





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase :    List[Any]  							= logging.get_logger(__name__)

_lowerCamelCase :    int  							= {
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "marian"
							UpperCAmelCase_      						=   ["past_key_values"]
							UpperCAmelCase_      						=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






							def __init__( self     :					int,			_UpperCAmelCase     :					str=5_8_1_0_1,			_UpperCAmelCase     :					Dict=None,			_UpperCAmelCase     :					Optional[Any]=1_0_2_4,			_UpperCAmelCase     :					Dict=1_2,			_UpperCAmelCase     :					str=4_0_9_6,			_UpperCAmelCase     :					List[str]=1_6,			_UpperCAmelCase     :					str=1_2,			_UpperCAmelCase     :					List[str]=4_0_9_6,			_UpperCAmelCase     :					Union[str, Any]=1_6,			_UpperCAmelCase     :					int=0.0,			_UpperCAmelCase     :					int=0.0,			_UpperCAmelCase     :					str=True,			_UpperCAmelCase     :					List[str]=True,			_UpperCAmelCase     :					Optional[Any]="gelu",			_UpperCAmelCase     :					int=1_0_2_4,			_UpperCAmelCase     :					int=0.1,			_UpperCAmelCase     :					Any=0.0,			_UpperCAmelCase     :					str=0.0,			_UpperCAmelCase     :					Optional[Any]=0.02,			_UpperCAmelCase     :					List[str]=5_8_1_0_0,			_UpperCAmelCase     :					int=False,			_UpperCAmelCase     :					List[str]=5_8_1_0_0,			_UpperCAmelCase     :					Any=0,			_UpperCAmelCase     :					int=0,			_UpperCAmelCase     :					Optional[Any]=True,			**_UpperCAmelCase     :					Optional[int],			)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						vocab_size
														SCREAMING_SNAKE_CASE__       :      str     =						decoder_vocab_size or vocab_size
														SCREAMING_SNAKE_CASE__       :      str     =						max_position_embeddings
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						d_model
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						encoder_ffn_dim
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						encoder_layers
														SCREAMING_SNAKE_CASE__       :      Tuple     =						encoder_attention_heads
														SCREAMING_SNAKE_CASE__       :      List[str]     =						decoder_ffn_dim
														SCREAMING_SNAKE_CASE__       :      Tuple     =						decoder_layers
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						decoder_attention_heads
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						dropout
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						attention_dropout
														SCREAMING_SNAKE_CASE__       :      int     =						activation_dropout
														SCREAMING_SNAKE_CASE__       :      List[str]     =						activation_function
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						init_std
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						encoder_layerdrop
														SCREAMING_SNAKE_CASE__       :      str     =						decoder_layerdrop
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						use_cache
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						encoder_layers
														SCREAMING_SNAKE_CASE__       :      str     =						scale_embedding  # scale factor will be sqrt(d_model) if True
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						share_encoder_decoder_embeddings
														super().__init__(
														    pad_token_id=_UpperCAmelCase,			eos_token_id=_UpperCAmelCase,			is_encoder_decoder=_UpperCAmelCase,			decoder_start_token_id=_UpperCAmelCase,			forced_eos_token_id=_UpperCAmelCase,			**_UpperCAmelCase,			)


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							@property
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
							def  A_     ( self     :					Any						)				->			Mapping[str, Mapping[int, str]]:





														"""simple docstring"""
														if self.task in ["default", "seq2seq-lm"]:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						OrderedDict(
																					    [
																					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																					    ]						)

																					if self.use_past:
																												SCREAMING_SNAKE_CASE__       :      int     =						{0: "batch"}
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{0: "batch", 1: "past_decoder_sequence + sequence"}
																					else:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						{0: "batch", 1: "decoder_sequence"}
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{0: "batch", 1: "decoder_sequence"}

																					if self.use_past:
																												self.fill_with_past_key_values_(_UpperCAmelCase,			direction="inputs"						)
														elif self.task == "causal-lm":
																					# TODO: figure this case out.
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						OrderedDict(
																					    [
																					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																					    ]						)
																					if self.use_past:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.num_layers
																												for i in range(_UpperCAmelCase						):
																																			SCREAMING_SNAKE_CASE__       :      List[str]     =						{0: "batch", 2: "past_sequence + sequence"}
																																			SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{0: "batch", 2: "past_sequence + sequence"}
														else:
																					SCREAMING_SNAKE_CASE__       :      Any     =						OrderedDict(
																					    [
																					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																					        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
																					        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
																					    ]						)

														return common_inputs






							@property
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
							def  A_     ( self     :					Tuple						)				->			Mapping[str, Mapping[int, str]]:





														"""simple docstring"""
														if self.task in ["default", "seq2seq-lm"]:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						super().outputs
														else:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						super(_UpperCAmelCase,			self						).outputs
																					if self.use_past:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.num_layers
																												for i in range(_UpperCAmelCase						):
																																			SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{0: "batch", 2: "past_sequence + sequence"}
																																			SCREAMING_SNAKE_CASE__       :      Any     =						{0: "batch", 2: "past_sequence + sequence"}
														return common_outputs






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					PreTrainedTokenizer,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					Optional[TensorType] = None,			)				->			Mapping[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						self._generate_dummy_inputs_for_encoder_and_decoder(
														    _UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)

														# Generate decoder inputs
														SCREAMING_SNAKE_CASE__       :      Any     =						seq_length if not self.use_past else 1
														SCREAMING_SNAKE_CASE__       :      int     =						self._generate_dummy_inputs_for_encoder_and_decoder(
														    _UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
														SCREAMING_SNAKE_CASE__       :      Dict     =						dict(**_UpperCAmelCase,			**_UpperCAmelCase						)

														if self.use_past:
																					if not is_torch_available():
																												raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
																					else:
																												import torch
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						common_inputs["input_ids"].shape
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						common_inputs["decoder_input_ids"].shape[1]
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.num_attention_heads
																					SCREAMING_SNAKE_CASE__       :      str     =						(
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					SCREAMING_SNAKE_CASE__       :      str     =						decoder_seq_length + 3
																					SCREAMING_SNAKE_CASE__       :      str     =						(
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.cat(
																					    [common_inputs["decoder_attention_mask"], torch.ones(_UpperCAmelCase,			_UpperCAmelCase						)],			dim=1						)

																					SCREAMING_SNAKE_CASE__       :      Any     =						[]
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.num_layers
																					SCREAMING_SNAKE_CASE__       :      Any     =						min(_UpperCAmelCase,			_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      Any     =						max(_UpperCAmelCase,			_UpperCAmelCase						) - min_num_layers
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

																					for _ in range(_UpperCAmelCase						):
																												common_inputs["past_key_values"].append(
																												    (
																												        torch.zeros(_UpperCAmelCase						),
																												        torch.zeros(_UpperCAmelCase						),
																												        torch.zeros(_UpperCAmelCase						),
																												        torch.zeros(_UpperCAmelCase						),
																												    )						)
																					# TODO: test this.
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						encoder_shape if remaining_side_name == "encoder" else decoder_shape
																					for _ in range(_UpperCAmelCase,			_UpperCAmelCase						):
																												common_inputs["past_key_values"].append((torch.zeros(_UpperCAmelCase						), torch.zeros(_UpperCAmelCase						))						)
														return common_inputs






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					PreTrainedTokenizer,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					Optional[TensorType] = None,			)				->			Mapping[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self._generate_dummy_inputs_for_encoder_and_decoder(
														    _UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)

														if self.use_past:
																					if not is_torch_available():
																												raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
																					else:
																												import torch
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					SCREAMING_SNAKE_CASE__       :      str     =						seqlen + 2
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						self.num_layers
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.num_attention_heads
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						(
																					    batch,
																					    num_encoder_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)

																					SCREAMING_SNAKE_CASE__       :      Any     =						common_inputs["attention_mask"].dtype
																					SCREAMING_SNAKE_CASE__       :      int     =						torch.cat(
																					    [common_inputs["attention_mask"], torch.ones(_UpperCAmelCase,			_UpperCAmelCase,			dtype=_UpperCAmelCase						)],			dim=1						)
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						[
																					    (torch.zeros(_UpperCAmelCase						), torch.zeros(_UpperCAmelCase						)) for _ in range(_UpperCAmelCase						)
																					]
														return common_inputs






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					PreTrainedTokenizer,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					Optional[TensorType] = None,			)				->			Mapping[str, Any]:





														"""simple docstring"""
														# Copied from OnnxConfig.generate_dummy_inputs
														# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														SCREAMING_SNAKE_CASE__       :      Any     =						compute_effective_axis_dimension(
														    _UpperCAmelCase,			fixed_dimension=OnnxConfig.default_fixed_batch,			num_token_to_add=0						)

														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														SCREAMING_SNAKE_CASE__       :      Dict     =						tokenizer.num_special_tokens_to_add(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						compute_effective_axis_dimension(
														    _UpperCAmelCase,			fixed_dimension=OnnxConfig.default_fixed_sequence,			num_token_to_add=_UpperCAmelCase						)

														# Generate dummy inputs according to compute batch and sequence
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
														SCREAMING_SNAKE_CASE__       :      List[str]     =						dict(tokenizer(_UpperCAmelCase,			return_tensors=_UpperCAmelCase						)						)
														return common_inputs






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					PreTrainedTokenizer,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					Optional[TensorType] = None,			)				->			Mapping[str, Any]:





														"""simple docstring"""
														if self.task in ["default", "seq2seq-lm"]:
																					SCREAMING_SNAKE_CASE__       :      str     =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																					    _UpperCAmelCase,			batch_size=_UpperCAmelCase,			seq_length=_UpperCAmelCase,			is_pair=_UpperCAmelCase,			framework=_UpperCAmelCase						)

														else:
																					SCREAMING_SNAKE_CASE__       :      Any     =						self._generate_dummy_inputs_for_causal_lm(
																					    _UpperCAmelCase,			batch_size=_UpperCAmelCase,			seq_length=_UpperCAmelCase,			is_pair=_UpperCAmelCase,			framework=_UpperCAmelCase						)

														return common_inputs






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					List[Any]						)				->			Optional[int]:





														"""simple docstring"""
														if self.task in ["default", "seq2seq-lm"]:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						super()._flatten_past_key_values_(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)
														else:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						super(_UpperCAmelCase,			self						)._flatten_past_key_values_(
																					    _UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)






							@property
							def  A_     ( self     :					Any						)				->			float:





														"""simple docstring"""
														return 1E-4













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

_lowerCamelCase :    Tuple  							= collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])

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




def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    List[Any]:

							'''simple docstring'''






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




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please use tf.data to implement this functionality."       )
def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    List[str]:

							'''simple docstring'''






							print("Extracting"			,				f.name       )
							with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
														SCREAMING_SNAKE_CASE__       :      int     =						_readaa(SCREAMING_SNAKE_CASE__       )
														if magic != 20_51:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      str     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      str     =						bytestream.read(rows * cols * num_images       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						numpy.frombuffer(SCREAMING_SNAKE_CASE__			,				dtype=numpy.uinta       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						data.reshape(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				1       )
														return data




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please use tf.one_hot on tensors."       )
def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Tuple       )	->    Union[str, Any]:

							'''simple docstring'''






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




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

							'''simple docstring'''






							print("Extracting"			,				f.name       )
							with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_readaa(SCREAMING_SNAKE_CASE__       )
														if magic != 20_49:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)       )
														SCREAMING_SNAKE_CASE__       :      Tuple     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						bytestream.read(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						numpy.frombuffer(SCREAMING_SNAKE_CASE__			,				dtype=numpy.uinta       )
														if one_hot:
																					return _dense_to_one_hot(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
														return labels


class 						lowerCamelCase       :







							"""simple docstring"""




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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						random_seed.get_seed(_UpperCAmelCase						)
														# If op level seed is not set, use whatever graph level seed is returned
														numpy.random.seed(seeda if seed is None else seeda						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						dtypes.as_dtype(_UpperCAmelCase						).base_dtype
														if dtype not in (dtypes.uinta, dtypes.floataa):
																					raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype						)
														if fake_data:
																					SCREAMING_SNAKE_CASE__       :      str     =						1_0_0_0_0
																					SCREAMING_SNAKE_CASE__       :      str     =						one_hot
														else:
																					assert (
																					    images.shape[0] == labels.shape[0]
																					), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																					SCREAMING_SNAKE_CASE__       :      List[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
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						images.reshape(
																												    images.shape[0],			images.shape[1] * images.shape[2]						)
																					if dtype == dtypes.floataa:
																												# Convert from [0, 255] -> [0.0, 1.0].
																												SCREAMING_SNAKE_CASE__       :      Any     =						images.astype(numpy.floataa						)
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						numpy.multiply(_UpperCAmelCase,			1.0 / 255.0						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						images
														SCREAMING_SNAKE_CASE__       :      str     =						labels
														SCREAMING_SNAKE_CASE__       :      Any     =						0
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						0






							@property
							def  A_     ( self     :					List[Any]						)				->			int:





														"""simple docstring"""
														return self._images






							@property
							def  A_     ( self     :					List[str]						)				->			Optional[int]:





														"""simple docstring"""
														return self._labels






							@property
							def  A_     ( self     :					Optional[Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														return self._num_examples






							@property
							def  A_     ( self     :					List[Any]						)				->			Tuple:





														"""simple docstring"""
														return self._epochs_completed






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any]=False,			_UpperCAmelCase     :					str=True						)				->			Any:





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




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

							'''simple docstring'''






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







@deprecated(
    SCREAMING_SNAKE_CASE__			,				"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"       )
def _a (  SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  str=False			,				SCREAMING_SNAKE_CASE__      :  Any=False			,				SCREAMING_SNAKE_CASE__      :  List[Any]=dtypes.floataa			,				SCREAMING_SNAKE_CASE__      :  str=True			,				SCREAMING_SNAKE_CASE__      :  Dict=50_00			,				SCREAMING_SNAKE_CASE__      :  str=None			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=DEFAULT_SOURCE_URL			,				)	->    Any:

							'''simple docstring'''






							if fake_data:

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

														SCREAMING_SNAKE_CASE__       :      int     =						fake()
														SCREAMING_SNAKE_CASE__       :      List[str]     =						fake()
														SCREAMING_SNAKE_CASE__       :      Any     =						fake()
														return _Datasets(train=SCREAMING_SNAKE_CASE__			,				validation=SCREAMING_SNAKE_CASE__			,				test=SCREAMING_SNAKE_CASE__       )

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

							SCREAMING_SNAKE_CASE__       :      Dict     =						"train-images-idx3-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"train-labels-idx1-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"t10k-images-idx3-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      str     =						"t10k-labels-idx1-ubyte.gz"

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + train_images_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      Dict     =						_extract_images(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + train_labels_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						_extract_labels(SCREAMING_SNAKE_CASE__			,				one_hot=SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + test_images_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_extract_images(SCREAMING_SNAKE_CASE__       )

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

							if not 0 <= validation_size <= len(SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						(
														    "Validation size should be between 0 and "
														    f'''{len(SCREAMING_SNAKE_CASE__       )}. Received: {validation_size}.'''
														)
														raise ValueError(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      str     =						train_images[:validation_size]
							SCREAMING_SNAKE_CASE__       :      str     =						train_labels[:validation_size]
							SCREAMING_SNAKE_CASE__       :      Any     =						train_images[validation_size:]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						train_labels[validation_size:]

							SCREAMING_SNAKE_CASE__       :      Any     =						{"dtype": dtype, "reshape": reshape, "seed": seed}

							SCREAMING_SNAKE_CASE__       :      Tuple     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )

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






