": 3},
					]



@pytest.mark.parametrize("name, expected_exception" ,   [(None, TypeError), ("", ValueError)]				)
def  lowercase__(		_UpperCamelCase  : Optional[int] ,   _UpperCamelCase  : List[Any]				)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   [SplitInfo(name="train" ,   num_examples=100				)]
					_UpperCamelCase											=   "train"
					_UpperCamelCase											=   "arrow"
					_UpperCamelCase											=   "prefix_path"
					with pytest.raises(_UpperCamelCase				):
										_UpperCamelCase											=   make_file_instructions(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)










'''simple docstring'''



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     A_ (  nn.Linear  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								_UpperCamelCase											=   pruning_method

								if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
													_UpperCamelCase											=   mask_scale
													_UpperCamelCase											=   mask_init
													_UpperCamelCase											=   nn.Parameter(torch.empty(self.weight.size()   )   )
													self.init_mask()




			def       a	(					self   ):
								if self.mask_init == "constant":
													init.constant_(self.mask_scores			,     val=self.mask_scale   )
								elif self.mask_init == "uniform":
													init.uniform_(self.mask_scores			,     a=-self.mask_scale			,     b=self.mask_scale   )
								elif self.mask_init == "kaiming":
													init.kaiming_uniform_(self.mask_scores			,     a=math.sqrt(5   )   )




			def       a	(					self			,     A_			,     A_   ):
								# Get the mask
								if self.pruning_method == "topK":
													_UpperCamelCase											=   TopKBinarizer.apply(self.mask_scores			,     A_   )
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													_UpperCamelCase											=   "sigmoied" in self.pruning_method
													_UpperCamelCase											=   ThresholdBinarizer.apply(self.mask_scores			,     A_			,     A_   )
								elif self.pruning_method == "magnitude":
													_UpperCamelCase											=   MagnitudeBinarizer.apply(self.weight			,     A_   )
								elif self.pruning_method == "l0":
													_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   -0.1, 1.1, 2 / 3
													if self.training:
																		_UpperCamelCase											=   torch.zeros_like(self.mask_scores   ).uniform_().clamp(0.0001			,     0.9999   )
																		_UpperCamelCase											=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b   )
													else:
																		_UpperCamelCase											=   torch.sigmoid(self.mask_scores   )
													_UpperCamelCase											=   s * (r - l) + l
													_UpperCamelCase											=   s_bar.clamp(min=0.0			,     max=1.0   )
								# Mask weights with computed mask
								_UpperCamelCase											=   mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(A_			,     A_			,     self.bias   )



