
from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


lowercase						:      List[Any]									= [
    'openmmlab/upernet-convnext-tiny',
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
lowercase						:      str									= 'UperNetConfig'




class 				lowerCamelCase__							(       nn.Module):




	'''simple docstring'''




	def __init__(      self	:Tuple  ,     a	:int  ,     a	:int  ,     a	:Union[int, Tuple[int, int]]  ,     a	:Union[int, Tuple[int, int], str] = 0  ,     a	:bool = False  ,     a	:Union[int, Tuple[int, int]] = 1  ,     )	->				None:
								super().__init__()
								__UpperCamelCase			:     List[Any]         =      nn.Convad(
								    in_channels=a  ,     out_channels=a  ,     kernel_size=a  ,     padding=a  ,     bias=a  ,     dilation=a  ,     )
								__UpperCamelCase			:     Any         =      nn.BatchNormad(a		)
								__UpperCamelCase			:     Optional[Any]         =      nn.ReLU()







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:torch.Tensor		)	->				torch.Tensor:
								__UpperCamelCase			:     int         =      self.conv(a		)
								__UpperCamelCase			:     Any         =      self.batch_norm(a		)
								__UpperCamelCase			:     Union[str, Any]         =      self.activation(a		)

								return output




class 				lowerCamelCase__							(       nn.Module):




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:int  ,     a	:int  ,     a	:int		)	->				None:
								super().__init__()
								__UpperCamelCase			:     Optional[int]         =      [
								    nn.AdaptiveAvgPoolad(a		),
								    UperNetConvModule(a  ,     a  ,     kernel_size=1		),
								]
								for i, layer in enumerate(self.layers		):
															self.add_module(str(a		)  ,     a		)







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:torch.Tensor		)	->				torch.Tensor:
								__UpperCamelCase			:     Tuple         =      input
								for layer in self.layers:
															__UpperCamelCase			:     Dict         =      layer(a		)
								return hidden_state




class 				lowerCamelCase__							(       nn.Module):




	'''simple docstring'''




	def __init__(      self	:Dict  ,     a	:Tuple[int, ...]  ,     a	:int  ,     a	:int  ,     a	:bool		)	->				None:
								super().__init__()
								__UpperCamelCase			:     Optional[int]         =      pool_scales
								__UpperCamelCase			:     Optional[Any]         =      align_corners
								__UpperCamelCase			:     List[str]         =      in_channels
								__UpperCamelCase			:     Union[str, Any]         =      channels
								__UpperCamelCase			:     Union[str, Any]         =      []
								for i, pool_scale in enumerate(a		):
															__UpperCamelCase			:     str         =      UperNetPyramidPoolingBlock(pool_scale=a  ,     in_channels=a  ,     channels=a		)
															self.blocks.append(a		)
															self.add_module(str(a		)  ,     a		)







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:torch.Tensor		)	->				List[torch.Tensor]:
								__UpperCamelCase			:     Union[str, Any]         =      []
								for ppm in self.blocks:
															__UpperCamelCase			:     Union[str, Any]         =      ppm(a		)
															__UpperCamelCase			:     str         =      nn.functional.interpolate(
															    a  ,     size=x.size()[2:]  ,     mode="bilinear"  ,     align_corners=self.align_corners		)
															ppm_outs.append(a		)
								return ppm_outs




class 				lowerCamelCase__							(       nn.Module):




	'''simple docstring'''




	def __init__(      self	:List[Any]  ,     a	:Any  ,     a	:Optional[Any]		)	->				int:
								super().__init__()

								__UpperCamelCase			:     Optional[int]         =      config
								__UpperCamelCase			:     str         =      config.pool_scales  # e.g. (1, 2, 3, 6)
								__UpperCamelCase			:     Optional[int]         =      in_channels
								__UpperCamelCase			:     List[str]         =      config.hidden_size
								__UpperCamelCase			:     int         =      False
								__UpperCamelCase			:     str         =      nn.Convad(self.channels  ,     config.num_labels  ,     kernel_size=1		)

								# PSP Module
								__UpperCamelCase			:     List[Any]         =      UperNetPyramidPoolingModule(
								    self.pool_scales  ,     self.in_channels[-1]  ,     self.channels  ,     align_corners=self.align_corners  ,     )
								__UpperCamelCase			:     Tuple         =      UperNetConvModule(
								    self.in_channels[-1] + len(self.pool_scales		) * self.channels  ,     self.channels  ,     kernel_size=3  ,     padding=1  ,     )
								# FPN Module
								__UpperCamelCase			:     Any         =      nn.ModuleList()
								__UpperCamelCase			:     Union[str, Any]         =      nn.ModuleList()
								for in_channels in self.in_channels[:-1]:  # skip the top layer
															__UpperCamelCase			:     Tuple         =      UperNetConvModule(a  ,     self.channels  ,     kernel_size=1		)
															__UpperCamelCase			:     List[Any]         =      UperNetConvModule(self.channels  ,     self.channels  ,     kernel_size=3  ,     padding=1		)
															self.lateral_convs.append(a		)
															self.fpn_convs.append(a		)

								__UpperCamelCase			:     Union[str, Any]         =      UperNetConvModule(
								    len(self.in_channels		) * self.channels  ,     self.channels  ,     kernel_size=3  ,     padding=1  ,     )







	def      _lowerCamelCase       (      self	:Tuple		)	->				List[str]:
								self.apply(self._init_weights		)







	def      _lowerCamelCase       (      self	:str  ,     a	:int		)	->				Tuple:
								if isinstance(a  ,     nn.Convad		):
															module.weight.data.normal_(mean=0.0  ,     std=self.config.initializer_range		)
															if module.bias is not None:
																						module.bias.data.zero_()







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:List[str]		)	->				int:
								__UpperCamelCase			:     Optional[int]         =      inputs[-1]
								__UpperCamelCase			:     Tuple         =      [x]
								psp_outs.extend(self.psp_modules(a		)		)
								__UpperCamelCase			:     List[str]         =      torch.cat(a  ,     dim=1		)
								__UpperCamelCase			:     Any         =      self.bottleneck(a		)

								return output







	def      _lowerCamelCase       (      self	:List[Any]  ,     a	:torch.Tensor		)	->				torch.Tensor:
								# build laterals
								__UpperCamelCase			:     List[str]         =      [lateral_conv(encoder_hidden_states[i]		) for i, lateral_conv in enumerate(self.lateral_convs		)]

								laterals.append(self.psp_forward(a		)		)

								# build top-down path
								__UpperCamelCase			:     str         =      len(a		)
								for i in range(used_backbone_levels - 1  ,     0  ,     -1		):
															__UpperCamelCase			:     str         =      laterals[i - 1].shape[2:]
															__UpperCamelCase			:     int         =      laterals[i - 1] + nn.functional.interpolate(
															    laterals[i]  ,     size=a  ,     mode="bilinear"  ,     align_corners=self.align_corners		)

								# build outputs
								__UpperCamelCase			:     int         =      [self.fpn_convs[i](laterals[i]		) for i in range(used_backbone_levels - 1		)]
								# append psp feature
								fpn_outs.append(laterals[-1]		)

								for i in range(used_backbone_levels - 1  ,     0  ,     -1		):
															__UpperCamelCase			:     Tuple         =      nn.functional.interpolate(
															    fpn_outs[i]  ,     size=fpn_outs[0].shape[2:]  ,     mode="bilinear"  ,     align_corners=self.align_corners		)
								__UpperCamelCase			:     Any         =      torch.cat(a  ,     dim=1		)
								__UpperCamelCase			:     int         =      self.fpn_bottleneck(a		)
								__UpperCamelCase			:     int         =      self.classifier(a		)

								return output




class 				lowerCamelCase__							(       nn.Module):




	'''simple docstring'''




	def __init__(      self	:Any  ,     a	:Any  ,     a	:int = 2  ,     a	:int = 3  ,     a	:Union[int, Tuple[int, int]] = 1		)	->				None:
								super().__init__()

								__UpperCamelCase			:     int         =      config
								__UpperCamelCase			:     Optional[Any]         =      config.auxiliary_in_channels
								__UpperCamelCase			:     Union[str, Any]         =      config.auxiliary_channels
								__UpperCamelCase			:     Optional[int]         =      config.auxiliary_num_convs
								__UpperCamelCase			:     Optional[Any]         =      config.auxiliary_concat_input
								__UpperCamelCase			:     str         =      in_index

								__UpperCamelCase			:     List[Any]         =      (kernel_size // 2) * dilation
								__UpperCamelCase			:     Any         =      []
								convs.append(
								    UperNetConvModule(
								        self.in_channels  ,     self.channels  ,     kernel_size=a  ,     padding=a  ,     dilation=a		)		)
								for i in range(self.num_convs - 1		):
															convs.append(
															    UperNetConvModule(
															        self.channels  ,     self.channels  ,     kernel_size=a  ,     padding=a  ,     dilation=a		)		)
								if self.num_convs == 0:
															__UpperCamelCase			:     Union[str, Any]         =      nn.Identity()
								else:
															__UpperCamelCase			:     Union[str, Any]         =      nn.Sequential(*a		)
								if self.concat_input:
															__UpperCamelCase			:     Any         =      UperNetConvModule(
															    self.in_channels + self.channels  ,     self.channels  ,     kernel_size=a  ,     padding=kernel_size // 2		)

								__UpperCamelCase			:     Tuple         =      nn.Convad(self.channels  ,     config.num_labels  ,     kernel_size=1		)







	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								self.apply(self._init_weights		)







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:Dict		)	->				List[str]:
								if isinstance(a  ,     nn.Convad		):
															module.weight.data.normal_(mean=0.0  ,     std=self.config.initializer_range		)
															if module.bias is not None:
																						module.bias.data.zero_()







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:torch.Tensor		)	->				torch.Tensor:
								# just take the relevant feature maps
								__UpperCamelCase			:     str         =      encoder_hidden_states[self.in_index]
								__UpperCamelCase			:     Optional[int]         =      self.convs(a		)
								if self.concat_input:
															__UpperCamelCase			:     int         =      self.conv_cat(torch.cat([hidden_states, output]  ,     dim=1		)		)
								__UpperCamelCase			:     str         =      self.classifier(a		)
								return output




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  UperNetConfig
	_A			=  'pixel_values'
	_A			=  True







	def      _lowerCamelCase       (      self	:Tuple  ,     a	:List[Any]		)	->				Optional[Any]:
								if isinstance(a  ,     a		):
															module.backbone.init_weights()
															module.decode_head.init_weights()
															module.auxiliary_head.init_weights()







	def      _lowerCamelCase       (      self	:Dict		)	->				Any:
								self.backbone.init_weights()
								self.decode_head.init_weights()
								self.auxiliary_head.init_weights()







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:List[Any]  ,     a	:Optional[Any]=False		)	->				Union[str, Any]:
								if isinstance(a  ,     a		):
															__UpperCamelCase			:     Tuple         =      value
lowercase						:      Dict									= R'\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowercase						:      Dict									= R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'




@add_start_docstrings(
    'UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'  ,		__lowercase  ,		)
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:int  ,     a	:Tuple		)	->				List[str]:
								super().__init__(a		)

								__UpperCamelCase			:     Union[str, Any]         =      AutoBackbone.from_config(config.backbone_config		)

								# Semantic segmentation head(s)
								__UpperCamelCase			:     int         =      UperNetHead(a  ,     in_channels=self.backbone.channels		)
								__UpperCamelCase			:     Any         =      UperNetFCNHead(a		) if config.use_auxiliary_head else None

								# Initialize weights and apply final processing
								self.post_init()







	@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"		)		)
	@replace_return_docstrings(output_type=a  ,     config_class=_CONFIG_FOR_DOC		)
	def      _lowerCamelCase       (      self	:Any  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[bool] = None  ,     a	:Optional[bool] = None  ,     a	:Optional[torch.Tensor] = None  ,     a	:Optional[bool] = None  ,     )	->				Union[tuple, SemanticSegmenterOutput]:
								__UpperCamelCase			:     Union[str, Any]         =      return_dict if return_dict is not None else self.config.use_return_dict
								__UpperCamelCase			:     Tuple         =      (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__UpperCamelCase			:     Union[str, Any]         =      output_attentions if output_attentions is not None else self.config.output_attentions

								__UpperCamelCase			:     int         =      self.backbone.forward_with_filtered_kwargs(
								    a  ,     output_hidden_states=a  ,     output_attentions=a		)
								__UpperCamelCase			:     Optional[Any]         =      outputs.feature_maps

								__UpperCamelCase			:     str         =      self.decode_head(a		)
								__UpperCamelCase			:     List[str]         =      nn.functional.interpolate(a  ,     size=pixel_values.shape[2:]  ,     mode="bilinear"  ,     align_corners=a		)

								__UpperCamelCase			:     Union[str, Any]         =      None
								if self.auxiliary_head is not None:
															__UpperCamelCase			:     Optional[Any]         =      self.auxiliary_head(a		)
															__UpperCamelCase			:     List[str]         =      nn.functional.interpolate(
															    a  ,     size=pixel_values.shape[2:]  ,     mode="bilinear"  ,     align_corners=a		)

								__UpperCamelCase			:     int         =      None
								if labels is not None:
															if self.config.num_labels == 1:
																						raise ValueError("The number of labels should be greater than one"		)
															else:
																						# compute weighted loss
																						__UpperCamelCase			:     Any         =      CrossEntropyLoss(ignore_index=self.config.loss_ignore_index		)
																						__UpperCamelCase			:     Optional[Any]         =      loss_fct(a  ,     a		)
																						__UpperCamelCase			:     Optional[int]         =      loss_fct(a  ,     a		)
																						__UpperCamelCase			:     Tuple         =      main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

								if not return_dict:
															if output_hidden_states:
																						__UpperCamelCase			:     Tuple         =      (logits,) + outputs[1:]
															else:
																						__UpperCamelCase			:     Tuple         =      (logits,) + outputs[2:]
															return ((loss,) + output) if loss is not None else output

								return SemanticSegmenterOutput(
								    loss=a  ,     logits=a  ,     hidden_states=outputs.hidden_states  ,     attentions=outputs.attentions  ,     )

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

lowercase						:      List[str]									= collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      numpy.dtype(numpy.uintaa).newbyteorder(">")
							return numpy.frombuffer(bytestream.read(4)		,						dtype=_lowerCamelCase)[0]




@deprecated(_lowerCamelCase		,						"Please use tf.data to implement this functionality.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Any:






							'''simple docstring'''





							print("Extracting"		,						f.name)
							with gzip.GzipFile(fileobj=_lowerCamelCase) as bytestream:
														__UpperCamelCase			:     str         =      _readaa(_lowerCamelCase)
														if magic != 2_051:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name))
														__UpperCamelCase			:     List[str]         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     Optional[int]         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      bytestream.read(rows * cols * num_images)
														__UpperCamelCase			:     Optional[int]         =      numpy.frombuffer(_lowerCamelCase		,						dtype=numpy.uinta)
														__UpperCamelCase			:     Dict         =      data.reshape(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						1)
														return data




@deprecated(_lowerCamelCase		,						"Please use tf.one_hot on tensors.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    List[str])      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      labels_dense.shape[0]
							__UpperCamelCase			:     str         =      numpy.arange(_lowerCamelCase) * num_classes
							__UpperCamelCase			:     str         =      numpy.zeros((num_labels, num_classes))
							__UpperCamelCase			:     Tuple         =      1
							return labels_one_hot




@deprecated(_lowerCamelCase		,						"Please use tf.data to implement this functionality.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Optional[int]=False		,						_lowerCamelCase				:    Optional[int]=10)      ->					Dict:






							'''simple docstring'''





							print("Extracting"		,						f.name)
							with gzip.GzipFile(fileobj=_lowerCamelCase) as bytestream:
														__UpperCamelCase			:     int         =      _readaa(_lowerCamelCase)
														if magic != 2_049:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name))
														__UpperCamelCase			:     Any         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     List[Any]         =      bytestream.read(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      numpy.frombuffer(_lowerCamelCase		,						dtype=numpy.uinta)
														if one_hot:
																					return _dense_to_one_hot(_lowerCamelCase		,						_lowerCamelCase)
														return labels




class 				lowerCamelCase__							:




	'''simple docstring'''




	@deprecated(
	    a  ,     "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models."  ,     )
	def __init__(      self	:int  ,     a	:Any  ,     a	:List[str]  ,     a	:Union[str, Any]=False  ,     a	:List[Any]=False  ,     a	:Dict=dtypes.floataa  ,     a	:int=True  ,     a	:Optional[int]=None  ,     )	->				List[str]:
								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      random_seed.get_seed(a		)
								# If op level seed is not set, use whatever graph level seed is returned
								numpy.random.seed(seeda if seed is None else seeda		)
								__UpperCamelCase			:     Optional[Any]         =      dtypes.as_dtype(a		).base_dtype
								if dtype not in (dtypes.uinta, dtypes.floataa):
															raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype		)
								if fake_data:
															__UpperCamelCase			:     str         =      1_0_0_0_0
															__UpperCamelCase			:     Optional[Any]         =      one_hot
								else:
															assert (
															    images.shape[0] == labels.shape[0]
															), f'images.shape: {images.shape} labels.shape: {labels.shape}'
															__UpperCamelCase			:     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
																						__UpperCamelCase			:     Union[str, Any]         =      images.reshape(
																						    images.shape[0]  ,     images.shape[1] * images.shape[2]		)
															if dtype == dtypes.floataa:
																						# Convert from [0, 255] -> [0.0, 1.0].
																						__UpperCamelCase			:     List[Any]         =      images.astype(numpy.floataa		)
																						__UpperCamelCase			:     Optional[Any]         =      numpy.multiply(a  ,     1.0 / 255.0		)
								__UpperCamelCase			:     Optional[Any]         =      images
								__UpperCamelCase			:     List[Any]         =      labels
								__UpperCamelCase			:     str         =      0
								__UpperCamelCase			:     Union[str, Any]         =      0







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				Any:
								return self._images







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				Dict:
								return self._labels







	@property
	def      _lowerCamelCase       (      self	:List[str]		)	->				str:
								return self._num_examples







	@property
	def      _lowerCamelCase       (      self	:Tuple		)	->				Dict:
								return self._epochs_completed







	def      _lowerCamelCase       (      self	:Any  ,     a	:Optional[int]  ,     a	:Optional[int]=False  ,     a	:int=True		)	->				Optional[int]:
								if fake_data:
															__UpperCamelCase			:     Any         =      [1] * 7_8_4
															__UpperCamelCase			:     Union[str, Any]         =      [1] + [0] * 9 if self.one_hot else 0
															return (
															    [fake_image for _ in range(a		)],
															    [fake_label for _ in range(a		)],
															)
								__UpperCamelCase			:     str         =      self._index_in_epoch
								# Shuffle for the first epoch
								if self._epochs_completed == 0 and start == 0 and shuffle:
															__UpperCamelCase			:     Any         =      numpy.arange(self._num_examples		)
															numpy.random.shuffle(a		)
															__UpperCamelCase			:     int         =      self.images[perma]
															__UpperCamelCase			:     str         =      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
															__UpperCamelCase			:     Optional[int]         =      self._num_examples - start
															__UpperCamelCase			:     Optional[int]         =      self._images[start : self._num_examples]
															__UpperCamelCase			:     int         =      self._labels[start : self._num_examples]
															# Shuffle the data
															if shuffle:
																						__UpperCamelCase			:     Optional[int]         =      numpy.arange(self._num_examples		)
																						numpy.random.shuffle(a		)
																						__UpperCamelCase			:     Optional[Any]         =      self.images[perm]
																						__UpperCamelCase			:     Tuple         =      self.labels[perm]
															# Start next epoch
															__UpperCamelCase			:     Tuple         =      0
															__UpperCamelCase			:     Union[str, Any]         =      batch_size - rest_num_examples
															__UpperCamelCase			:     List[str]         =      self._index_in_epoch
															__UpperCamelCase			:     Dict         =      self._images[start:end]
															__UpperCamelCase			:     str         =      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
															__UpperCamelCase			:     Union[str, Any]         =      self._index_in_epoch
															return self._images[start:end], self._labels[start:end]




@deprecated(_lowerCamelCase		,						"Please write your own downloading logic.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Union[str, Any])      ->					Tuple:






							'''simple docstring'''





							if not gfile.Exists(_lowerCamelCase):
														gfile.MakeDirs(_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      os.path.join(_lowerCamelCase		,						_lowerCamelCase)
							if not gfile.Exists(_lowerCamelCase):
														urllib.request.urlretrieve(_lowerCamelCase		,						_lowerCamelCase)  # noqa: S310
														with gfile.GFile(_lowerCamelCase) as f:
																					__UpperCamelCase			:     Any         =      f.size()
														print("Successfully downloaded"		,						_lowerCamelCase		,						_lowerCamelCase		,						"bytes.")
							return filepath




@deprecated(
    _lowerCamelCase		,						"Please use alternatives such as:" " tensorflow_datasets.load('mnist')")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[Any]=False		,						_lowerCamelCase				:    str=False		,						_lowerCamelCase				:    List[str]=dtypes.floataa		,						_lowerCamelCase				:    Any=True		,						_lowerCamelCase				:    Union[str, Any]=5_000		,						_lowerCamelCase				:    str=None		,						_lowerCamelCase				:    Optional[int]=DEFAULT_SOURCE_URL		,						)      ->					List[Any]:






							'''simple docstring'''





							if fake_data:

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

														__UpperCamelCase			:     Optional[int]         =      fake()
														__UpperCamelCase			:     Tuple         =      fake()
														__UpperCamelCase			:     List[str]         =      fake()
														return _Datasets(train=_lowerCamelCase		,						validation=_lowerCamelCase		,						test=_lowerCamelCase)

							if not source_url:  # empty string check
														__UpperCamelCase			:     str         =      DEFAULT_SOURCE_URL

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

							__UpperCamelCase			:     Optional[int]         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + train_images_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     int         =      _extract_images(_lowerCamelCase)

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

							__UpperCamelCase			:     int         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + test_images_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     Optional[int]         =      _extract_images(_lowerCamelCase)

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

							if not 0 <= validation_size <= len(_lowerCamelCase):
														__UpperCamelCase			:     str         =      (
														    "Validation size should be between 0 and "
														    F'{len(_lowerCamelCase)}. Received: {validation_size}.'
														)
														raise ValueError(_lowerCamelCase)

							__UpperCamelCase			:     Any         =      train_images[:validation_size]
							__UpperCamelCase			:     Optional[Any]         =      train_labels[:validation_size]
							__UpperCamelCase			:     Optional[int]         =      train_images[validation_size:]
							__UpperCamelCase			:     Tuple         =      train_labels[validation_size:]

							__UpperCamelCase			:     List[str]         =      {"dtype": dtype, "reshape": reshape, "seed": seed}

							__UpperCamelCase			:     Union[str, Any]         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)
							__UpperCamelCase			:     str         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)

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