




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 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						List[Any]=sys.maxsize		):
       SCREAMING_SNAKE_CASE_      		=     'bilinear'
       SCREAMING_SNAKE_CASE_      		=     max_size
       SCREAMING_SNAKE_CASE_      		=     short_edge_length




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

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

       return img_augs





class 			lowerCamelCase_				:


 '''simple docstring'''







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



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     tuple(max(_lowerCAmelCase		) for s in zip(*[img.shape for img in images]		)		)
       SCREAMING_SNAKE_CASE_      		=     [im.shape[-2:] for im in images]
       SCREAMING_SNAKE_CASE_      		=     [
           nn.functional.pad(
               _lowerCAmelCase							,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]							,							value=self.pad_value							,							)
           for size, im in zip(_lowerCAmelCase							,							_lowerCAmelCase		)
       ]

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




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

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


def    UpperCAmelCase_				( __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      Any				)   ->					Union[str, Any]:
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes





def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      Tuple[int, int]				)   ->					Any:
      assert torch.isfinite(__UpperCAmelCase				).all(), "Box tensor contains infinite or NaN!"
      SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     box_size
      tensor[:, 0].clamp_(min=0     ,     max=__UpperCAmelCase				)
      tensor[:, 1].clamp_(min=0     ,     max=__UpperCAmelCase				)
      tensor[:, 2].clamp_(min=0     ,     max=__UpperCAmelCase				)
      tensor[:, 3].clamp_(min=0     ,     max=__UpperCAmelCase				)




"""simple docstring"""






import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

__snake_case				:  Union[str, Any] =       collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__snake_case				:  Any =       'https://storage.googleapis.com/cvdf-datasets/mnist/'


def      _lowercase							(    __snake_case      )		->  List[Any]:
			__lowerCAmelCase :    int				=			numpy.dtype(numpy.uintaa      ).newbyteorder(">"      )
			return numpy.frombuffer(bytestream.read(4      )   ,dtype=__snake_case      )[0]


@deprecated(__snake_case   ,"Please use tf.data to implement this functionality."      )
def      _lowercase							(    __snake_case      )		->  Union[str, Any]:
			print("Extracting"   ,f.name      )
			with gzip.GzipFile(fileobj=__snake_case      ) as bytestream:
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						if magic != 2_051:
									raise ValueError(
									    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)      )
						__lowerCAmelCase :    List[str]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    int				=			_readaa(__snake_case      )
						__lowerCAmelCase :    int				=			bytestream.read(rows * cols * num_images      )
						__lowerCAmelCase :    Optional[Any]				=			numpy.frombuffer(__snake_case   ,dtype=numpy.uinta      )
						__lowerCAmelCase :    str				=			data.reshape(__snake_case   ,__snake_case   ,__snake_case   ,1      )
						return data


@deprecated(__snake_case   ,"Please use tf.one_hot on tensors."      )
def      _lowercase							(    __snake_case   ,__snake_case      )		->  Any:
			__lowerCAmelCase :    Union[str, Any]				=			labels_dense.shape[0]
			__lowerCAmelCase :    Optional[int]				=			numpy.arange(__snake_case      ) * num_classes
			__lowerCAmelCase :    int				=			numpy.zeros((num_labels, num_classes)      )
			__lowerCAmelCase :    str				=			1
			return labels_one_hot


@deprecated(__snake_case   ,"Please use tf.data to implement this functionality."      )
def      _lowercase							(    __snake_case   ,__snake_case=False   ,__snake_case=10      )		->  str:
			print("Extracting"   ,f.name      )
			with gzip.GzipFile(fileobj=__snake_case      ) as bytestream:
						__lowerCAmelCase :    List[str]				=			_readaa(__snake_case      )
						if magic != 2_049:
									raise ValueError(
									    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)      )
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    Union[str, Any]				=			bytestream.read(__snake_case      )
						__lowerCAmelCase :    Dict				=			numpy.frombuffer(__snake_case   ,dtype=numpy.uinta      )
						if one_hot:
									return _dense_to_one_hot(__snake_case   ,__snake_case      )
						return labels



class      A__  :




	'''simple docstring'''





	@deprecated(
	    _SCREAMING_SNAKE_CASE			,     "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models."			,     )
	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Any=False			,     _SCREAMING_SNAKE_CASE:						int=False			,     _SCREAMING_SNAKE_CASE:						str=dtypes.floataa			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     )							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			random_seed.get_seed(_SCREAMING_SNAKE_CASE)
				# If op level seed is not set, use whatever graph level seed is returned
				numpy.random.seed(seeda if seed is None else seeda)
				__lowerCAmelCase :    Optional[Any]				=			dtypes.as_dtype(_SCREAMING_SNAKE_CASE).base_dtype
				if dtype not in (dtypes.uinta, dtypes.floataa):
							raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
				if fake_data:
							__lowerCAmelCase :    Tuple				=			1_0000
							__lowerCAmelCase :    Optional[Any]				=			one_hot
				else:
							assert (
							    images.shape[0] == labels.shape[0]
							), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
							__lowerCAmelCase :    Union[str, Any]				=			images.shape[0]

							# Convert shape from [num examples, rows, columns, depth]
							# to [num examples, rows*columns] (assuming depth == 1)
							if reshape:
										assert images.shape[3] == 1
										__lowerCAmelCase :    List[str]				=			images.reshape(
										    images.shape[0]			,     images.shape[1] * images.shape[2])
							if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										__lowerCAmelCase :    Dict				=			images.astype(numpy.floataa)
										__lowerCAmelCase :    int				=			numpy.multiply(_SCREAMING_SNAKE_CASE			,     1.0 / 255.0)
				__lowerCAmelCase :    Optional[Any]				=			images
				__lowerCAmelCase :    int				=			labels
				__lowerCAmelCase :    Optional[Any]				=			0
				__lowerCAmelCase :    Optional[int]				=			0

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Tuple:

				"""simple docstring"""

				return self._images

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			int:

				"""simple docstring"""

				return self._labels

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			str:

				"""simple docstring"""

				return self._num_examples

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			List[str]:

				"""simple docstring"""

				return self._epochs_completed







	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						int=False			,     _SCREAMING_SNAKE_CASE:						List[str]=True)							->			int:

				"""simple docstring"""

				if fake_data:
							__lowerCAmelCase :    Dict				=			[1] * 784
							__lowerCAmelCase :    str				=			[1] + [0] * 9 if self.one_hot else 0
							return (
							    [fake_image for _ in range(_SCREAMING_SNAKE_CASE)],
							    [fake_label for _ in range(_SCREAMING_SNAKE_CASE)],
							)
				__lowerCAmelCase :    Tuple				=			self._index_in_epoch
				# Shuffle for the first epoch
				if self._epochs_completed == 0 and start == 0 and shuffle:
							__lowerCAmelCase :    Any				=			numpy.arange(self._num_examples)
							numpy.random.shuffle(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    Union[str, Any]				=			self.images[perma]
							__lowerCAmelCase :    Dict				=			self.labels[perma]
				# Go to the next epoch
				if start + batch_size > self._num_examples:
							# Finished epoch
							self._epochs_completed += 1
							# Get the rest examples in this epoch
							__lowerCAmelCase :    Tuple				=			self._num_examples - start
							__lowerCAmelCase :    List[str]				=			self._images[start : self._num_examples]
							__lowerCAmelCase :    Tuple				=			self._labels[start : self._num_examples]
							# Shuffle the data
							if shuffle:
										__lowerCAmelCase :    Tuple				=			numpy.arange(self._num_examples)
										numpy.random.shuffle(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase :    Optional[Any]				=			self.images[perm]
										__lowerCAmelCase :    Dict				=			self.labels[perm]
							# Start next epoch
							__lowerCAmelCase :    str				=			0
							__lowerCAmelCase :    Dict				=			batch_size - rest_num_examples
							__lowerCAmelCase :    str				=			self._index_in_epoch
							__lowerCAmelCase :    Optional[Any]				=			self._images[start:end]
							__lowerCAmelCase :    Optional[Any]				=			self._labels[start:end]
							return (
							    numpy.concatenate((images_rest_part, images_new_part)			,     axis=0),
							    numpy.concatenate((labels_rest_part, labels_new_part)			,     axis=0),
							)
				else:
							self._index_in_epoch += batch_size
							__lowerCAmelCase :    int				=			self._index_in_epoch
							return self._images[start:end], self._labels[start:end]






@deprecated(__snake_case   ,"Please write your own downloading logic."      )
def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			if not gfile.Exists(__snake_case      ):
						gfile.MakeDirs(__snake_case      )
			__lowerCAmelCase :    Tuple				=			os.path.join(__snake_case   ,__snake_case      )
			if not gfile.Exists(__snake_case      ):
						urllib.request.urlretrieve(__snake_case   ,__snake_case      )  # noqa: S310
						with gfile.GFile(__snake_case      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			f.size()
						print("Successfully downloaded"   ,__snake_case   ,__snake_case   ,"bytes."      )
			return filepath





@deprecated(
    __snake_case   ,"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"      )
def      _lowercase							(    __snake_case   ,__snake_case=False   ,__snake_case=False   ,__snake_case=dtypes.floataa   ,__snake_case=True   ,__snake_case=5_000   ,__snake_case=None   ,__snake_case=DEFAULT_SOURCE_URL   ,)		->  Tuple:
			if fake_data:

						def fake():
									return _DataSet(
									    []   ,[]   ,fake_data=__snake_case   ,one_hot=__snake_case   ,dtype=__snake_case   ,seed=__snake_case      )

						__lowerCAmelCase :    Union[str, Any]				=			fake()
						__lowerCAmelCase :    Optional[Any]				=			fake()
						__lowerCAmelCase :    List[Any]				=			fake()
						return _Datasets(train=__snake_case   ,validation=__snake_case   ,test=__snake_case      )

			if not source_url:  # empty string check
						__lowerCAmelCase :    Optional[Any]				=			DEFAULT_SOURCE_URL

			__lowerCAmelCase :    Dict				=			"train-images-idx3-ubyte.gz"
			__lowerCAmelCase :    int				=			"train-labels-idx1-ubyte.gz"
			__lowerCAmelCase :    List[str]				=			"t10k-images-idx3-ubyte.gz"
			__lowerCAmelCase :    Any				=			"t10k-labels-idx1-ubyte.gz"

			__lowerCAmelCase :    Any				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + train_images_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			_extract_images(__snake_case      )

			__lowerCAmelCase :    Optional[int]				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + train_labels_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    Optional[int]				=			_extract_labels(__snake_case   ,one_hot=__snake_case      )

			__lowerCAmelCase :    Optional[int]				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + test_images_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    List[Any]				=			_extract_images(__snake_case      )

			__lowerCAmelCase :    str				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + test_labels_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    List[Any]				=			_extract_labels(__snake_case   ,one_hot=__snake_case      )

			if not 0 <= validation_size <= len(__snake_case      ):
						__lowerCAmelCase :    Tuple				=			(
						    "Validation size should be between 0 and "
						    F"""{len(__snake_case      )}. Received: {validation_size}."""
						)
						raise ValueError(__snake_case      )

			__lowerCAmelCase :    Any				=			train_images[:validation_size]
			__lowerCAmelCase :    Any				=			train_labels[:validation_size]
			__lowerCAmelCase :    List[str]				=			train_images[validation_size:]
			__lowerCAmelCase :    Optional[Any]				=			train_labels[validation_size:]

			__lowerCAmelCase :    Dict				=			{"dtype": dtype, "reshape": reshape, "seed": seed}

			__lowerCAmelCase :    str				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )
			__lowerCAmelCase :    Dict				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )
			__lowerCAmelCase :    Union[str, Any]				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )

			return _Datasets(train=__snake_case   ,validation=__snake_case   ,test=__snake_case      )