




"""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      lowerCamelCase__      (					lowerCamelCase_     ):


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







					snake_case     ,			snake_case     ,			snake_case						:  str        =			(
					    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(SCREAMING_SNAKE_CASE							)			,	"wb"							).close()
					snake_case						:  int        =			pa.Table.from_pydict({"filename": [Path(SCREAMING_SNAKE_CASE							).name] * 100}							)
					if take == -1:
							snake_case						:  Dict        =			len(SCREAMING_SNAKE_CASE							) - skip
					if skip is not None and take is not None:
							snake_case						:  Tuple        =			pa_table.slice(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
					return pa_table







class      lowerCamelCase__      (					lowerCamelCase_     ):


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			"my_name"
					snake_case						:  Dict        =			SplitInfo(name="train"			,	num_examples=100							)
					snake_case						:  List[Any]        =			SplitInfo(name="test"			,	num_examples=100							)
					snake_case						:  int        =			[train_info, test_info]
					snake_case						:  Dict        =			SplitDict()
					split_dict.add(SCREAMING_SNAKE_CASE							)
					split_dict.add(SCREAMING_SNAKE_CASE							)
					snake_case						:  int        =			DatasetInfo(splits=SCREAMING_SNAKE_CASE							)

					with tempfile.TemporaryDirectory() as tmp_dir:
							snake_case						:  Tuple        =			ReaderTest(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

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

							snake_case						:  Optional[Any]        =			["train", "test[:33%]"]
							snake_case						:  List[str]        =			[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
							for instructions in [instructionsa, instructionsa]:
									snake_case						:  Dict        =			[reader.read(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) for instr in instructions]
									snake_case     ,			snake_case						:  List[str]        =			(Dataset(**SCREAMING_SNAKE_CASE							) for dataset_kwargs in datasets_kwargs)
									self.assertEqual(train_dset["filename"][0]			,	F'''{name}-train'''							)
									self.assertEqual(train_dset.num_rows			,	100							)
									self.assertEqual(train_dset.num_columns			,	1							)
									self.assertIsInstance(train_dset.split			,	SCREAMING_SNAKE_CASE							)
									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			,	SCREAMING_SNAKE_CASE							)
									self.assertEqual(str(test_dset.split							)			,	"test[:33%]"							)
									del train_dset, test_dset


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			"my_name"
					snake_case						:  Optional[Any]        =			SplitInfo(name="train"			,	num_examples=1_000			,	shard_lengths=[100] * 10							)
					snake_case						:  Union[str, Any]        =			[train_info]
					snake_case						:  int        =			SplitDict()
					split_dict.add(SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[int]        =			DatasetInfo(splits=SCREAMING_SNAKE_CASE							)

					with tempfile.TemporaryDirectory() as tmp_dir:
							snake_case						:  str        =			ReaderTest(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  str        =			"train[:33%]"
							snake_case						:  Optional[Any]        =			Dataset(**reader.read(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)							)
							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			,	330							)
							self.assertEqual(dset.num_columns			,	1							)



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			SplitInfo(name="train"			,	num_examples=100							)
					snake_case						:  Dict        =			SplitInfo(name="test"			,	num_examples=100							)
					snake_case						:  Optional[Any]        =			SplitDict()
					split_dict.add(SCREAMING_SNAKE_CASE							)
					split_dict.add(SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[int]        =			DatasetInfo(splits=SCREAMING_SNAKE_CASE							)

					with tempfile.TemporaryDirectory() as tmp_dir:
							snake_case						:  int        =			ReaderTest(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  List[str]        =			[
							    {"filename": os.path.join(SCREAMING_SNAKE_CASE			,	"train"							)},
							    {"filename": os.path.join(SCREAMING_SNAKE_CASE			,	"test"							), "skip": 10, "take": 10},
							]
							snake_case						:  Dict        =			Dataset(**reader.read_files(SCREAMING_SNAKE_CASE			,	original_instructions="train+test[10:20]"							)							)
							self.assertEqual(dset.num_rows			,	110							)
							self.assertEqual(dset.num_columns			,	1							)
							del dset







@pytest.mark.parametrize("in_memory"    ,       [False, True]				)
def 					UpperCamelCase__       (						lowercase__					:       List[Any]    ,       lowercase__					:       List[str]    ,       lowercase__					:       Optional[int]				):
		snake_case						:  str        =			arrow_file
		with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
				snake_case						:  Dict        =			ArrowReader.read_table(lowercase__    ,       in_memory=lowercase__				)
		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 					UpperCamelCase__       (						lowercase__					:       Dict    ,       lowercase__					:       Any    ,       lowercase__					:       Any				):
		snake_case						:  List[str]        =			arrow_file
		snake_case						:  Dict        =			ArrowReader(""    ,       lowercase__				)
		with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
				snake_case						:  Any        =			reader.read_files([{"filename": filename}]    ,       in_memory=lowercase__				)
		assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
		snake_case						:  List[Any]        =			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 					UpperCamelCase__       (						):
		assert ReadInstruction("train"    ,       to=10    ,       unit="abs"				).to_spec() == "train[:10]"
		assert ReadInstruction("train"    ,       from_=-80    ,       to=10    ,       unit="%"				).to_spec() == "train[-80%:10%]"

		snake_case						:  str        =			"train+test"
		assert ReadInstruction.from_spec(lowercase__				).to_spec() == spec_train_test

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

		snake_case						:  Optional[int]        =			"train[15%:-20%]"
		assert ReadInstruction.from_spec(lowercase__				).to_spec() == spec_train_pct

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

		snake_case						:  List[str]        =			"train[:10%](pct1_dropremainder)"
		assert ReadInstruction.from_spec(lowercase__				).to_spec() == spec_train_pct_rounding

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



def 					UpperCamelCase__       (						):
		snake_case						:  str        =			"dummy"
		snake_case						:  List[Any]        =			[SplitInfo(name="train"    ,       num_examples=100				)]
		snake_case						:  Optional[Any]        =			"train[:33%]"
		snake_case						:  Dict        =			"arrow"
		snake_case						:  Any        =			"prefix"

		snake_case						:  Optional[Any]        =			make_file_instructions(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__				)
		assert isinstance(lowercase__    ,       lowercase__				)
		assert file_instructions.num_examples == 33
		assert file_instructions.file_instructions == [
		    {"filename": os.path.join(lowercase__    ,       F'''{name}-train.arrow'''				), "skip": 0, "take": 33}
		]

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



@pytest.mark.parametrize("name, expected_exception"    ,       [(None, TypeError), ("", ValueError)]				)
def 					UpperCamelCase__       (						lowercase__					:       int    ,       lowercase__					:       int				):
		snake_case						:  Any        =			[SplitInfo(name="train"    ,       num_examples=100				)]
		snake_case						:  Tuple        =			"train"
		snake_case						:  Union[str, Any]        =			"arrow"
		snake_case						:  str        =			"prefix_path"
		with pytest.raises(lowercase__				):
				snake_case						:  Optional[Any]        =			make_file_instructions(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__				)







"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__A							=     {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "GPTJForCausalLM",
							    "GPTJForQuestionAnswering",
							    "GPTJForSequenceClassification",
							    "GPTJModel",
							    "GPTJPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "TFGPTJForCausalLM",
							    "TFGPTJForQuestionAnswering",
							    "TFGPTJForSequenceClassification",
							    "TFGPTJModel",
							    "TFGPTJPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FlaxGPTJForCausalLM",
							    "FlaxGPTJModel",
							    "FlaxGPTJPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

							__A							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

