






"""simple docstring"""



import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__A		:	Union[str, Any]      =		(30, 487)
__A		:	Union[str, Any]      =		(36, 1_024)
__A		:	int      =		(100, 100)
__A		:	Union[str, Any]      =		100

__A		:	Union[str, Any]      =		datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

__A		:	Optional[int]      =		os.path.split(__file__)
__A		:	Tuple      =		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))



@get_duration
def 						lowercase   (    __snake_case    :			Optional[int]		, __snake_case    :			Union[str, Any]		, __snake_case    :			Union[str, Any]     ):
		with ArrowWriter(features=__snake_case		, path=os.path.join(__snake_case		, '''beta.arrow'''     )     ) as writer:
				for key, record in dummy_data:
						lowercase_			:			List[str]								=       my_features.encode_example(__snake_case     )
						writer.write(__snake_case     )
				lowercase_			:			List[Any]								=       writer.finalize()



@get_duration
def 						lowercase   (    __snake_case    :			str		, __snake_case    :			Union[str, Any]     ):
		lowercase_			:			Union[str, Any]								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		for _ in dataset:
				pass



@get_duration
def 						lowercase   (    __snake_case    :			Optional[Any]		, __snake_case    :			Tuple     ):
		lowercase_			:			List[Any]								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		dataset.set_format('''numpy'''     )
		for _ in dataset:
				pass



@get_duration
def 						lowercase   (    __snake_case    :			Any		, __snake_case    :			str     ):
		lowercase_			:			Any								=       1_0
		lowercase_			:			str								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		for i in range(0		, len(__snake_case     )		, __snake_case     ):
				lowercase_			:			Union[str, Any]								=       dataset[i : i + batch_size]



@get_duration
def 						lowercase   (    __snake_case    :			List[str]		, __snake_case    :			List[str]     ):
		lowercase_			:			Dict								=       1_0
		lowercase_			:			Optional[Any]								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		dataset.set_format('''numpy'''     )
		for i in range(0		, len(__snake_case     )		, __snake_case     ):
				lowercase_			:			Union[str, Any]								=       dataset[i : i + batch_size]



@get_duration
def 						lowercase   (    __snake_case    :			Tuple		, __snake_case    :			Tuple     ):
		lowercase_			:			List[Any]								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		for col in feats:
				lowercase_			:			Tuple								=       dataset[col]



@get_duration
def 						lowercase   (    __snake_case    :			Optional[Any]		, __snake_case    :			int     ):
		lowercase_			:			int								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		dataset.set_format('''numpy'''     )
		for col in feats:
				lowercase_			:			int								=       dataset[col]



def 						lowercase   (    ):
		lowercase_			:			List[Any]								=       {}
		lowercase_			:			Union[str, Any]								=       (
		    read_unformated,
		    read_formatted_as_numpy,
		    read_batch_unformated,
		    read_batch_formatted_as_numpy,
		    read_col_unformated,
		    read_col_formatted_as_numpy,
		)
		with tempfile.TemporaryDirectory() as tmp_dir:
				lowercase_			:			Union[str, Any]								=       datasets.Features({'''image''': ArrayaD(__snake_case		, dtype='''float32'''     )}     )
				lowercase_			:			Any								=       generate_examples(features=__snake_case		, num_examples=__snake_case     )
				lowercase_			:			Any								=       write(__snake_case		, __snake_case		, __snake_case     )
				for read_func in read_functions:
						lowercase_			:			List[Any]								=       read_func(__snake_case		, __snake_case     )

		with tempfile.TemporaryDirectory() as tmp_dir:
				# don't use fixed length for fair comparison
				# feats = datasets.Features(
				#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
				# )
				lowercase_			:			List[str]								=       datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''     )     )     )}     )
				lowercase_			:			Dict								=       generate_examples(
				    features=__snake_case		, num_examples=__snake_case		, seq_shapes={'''image''': SPEED_TEST_SHAPE}     )
				lowercase_			:			str								=       write(__snake_case		, __snake_case		, __snake_case     )
				for read_func in read_functions:
						lowercase_			:			Dict								=       read_func(__snake_case		, __snake_case     )

		with tempfile.TemporaryDirectory() as tmp_dir:
				# don't use fixed length for fair comparison
				# feats = datasets.Features(
				#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
				# )
				lowercase_			:			Any								=       datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''     )     )}     )
				lowercase_			:			List[Any]								=       generate_examples(
				    features=__snake_case		, num_examples=__snake_case		, seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		, )
				lowercase_			:			int								=       write(__snake_case		, __snake_case		, __snake_case     )
				for read_func in read_functions:
						lowercase_			:			Any								=       read_func(__snake_case		, __snake_case     )

		with open(__snake_case		, '''wb'''     ) as f:
				f.write(json.dumps(__snake_case     ).encode('''utf-8'''     )     )


if __name__ == "__main__":  # useful to run the profiler
						benchmark_array_xd()











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A		:	Any      =		{
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	List[str]      =		['''MarkupLMTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Optional[int]      =		[
						    '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MarkupLMForQuestionAnswering''',
						    '''MarkupLMForSequenceClassification''',
						    '''MarkupLMForTokenClassification''',
						    '''MarkupLMModel''',
						    '''MarkupLMPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
						from .feature_extraction_markuplm import MarkupLMFeatureExtractor
						from .processing_markuplm import MarkupLMProcessor
						from .tokenization_markuplm import MarkupLMTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_markuplm_fast import MarkupLMTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_markuplm import (
												    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MarkupLMForQuestionAnswering,
												    MarkupLMForSequenceClassification,
												    MarkupLMForTokenClassification,
												    MarkupLMModel,
												    MarkupLMPreTrainedModel,
												)


else:
						import sys

						__A		:	Optional[Any]      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)



