"""simple docstring"""






import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=sys.maxsize )    ->	Optional[Any]:
							a_							:			int     		=					"bilinear"
							a_							:			int     		=					max_size
							a_							:			Optional[Any]     		=					short_edge_length







	def __call__(			self       ,     _SCREAMING_SNAKE_CASE )    ->	Union[str, Any]:
							a_							:			List[Any]     		=					[]
							for img in imgs:
													a_		,					a_							:			List[Any]     		=					img.shape[:2]
													# later: provide list and randomly choose index for resize
													a_							:			str     		=					np.random.randint(self.short_edge_length[0]       ,     self.short_edge_length[1] + 1 )
													if size == 0:
																			return img
													a_							:			List[Any]     		=					size * 1.0 / min(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													if h < w:
																			a_		,					a_							:			str     		=					size, scale * w
													else:
																			a_		,					a_							:			Dict     		=					scale * h, size
													if max(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ) > self.max_size:
																			a_							:			Optional[int]     		=					self.max_size * 1.0 / max(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
																			a_							:			Optional[int]     		=					newh * scale
																			a_							:			Union[str, Any]     		=					neww * scale
													a_							:			Dict     		=					int(neww + 0.5 )
													a_							:			Union[str, Any]     		=					int(newh + 0.5 )

													if img.dtype == np.uinta:
																			a_							:			Optional[Any]     		=					Image.fromarray(_SCREAMING_SNAKE_CASE )
																			a_							:			List[str]     		=					pil_image.resize((neww, newh)       ,     PILImageResampling.BILINEAR )
																			a_							:			Optional[Any]     		=					np.asarray(_SCREAMING_SNAKE_CASE )
													else:
																			a_							:			int     		=					img.permute(2       ,     0       ,     1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
																			a_							:			Dict     		=					nn.functional.interpolate(
																			    _SCREAMING_SNAKE_CASE       ,     (newh, neww)       ,     mode=self.interp_method       ,     align_corners=_SCREAMING_SNAKE_CASE ).squeeze(0 )
													img_augs.append(_SCREAMING_SNAKE_CASE )

							return img_augs




class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE )    ->	Union[str, Any]:
							a_							:			Dict     		=					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,     cfg.INPUT.MAX_SIZE_TEST )
							a_							:			List[Any]     		=					cfg.INPUT.FORMAT
							a_							:			Optional[int]     		=					cfg.SIZE_DIVISIBILITY
							a_							:			int     		=					cfg.PAD_VALUE
							a_							:			Any     		=					cfg.INPUT.MAX_SIZE_TEST
							a_							:			Union[str, Any]     		=					cfg.MODEL.DEVICE
							a_							:			Optional[int]     		=					torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )       ,     1       ,     1 )
							a_							:			Optional[Any]     		=					torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )       ,     1       ,     1 )
							a_							:			int     		=					lambda _SCREAMING_SNAKE_CASE : (x - self.pixel_mean) / self.pixel_std







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	Union[str, Any]:
							a_							:			Union[str, Any]     		=					tuple(max(_SCREAMING_SNAKE_CASE ) for s in zip(*[img.shape for img in images] ) )
							a_							:			Union[str, Any]     		=					[im.shape[-2:] for im in images]
							a_							:			Tuple     		=					[
							    nn.functional.pad(
							        _SCREAMING_SNAKE_CASE       ,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,     value=self.pad_value       ,     )
							    for size, im in zip(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
							]

							return torch.stack(_SCREAMING_SNAKE_CASE ), torch.tensor(_SCREAMING_SNAKE_CASE )







	def __call__(			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=False )    ->	Dict:
							with torch.no_grad():
													if not isinstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ):
																			a_							:			int     		=					[images]
													if single_image:
																			assert len(_SCREAMING_SNAKE_CASE ) == 1
													for i in range(len(_SCREAMING_SNAKE_CASE ) ):
																			if isinstance(images[i]       ,     torch.Tensor ):
																									images.insert(_SCREAMING_SNAKE_CASE       ,     images.pop(_SCREAMING_SNAKE_CASE ).to(self.device ).float() )
																			elif not isinstance(images[i]       ,     torch.Tensor ):
																									images.insert(
																									    _SCREAMING_SNAKE_CASE       ,     torch.as_tensor(img_tensorize(images.pop(_SCREAMING_SNAKE_CASE )       ,     input_format=self.input_format ) )
																									    .to(self.device )
																									    .float()       ,     )
            # resize smallest edge
													a_							:			Any     		=					torch.tensor([im.shape[:2] for im in images] )
													a_							:			int     		=					self.aug(_SCREAMING_SNAKE_CASE )
													# transpose images and convert to torch tensors
													# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
													# now normalize before pad to avoid useless arithmetic
													a_							:			int     		=					[self.normalizer(_SCREAMING_SNAKE_CASE ) for x in images]
													# now pad them to do the following operations
													a_		,					a_							:			Dict     		=					self.pad(_SCREAMING_SNAKE_CASE )
													# Normalize

													if self.size_divisibility > 0:
																			raise NotImplementedError()
													# pad
													a_							:			Dict     		=					torch.true_divide(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													if single_image:
																			return images[0], sizes[0], scales_yx[0]
													else:
																			return images, sizes, scales_yx




def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[Any]		,			_SCREAMING_SNAKE_CASE	:Tuple  )				-> Optional[Any]:
						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes



def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:Tuple[int, int]  )				-> Tuple:
						assert torch.isfinite(_SCREAMING_SNAKE_CASE  ).all(), "Box tensor contains infinite or NaN!"
						a_		,					a_							:			List[str]     		=					box_size
						tensor[:, 0].clamp_(min=0		,			max=_SCREAMING_SNAKE_CASE  )
						tensor[:, 1].clamp_(min=0		,			max=_SCREAMING_SNAKE_CASE  )
						tensor[:, 2].clamp_(min=0		,			max=_SCREAMING_SNAKE_CASE  )
						tensor[:, 3].clamp_(min=0		,			max=_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()




