
"""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							:




      def __init__(							self   ,       lowercase_   ,       lowercase_=sys.maxsize							)						->					Optional[Any]:
          UpperCAmelCase									=					'bilinear'
          UpperCAmelCase									=					max_size
          UpperCAmelCase									=					short_edge_length




      def __call__(							self   ,       lowercase_							)						->					int:
          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      _UpperCAmelCase							:




      def __init__(							self   ,       lowercase_							)						->					Dict:
          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   ,       lowercase_							)						->					Optional[Any]:
          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   ,       lowercase_   ,       lowercase_=False							)						->					int:
          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     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					Dict ) ->      Tuple:






    """simple docstring"""




    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes







def     lowercase__						(					lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Tuple[int, int] ) ->      List[Any]:






    """simple docstring"""




    assert torch.isfinite(lowerCAmelCase ).all(), "Box tensor contains infinite or NaN!"
    UpperCAmelCase			,							UpperCAmelCase									=					box_size
    tensor[:, 0].clamp_(min=0 ,				max=lowerCAmelCase )
    tensor[:, 1].clamp_(min=0 ,				max=lowerCAmelCase )
    tensor[:, 2].clamp_(min=0 ,				max=lowerCAmelCase )
    tensor[:, 3].clamp_(min=0 ,				max=lowerCAmelCase )





"""simple docstring"""

import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							str							      =	""
      __SCREAMING_SNAKE_CASE   :							str							      =	(
          None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
      )
      __SCREAMING_SNAKE_CASE   :							str							      =	None  # compression type in fsspec. ex: "gzip"
      __SCREAMING_SNAKE_CASE   :							str							      =	None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




      def __init__(							self   ,       lowercase_ = ""   ,       lowercase_ = None   ,       lowercase_ = None   ,       **lowercase_							)						->					str:
          super().__init__(self   ,       **lowercase_							)
          # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
          UpperCAmelCase									=					fsspec.open(
              lowercase_   ,       mode='rb'   ,       protocol=lowercase_   ,       compression=self.compression   ,       client_kwargs={
                  'requote_redirect_url': False,  # see https://github.com/huggingface/datasets/pull/5459
                  'trust_env': True,  # Enable reading proxy env variables.
                  **(target_options or {}).pop('client_kwargs'   ,       {}							),  # To avoid issues if it was already passed.
              }   ,       **(target_options or {})   ,       )
          UpperCAmelCase									=					os.path.basename(self.file.path.split('::'							)[0]							)
          UpperCAmelCase									=					(
              self.compressed_name[: self.compressed_name.rindex('.'							)]
              if '.' in self.compressed_name
              else self.compressed_name
          )
          UpperCAmelCase									=					None




      @classmethod
      def     a_      (							cls   ,       lowercase_							)						->					Union[str, Any]:
          # compressed file paths are always relative to the archive root
          return super()._strip_protocol(lowercase_							).lstrip('/'							)




      def     a_      (							self							)						->					int:
          if self.dir_cache is None:
              UpperCAmelCase									=					{**self.file.fs.info(self.file.path							), 'name': self.uncompressed_name}
              UpperCAmelCase									=					{f['name']: f}




      def     a_      (							self   ,       lowercase_							)						->					Any:
          return self.file.open().read()




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = "rb"   ,       lowercase_=None   ,       lowercase_=True   ,       lowercase_=None   ,       **lowercase_   ,       )						->					Optional[Any]:
          UpperCAmelCase									=					self._strip_protocol(lowercase_							)
          if mode != "rb":
              raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"							)
          return self.file.open()

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	"bz2"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"bz2"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	".bz2"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"gzip"
      __SCREAMING_SNAKE_CASE   :							Any							      =	"gzip"
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	".gz"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"lz4"
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	"lz4"
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	".lz4"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"xz"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"xz"
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	".xz"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"zstd"
      __SCREAMING_SNAKE_CASE   :							int							      =	"zstd"
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	".zst"




      def __init__(							self   ,       lowercase_   ,       lowercase_ = "rb"   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = DEFAULT_BLOCK_SIZE   ,       **lowercase_   ,       )						->					str:
          super().__init__(
              fo=lowercase_   ,       mode=lowercase_   ,       target_protocol=lowercase_   ,       target_options=lowercase_   ,       block_size=lowercase_   ,       **lowercase_   ,       )
          # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
          #
          # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
          #     out.close = close
          # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
          #
          # see https://github.com/intake/filesystem_spec/issues/725
          UpperCAmelCase									=					self.file.__enter__
          class      _UpperCAmelCase							:




                def __init__(							self   ,       lowercase_							)						->					List[Any]:
                    UpperCAmelCase									=					file_




                def __enter__(							self							)						->					List[str]:
                    self._file.__enter__()
                    return self




                def __exit__(							self   ,       *lowercase_   ,       **lowercase_							)						->					List[str]:
                    self._file.__exit__(*lowercase_   ,       **lowercase_							)




                def __iter__(							self							)						->					Optional[int]:
                    return iter(self._file							)




                def     a_      (							self							)						->					Tuple:
                    return next(self._file							)




                def __getattr__(							self   ,       lowercase_							)						->					Optional[int]:
                    return getattr(self._file   ,       lowercase_							)

          def fixed_enter(*lowercase_   ,       **lowercase_							):
              return WrappedFile(_enter(*lowercase_   ,       **lowercase_							)							)

          UpperCAmelCase									=					fixed_enter



