"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCamelCase     					=     {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

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

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

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    '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

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""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


UpperCamelCase     					=     (30, 4_87)
UpperCamelCase     					=     (36, 10_24)
UpperCamelCase     					=     (1_00, 1_00)
UpperCamelCase     					=     1_00

UpperCamelCase     					=     datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

UpperCamelCase					,      UpperCamelCase     					=     os.path.split(__file__)
UpperCamelCase     					=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Tuple  )				-> Any:
						with ArrowWriter(features=_SCREAMING_SNAKE_CASE		,			path=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )  ) as writer:
												for key, record in dummy_data:
																		a_							:			int     		=					my_features.encode_example(_SCREAMING_SNAKE_CASE  )
																		writer.write(_SCREAMING_SNAKE_CASE  )
												a_		,					a_							:			str     		=					writer.finalize()

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Any  )				-> Tuple:
						a_							:			Dict     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for _ in dataset:
												pass

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Dict  )				-> Optional[Any]:
						a_							:			List[str]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for _ in dataset:
												pass

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[Any]		,			_SCREAMING_SNAKE_CASE	:List[str]  )				-> List[str]:
						a_							:			Optional[Any]     		=					10
						a_							:			Optional[int]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for i in range(0		,			len(_SCREAMING_SNAKE_CASE  )		,			_SCREAMING_SNAKE_CASE  ):
												a_							:			List[str]     		=					dataset[i : i + batch_size]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:List[str]  )				-> Optional[int]:
						a_							:			List[str]     		=					10
						a_							:			int     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for i in range(0		,			len(_SCREAMING_SNAKE_CASE  )		,			_SCREAMING_SNAKE_CASE  ):
												a_							:			str     		=					dataset[i : i + batch_size]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Optional[Any]  )				-> Dict:
						a_							:			List[str]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for col in feats:
												a_							:			List[Any]     		=					dataset[col]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Optional[Any]  )				-> Dict:
						a_							:			Any     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for col in feats:
												a_							:			Any     		=					dataset[col]

def  lowerCAmelCase_      ()				-> Optional[Any]:
						a_							:			List[Any]     		=					{}
						a_							:			str     		=					(
						    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:
												a_							:			Optional[int]     		=					datasets.Features({"image": ArrayaD(_SCREAMING_SNAKE_CASE		,			dtype="float32"  )}  )
												a_							:			Optional[Any]     		=					generate_examples(features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE  )
												a_							:			List[str]     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			Optional[int]     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_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])}
												# )
												a_							:			Union[str, Any]     		=					datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"  )  )  )}  )
												a_							:			List[str]     		=					generate_examples(
												    features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE		,			seq_shapes={"image": SPEED_TEST_SHAPE}  )
												a_							:			str     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			List[str]     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_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])}
												# )
												a_							:			int     		=					datasets.Features({"image": datasets.Sequence(datasets.Value("float32"  )  )}  )
												a_							:			str     		=					generate_examples(
												    features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE		,			seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,			)
												a_							:			List[Any]     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			str     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

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


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




