




'''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      _a    :
       def __init__(      self		:      str  ,							lowercase		:      Any  ,							lowercase		:      Optional[Any]=sys.maxsize							):



           '''simple docstring'''

           UpperCAmelCase     			=						'''bilinear'''
           UpperCAmelCase     			=						max_size
           UpperCAmelCase     			=						short_edge_length







       def __call__(      self		:      Tuple  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						[]
           for img in imgs:
               UpperCAmelCase			,	UpperCAmelCase     			=						img.shape[:2]
               # later: provide list and randomly choose index for resize
               UpperCAmelCase     			=						np.random.randint(self.short_edge_length[0]  ,							self.short_edge_length[1] + 1							)
               if size == 0:
                   return img
               UpperCAmelCase     			=						size * 1.0 / min(lowercase  ,							lowercase							)
               if h < w:
                   UpperCAmelCase			,	UpperCAmelCase     			=						size, scale * w
               else:
                   UpperCAmelCase			,	UpperCAmelCase     			=						scale * h, size
               if max(lowercase  ,							lowercase							) > self.max_size:
                   UpperCAmelCase     			=						self.max_size * 1.0 / max(lowercase  ,							lowercase							)
                   UpperCAmelCase     			=						newh * scale
                   UpperCAmelCase     			=						neww * scale
               UpperCAmelCase     			=						int(neww + 0.5							)
               UpperCAmelCase     			=						int(newh + 0.5							)

               if img.dtype == np.uinta:
                   UpperCAmelCase     			=						Image.fromarray(lowercase							)
                   UpperCAmelCase     			=						pil_image.resize((neww, newh)  ,							PILImageResampling.BILINEAR							)
                   UpperCAmelCase     			=						np.asarray(lowercase							)
               else:
                   UpperCAmelCase     			=						img.permute(2  ,							0  ,							1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
                   UpperCAmelCase     			=						nn.functional.interpolate(
                       lowercase  ,							(newh, neww)  ,							mode=self.interp_method  ,							align_corners=lowercase							).squeeze(0							)
               img_augs.append(lowercase							)

           return img_augs






class      _a    :
       def __init__(      self		:      Any  ,							lowercase		:      Dict							):



           '''simple docstring'''

           UpperCAmelCase     			=						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]  ,							cfg.INPUT.MAX_SIZE_TEST							)
           UpperCAmelCase     			=						cfg.INPUT.FORMAT
           UpperCAmelCase     			=						cfg.SIZE_DIVISIBILITY
           UpperCAmelCase     			=						cfg.PAD_VALUE
           UpperCAmelCase     			=						cfg.INPUT.MAX_SIZE_TEST
           UpperCAmelCase     			=						cfg.MODEL.DEVICE
           UpperCAmelCase     			=						torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)  ,							1  ,							1							)
           UpperCAmelCase     			=						torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)  ,							1  ,							1							)
           UpperCAmelCase     			=						lambda lowercase							: (x - self.pixel_mean) / self.pixel_std
       def        A (      self		:      List[Any]  ,							lowercase		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						tuple(max(lowercase							) for s in zip(*[img.shape for img in images]							)							)
           UpperCAmelCase     			=						[im.shape[-2:] for im in images]
           UpperCAmelCase     			=						[
               nn.functional.pad(
                   lowercase  ,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]  ,							value=self.pad_value  ,							)
               for size, im in zip(lowercase  ,							lowercase							)
           ]

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







       def __call__(      self		:      int  ,							lowercase		:      List[Any]  ,							lowercase		:      Tuple=False							):



           '''simple docstring'''

           with torch.no_grad():
               if not isinstance(lowercase  ,							lowercase							):
                   UpperCAmelCase     			=						[images]
               if single_image:
                   assert len(lowercase							) == 1
               for i in range(len(lowercase							)							):
                   if isinstance(images[i]  ,							torch.Tensor							):
                       images.insert(lowercase  ,							images.pop(lowercase							).to(self.device							).float()							)
                   elif not isinstance(images[i]  ,							torch.Tensor							):
                       images.insert(
                           lowercase  ,							torch.as_tensor(img_tensorize(images.pop(lowercase							)  ,							input_format=self.input_format							)							)
                           .to(self.device							)
                           .float()  ,							)
            # resize smallest edge
               UpperCAmelCase     			=						torch.tensor([im.shape[:2] for im in images]							)
               UpperCAmelCase     			=						self.aug(lowercase							)
               # 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
               UpperCAmelCase     			=						[self.normalizer(lowercase							) for x in images]
               # now pad them to do the following operations
               UpperCAmelCase			,	UpperCAmelCase     			=						self.pad(lowercase							)
               # Normalize

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






def 		snake_case_				(_a      :				Optional[Any]       ,  _a      :				Optional[Any]	):
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes







def 		snake_case_				(_a      :				List[str]       ,  _a      :				Tuple[int, int]	):
    assert torch.isfinite(_a	).all(), "Box tensor contains infinite or NaN!"
    UpperCAmelCase			,	UpperCAmelCase     			=						box_size
    tensor[:, 0].clamp_(min=0       ,  max=_a	)
    tensor[:, 1].clamp_(min=0       ,  max=_a	)
    tensor[:, 2].clamp_(min=0       ,  max=_a	)
    tensor[:, 3].clamp_(min=0       ,  max=_a	)










'''simple docstring'''







import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


A					       =10_00_00_00_00_00
A					       =1_00_00

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





def 		snake_case_				(_a      :				int       ,  _a      :				int	):
    UpperCAmelCase     			=						pa.Table.from_pydict({'''col''': [0] * chunk_size}	)
    UpperCAmelCase     			=						pa.concat_tables([table] * (num_examples // chunk_size)	)
    return datasets.Dataset(_a       ,  fingerprint='''table_100B'''	)


@dataclass
class      _a    :
       __a							:	int
       __a							:	int
       __a							:	int
       __a							:	int
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase     			=						np.random.default_rng(self.seed							)
           UpperCAmelCase     			=						rng.integers(low=self.low  ,							high=self.high  ,							size=self.size							).tolist()
       def __iter__(      self		:      Tuple							):



           '''simple docstring'''

           return iter(self._sampled_values							)







       def __len__(      self		:      Dict							):



           '''simple docstring'''

           return self.size






@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[0]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[-1]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[range(len(_a	) // 2       ,  len(_a	) // 2 + 1_0_2_4	)]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[RandIter(0       ,  len(_a	)       ,  1_0_2_4       ,  seed=4_2	)]





def 		snake_case_				():
    UpperCAmelCase     			=						{'''num examples''': SPEED_TEST_N_EXAMPLES}
    UpperCAmelCase     			=						(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
    print('''generating dataset'''	)
    UpperCAmelCase     			=						generate_100B_dataset(num_examples=_a       ,  chunk_size=_a	)
    print('''Functions'''	)
    for func in functions:
        print(func.__name__	)
        UpperCAmelCase     			=						func(_a	)

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


if __name__ == "__main__":  # useful to run the profiler
   benchmark_table_100B()




