

"""simple docstring"""




from typing import TYPE_CHECKING

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


__A	:       Optional[int]        =						{
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       List[Any]        =						[
	    'Constraint',
	    'ConstraintListState',
	    'DisjunctiveConstraint',
	    'PhrasalConstraint',
	]
	__A	:       Union[str, Any]        =						[
	    'BeamHypotheses',
	    'BeamScorer',
	    'BeamSearchScorer',
	    'ConstrainedBeamSearchScorer',
	]
	__A	:       Dict        =						[
	    'EpsilonLogitsWarper',
	    'EtaLogitsWarper',
	    'ForcedBOSTokenLogitsProcessor',
	    'ForcedEOSTokenLogitsProcessor',
	    'HammingDiversityLogitsProcessor',
	    'InfNanRemoveLogitsProcessor',
	    'LogitsProcessor',
	    'LogitsProcessorList',
	    'LogitsWarper',
	    'MinLengthLogitsProcessor',
	    'MinNewTokensLengthLogitsProcessor',
	    'NoBadWordsLogitsProcessor',
	    'NoRepeatNGramLogitsProcessor',
	    'PrefixConstrainedLogitsProcessor',
	    'RepetitionPenaltyLogitsProcessor',
	    'SequenceBiasLogitsProcessor',
	    'EncoderRepetitionPenaltyLogitsProcessor',
	    'TemperatureLogitsWarper',
	    'TopKLogitsWarper',
	    'TopPLogitsWarper',
	    'TypicalLogitsWarper',
	    'EncoderNoRepeatNGramLogitsProcessor',
	    'ExponentialDecayLengthPenalty',
	    'LogitNormalization',
	]
	__A	:       List[str]        =						[
	    'MaxNewTokensCriteria',
	    'MaxLengthCriteria',
	    'MaxTimeCriteria',
	    'StoppingCriteria',
	    'StoppingCriteriaList',
	    'validate_stopping_criteria',
	]
	__A	:       int        =						[
	    'GenerationMixin',
	    'top_k_top_p_filtering',
	    'GreedySearchEncoderDecoderOutput',
	    'GreedySearchDecoderOnlyOutput',
	    'SampleEncoderDecoderOutput',
	    'SampleDecoderOnlyOutput',
	    'BeamSearchEncoderDecoderOutput',
	    'BeamSearchDecoderOnlyOutput',
	    'BeamSampleEncoderDecoderOutput',
	    'BeamSampleDecoderOnlyOutput',
	    'ContrastiveSearchEncoderDecoderOutput',
	    'ContrastiveSearchDecoderOnlyOutput',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       Dict        =						[
	    'TFForcedBOSTokenLogitsProcessor',
	    'TFForcedEOSTokenLogitsProcessor',
	    'TFLogitsProcessor',
	    'TFLogitsProcessorList',
	    'TFLogitsWarper',
	    'TFMinLengthLogitsProcessor',
	    'TFNoBadWordsLogitsProcessor',
	    'TFNoRepeatNGramLogitsProcessor',
	    'TFRepetitionPenaltyLogitsProcessor',
	    'TFTemperatureLogitsWarper',
	    'TFTopKLogitsWarper',
	    'TFTopPLogitsWarper',
	    'TFForceTokensLogitsProcessor',
	    'TFSuppressTokensAtBeginLogitsProcessor',
	    'TFSuppressTokensLogitsProcessor',
	]
	__A	:       int        =						[
	    'TFGenerationMixin',
	    'tf_top_k_top_p_filtering',
	    'TFGreedySearchDecoderOnlyOutput',
	    'TFGreedySearchEncoderDecoderOutput',
	    'TFSampleEncoderDecoderOutput',
	    'TFSampleDecoderOnlyOutput',
	    'TFBeamSearchEncoderDecoderOutput',
	    'TFBeamSearchDecoderOnlyOutput',
	    'TFBeamSampleEncoderDecoderOutput',
	    'TFBeamSampleDecoderOnlyOutput',
	    'TFContrastiveSearchEncoderDecoderOutput',
	    'TFContrastiveSearchDecoderOnlyOutput',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       int        =						[
	    'FlaxForcedBOSTokenLogitsProcessor',
	    'FlaxForcedEOSTokenLogitsProcessor',
	    'FlaxLogitsProcessor',
	    'FlaxLogitsProcessorList',
	    'FlaxLogitsWarper',
	    'FlaxMinLengthLogitsProcessor',
	    'FlaxTemperatureLogitsWarper',
	    'FlaxTopKLogitsWarper',
	    'FlaxTopPLogitsWarper',
	]
	__A	:       Optional[int]        =						[
	    'FlaxGenerationMixin',
	    'FlaxGreedySearchOutput',
	    'FlaxSampleOutput',
	    'FlaxBeamSearchOutput',
	]

if TYPE_CHECKING:
	from .configuration_utils import GenerationConfig
	from .streamers import TextIteratorStreamer, TextStreamer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
		from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
		from .logits_process import (
		    EncoderNoRepeatNGramLogitsProcessor,
		    EncoderRepetitionPenaltyLogitsProcessor,
		    EpsilonLogitsWarper,
		    EtaLogitsWarper,
		    ExponentialDecayLengthPenalty,
		    ForcedBOSTokenLogitsProcessor,
		    ForcedEOSTokenLogitsProcessor,
		    HammingDiversityLogitsProcessor,
		    InfNanRemoveLogitsProcessor,
		    LogitNormalization,
		    LogitsProcessor,
		    LogitsProcessorList,
		    LogitsWarper,
		    MinLengthLogitsProcessor,
		    MinNewTokensLengthLogitsProcessor,
		    NoBadWordsLogitsProcessor,
		    NoRepeatNGramLogitsProcessor,
		    PrefixConstrainedLogitsProcessor,
		    RepetitionPenaltyLogitsProcessor,
		    SequenceBiasLogitsProcessor,
		    TemperatureLogitsWarper,
		    TopKLogitsWarper,
		    TopPLogitsWarper,
		    TypicalLogitsWarper,
		)
		from .stopping_criteria import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)
		from .utils import (
		    BeamSampleDecoderOnlyOutput,
		    BeamSampleEncoderDecoderOutput,
		    BeamSearchDecoderOnlyOutput,
		    BeamSearchEncoderDecoderOutput,
		    ContrastiveSearchDecoderOnlyOutput,
		    ContrastiveSearchEncoderDecoderOutput,
		    GenerationMixin,
		    GreedySearchDecoderOnlyOutput,
		    GreedySearchEncoderDecoderOutput,
		    SampleDecoderOnlyOutput,
		    SampleEncoderDecoderOutput,
		    top_k_top_p_filtering,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tf_logits_process import (
		    TFForcedBOSTokenLogitsProcessor,
		    TFForcedEOSTokenLogitsProcessor,
		    TFForceTokensLogitsProcessor,
		    TFLogitsProcessor,
		    TFLogitsProcessorList,
		    TFLogitsWarper,
		    TFMinLengthLogitsProcessor,
		    TFNoBadWordsLogitsProcessor,
		    TFNoRepeatNGramLogitsProcessor,
		    TFRepetitionPenaltyLogitsProcessor,
		    TFSuppressTokensAtBeginLogitsProcessor,
		    TFSuppressTokensLogitsProcessor,
		    TFTemperatureLogitsWarper,
		    TFTopKLogitsWarper,
		    TFTopPLogitsWarper,
		)
		from .tf_utils import (
		    TFBeamSampleDecoderOnlyOutput,
		    TFBeamSampleEncoderDecoderOutput,
		    TFBeamSearchDecoderOnlyOutput,
		    TFBeamSearchEncoderDecoderOutput,
		    TFContrastiveSearchDecoderOnlyOutput,
		    TFContrastiveSearchEncoderDecoderOutput,
		    TFGenerationMixin,
		    TFGreedySearchDecoderOnlyOutput,
		    TFGreedySearchEncoderDecoderOutput,
		    TFSampleDecoderOnlyOutput,
		    TFSampleEncoderDecoderOutput,
		    tf_top_k_top_p_filtering,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .flax_logits_process import (
		    FlaxForcedBOSTokenLogitsProcessor,
		    FlaxForcedEOSTokenLogitsProcessor,
		    FlaxLogitsProcessor,
		    FlaxLogitsProcessorList,
		    FlaxLogitsWarper,
		    FlaxMinLengthLogitsProcessor,
		    FlaxTemperatureLogitsWarper,
		    FlaxTopKLogitsWarper,
		    FlaxTopPLogitsWarper,
		)
		from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
	import sys

	__A	:       Union[str, Any]        =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






'''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        __magic_name__		(       _UpperCAmelCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    Dict							,     lowercase_							:    List[str]=False       ):
										lowercase_		,		lowercase_		,		lowercase_   :     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(lowercase_       )							,     """wb"""       ).close()
										lowercase_   :     Union[str, Any]									=			pa.Table.from_pydict({"""filename""": [Path(lowercase_       ).name] * 100}       )
										if take == -1:
																	lowercase_   :     Optional[int]									=			len(lowercase_       ) - skip
										if skip is not None and take is not None:
																	lowercase_   :     Any									=			pa_table.slice(lowercase_							,     lowercase_       )
										return pa_table





class        __magic_name__		(       _UpperCAmelCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     Union[str, Any]									=			"""my_name"""
										lowercase_   :     Optional[Any]									=			SplitInfo(name="""train"""							,     num_examples=100       )
										lowercase_   :     Dict									=			SplitInfo(name="""test"""							,     num_examples=100       )
										lowercase_   :     str									=			[train_info, test_info]
										lowercase_   :     Any									=			SplitDict()
										split_dict.add(lowercase_       )
										split_dict.add(lowercase_       )
										lowercase_   :     Union[str, Any]									=			DatasetInfo(splits=lowercase_       )

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

																	lowercase_   :     Optional[Any]									=			"""test[:33%]"""
																	lowercase_   :     List[Any]									=			Dataset(**reader.read(lowercase_							,     lowercase_							,     lowercase_       )       )
																	self.assertEqual(dset["""filename"""][0]							,     f'''{name}-test'''       )
																	self.assertEqual(dset.num_rows							,     33       )
																	self.assertEqual(dset.num_columns							,     1       )

																	lowercase_   :     str									=			["""train""", """test[:33%]"""]
																	lowercase_   :     Any									=			[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""       )]
																	for instructions in [instructionsa, instructionsa]:
																								lowercase_   :     Dict									=			[reader.read(lowercase_							,     lowercase_							,     lowercase_       ) for instr in instructions]
																								lowercase_		,		lowercase_   :     List[Any]									=			(Dataset(**lowercase_       ) 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							,     lowercase_       )
																								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							,     lowercase_       )
																								self.assertEqual(str(test_dset.split       )							,     """test[:33%]"""       )
																								del train_dset, test_dset







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_   :     List[Any]									=			"""my_name"""
										lowercase_   :     int									=			SplitInfo(name="""train"""							,     num_examples=1000							,     shard_lengths=[100] * 10       )
										lowercase_   :     Tuple									=			[train_info]
										lowercase_   :     Any									=			SplitDict()
										split_dict.add(lowercase_       )
										lowercase_   :     Union[str, Any]									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Optional[int]									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     Optional[int]									=			"""train[:33%]"""
																	lowercase_   :     int									=			Dataset(**reader.read(lowercase_							,     lowercase_							,     lowercase_       )       )
																	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       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     Any									=			SplitInfo(name="""train"""							,     num_examples=100       )
										lowercase_   :     Optional[int]									=			SplitInfo(name="""test"""							,     num_examples=100       )
										lowercase_   :     Union[str, Any]									=			SplitDict()
										split_dict.add(lowercase_       )
										split_dict.add(lowercase_       )
										lowercase_   :     Tuple									=			DatasetInfo(splits=lowercase_       )

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

																	lowercase_   :     List[str]									=			[
																	    {"""filename""": os.path.join(lowercase_							,     """train"""       )},
																	    {"""filename""": os.path.join(lowercase_							,     """test"""       ), """skip""": 10, """take""": 10},
																	]
																	lowercase_   :     int									=			Dataset(**reader.read_files(lowercase_							,     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 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Union[str, Any]					)     -> Union[str, Any]:
							lowercase_   :     str									=			arrow_file
							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
														lowercase_   :     Optional[Any]									=			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 				lowerCamelCase      ( UpperCAmelCase__			:   Tuple , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Optional[int]					)     -> Any:
							lowercase_   :     Tuple									=			arrow_file
							lowercase_   :     Optional[Any]									=			ArrowReader("""""" , UpperCAmelCase__					)
							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
														lowercase_   :     Dict									=			reader.read_files([{"""filename""": filename}] , in_memory=UpperCAmelCase__					)
							assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
							lowercase_   :     Optional[int]									=			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 				lowerCamelCase      ( )     -> Optional[Any]:
							assert ReadInstruction("""train""" , to=10 , unit="""abs"""					).to_spec() == "train[:10]"
							assert ReadInstruction("""train""" , from_=-80 , to=10 , unit="""%"""					).to_spec() == "train[-80%:10%]"

							lowercase_   :     Tuple									=			"""train+test"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_test

							lowercase_   :     List[Any]									=			"""train[2:10]"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_abs

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

							lowercase_   :     Optional[int]									=			"""train[:10%](closest)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == "train[:10%]"

							lowercase_   :     List[Any]									=			"""train[:10%](pct1_dropremainder)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_pct_rounding

							lowercase_   :     Union[str, Any]									=			"""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_test_pct_rounding




def 				lowerCamelCase      ( )     -> int:
							lowercase_   :     Optional[int]									=			"""dummy"""
							lowercase_   :     Union[str, Any]									=			[SplitInfo(name="""train""" , num_examples=100					)]
							lowercase_   :     Optional[int]									=			"""train[:33%]"""
							lowercase_   :     Dict									=			"""arrow"""
							lowercase_   :     int									=			"""prefix"""

							lowercase_   :     int									=			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}
							]

							lowercase_   :     Dict									=			[SplitInfo(name="""train""" , num_examples=100 , shard_lengths=[10] * 10					)]
							lowercase_   :     Dict									=			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 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   Any					)     -> List[str]:
							lowercase_   :     str									=			[SplitInfo(name="""train""" , num_examples=100					)]
							lowercase_   :     Any									=			"""train"""
							lowercase_   :     Optional[int]									=			"""arrow"""
							lowercase_   :     str									=			"""prefix_path"""
							with pytest.raises(UpperCAmelCase__					):
														lowercase_   :     List[str]									=			make_file_instructions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)





