"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase     					=     logging.get_logger(__name__)
class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					List[str]						      =							["""pixel_values"""]







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE = True       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,     _SCREAMING_SNAKE_CASE = True       ,     _SCREAMING_SNAKE_CASE = 1 / 2_5_5       ,     _SCREAMING_SNAKE_CASE = True       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	None:
							super().__init__(**_SCREAMING_SNAKE_CASE )
							a_							:			Union[str, Any]     		=					size if size is not None else {"height": 2_2_4, "width": 2_2_4}
							a_							:			List[Any]     		=					get_size_dict(_SCREAMING_SNAKE_CASE )
							a_							:			Tuple     		=					do_resize
							a_							:			List[str]     		=					do_rescale
							a_							:			Dict     		=					do_normalize
							a_							:			Optional[Any]     		=					size
							a_							:			int     		=					resample
							a_							:			Dict     		=					rescale_factor
							a_							:			List[Any]     		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							a_							:			Optional[int]     		=					image_std if image_std is not None else IMAGENET_STANDARD_STD







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	np.ndarray:
							a_							:			Any     		=					get_size_dict(_SCREAMING_SNAKE_CASE )
							if "height" not in size or "width" not in size:
													raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
							return resize(
							    _SCREAMING_SNAKE_CASE       ,     size=(size["height"], size["width"])       ,     resample=_SCREAMING_SNAKE_CASE       ,     data_format=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE )    ->	np.ndarray:
							return rescale(_SCREAMING_SNAKE_CASE       ,     scale=_SCREAMING_SNAKE_CASE       ,     data_format=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	np.ndarray:
							return normalize(_SCREAMING_SNAKE_CASE       ,     mean=_SCREAMING_SNAKE_CASE       ,     std=_SCREAMING_SNAKE_CASE       ,     data_format=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	Tuple:
							a_							:			str     		=					do_resize if do_resize is not None else self.do_resize
							a_							:			List[str]     		=					do_rescale if do_rescale is not None else self.do_rescale
							a_							:			Tuple     		=					do_normalize if do_normalize is not None else self.do_normalize
							a_							:			Optional[Any]     		=					resample if resample is not None else self.resample
							a_							:			Tuple     		=					rescale_factor if rescale_factor is not None else self.rescale_factor
							a_							:			Optional[int]     		=					image_mean if image_mean is not None else self.image_mean
							a_							:			List[Any]     		=					image_std if image_std is not None else self.image_std

							a_							:			List[str]     		=					size if size is not None else self.size
							a_							:			Optional[Any]     		=					get_size_dict(_SCREAMING_SNAKE_CASE )

							a_							:			Union[str, Any]     		=					make_list_of_images(_SCREAMING_SNAKE_CASE )

							if not valid_images(_SCREAMING_SNAKE_CASE ):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray." )

							if do_resize and size is None:
													raise ValueError("Size must be specified if do_resize is True." )

							if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True." )

							# All transformations expect numpy arrays.
							a_							:			Union[str, Any]     		=					[to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]

							if do_resize:
													a_							:			int     		=					[self.resize(image=_SCREAMING_SNAKE_CASE       ,     size=_SCREAMING_SNAKE_CASE       ,     resample=_SCREAMING_SNAKE_CASE ) for image in images]

							if do_rescale:
													a_							:			Dict     		=					[self.rescale(image=_SCREAMING_SNAKE_CASE       ,     scale=_SCREAMING_SNAKE_CASE ) for image in images]

							if do_normalize:
													a_							:			Dict     		=					[self.normalize(image=_SCREAMING_SNAKE_CASE       ,     mean=_SCREAMING_SNAKE_CASE       ,     std=_SCREAMING_SNAKE_CASE ) for image in images]

							a_							:			str     		=					[to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ) for image in images]

							a_							:			Optional[Any]     		=					{"pixel_values": images}
							return BatchFeature(data=_SCREAMING_SNAKE_CASE       ,     tensor_type=_SCREAMING_SNAKE_CASE )





"""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()




