
"""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

lowercase_       :						Dict     					=	collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])

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





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				List[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    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Dict:
  print("Extracting",						f.name				)
  with gzip.GzipFile(fileobj=lowerCamelCase__				) as bytestream:
    _SCREAMING_SNAKE_CASE   :				Any							    = _readaa(lowerCamelCase__				)
    if magic != 2_0_5_1:
      raise ValueError(
          "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)				)
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = _readaa(lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = _readaa(lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = _readaa(lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				Any							    = bytestream.read(rows * cols * num_images				)
    _SCREAMING_SNAKE_CASE   :				Any							    = numpy.frombuffer(lowerCamelCase__,						dtype=numpy.uinta				)
    _SCREAMING_SNAKE_CASE   :				Any							    = data.reshape(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						1				)
    return data





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





@deprecated(lowerCamelCase__,						"Please use tf.data to implement this functionality."				)
def    _lowerCAmelCase    (     lowerCamelCase__							:							int,						lowerCamelCase__							:							Union[str, Any]=False,						lowerCamelCase__							:							Optional[Any]=1_0				)  ->    Dict:
  print("Extracting",						f.name				)
  with gzip.GzipFile(fileobj=lowerCamelCase__				) as bytestream:
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = _readaa(lowerCamelCase__				)
    if magic != 2_0_4_9:
      raise ValueError(
          "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)				)
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = _readaa(lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = bytestream.read(lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				Dict							    = numpy.frombuffer(lowerCamelCase__,						dtype=numpy.uinta				)
    if one_hot:
      return _dense_to_one_hot(lowerCamelCase__,						lowerCamelCase__				)
    return labels
class 			UpperCamelCase      :
     @deprecated(
         snake_case__  ,     "Please use alternatives such as official/mnist/_DataSet.py"
         " from tensorflow/models."  ,     )
     def __init__(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__=False  ,     snake_case__=False  ,     snake_case__=dtypes.floataa  ,     snake_case__=True  ,     snake_case__=None  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = random_seed.get_seed(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				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = dtypes.as_dtype(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:
         _SCREAMING_SNAKE_CASE   :				str							    = 10000
         _SCREAMING_SNAKE_CASE   :				List[str]							    = one_hot
       else:
         assert (
             images.shape[0] == labels.shape[0]
         ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
         _SCREAMING_SNAKE_CASE   :				Tuple							    = 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   :				Dict							    = 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   :				Union[str, Any]							    = images.astype(numpy.floataa				)
           _SCREAMING_SNAKE_CASE   :				int							    = numpy.multiply(snake_case__  ,     1.0 / 255.0				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = images
       _SCREAMING_SNAKE_CASE   :				Any							    = labels
       _SCREAMING_SNAKE_CASE   :				Any							    = 0
       _SCREAMING_SNAKE_CASE   :				Any							    = 0
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self._images
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self._labels
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self._num_examples
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self._epochs_completed




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__=False  ,     snake_case__=True				):




       """simple docstring"""


       if fake_data:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = [1] * 784
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [1] + [0] * 9 if self.one_hot else 0
         return (
             [fake_image for _ in range(snake_case__				)],
             [fake_label for _ in range(snake_case__				)],
         )
       _SCREAMING_SNAKE_CASE   :				Dict							    = self._index_in_epoch
       # Shuffle for the first epoch
       if self._epochs_completed == 0 and start == 0 and shuffle:
         _SCREAMING_SNAKE_CASE   :				Dict							    = numpy.arange(self._num_examples				)
         numpy.random.shuffle(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Dict							    = self.images[perma]
         _SCREAMING_SNAKE_CASE   :				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   :				Any							    = self._num_examples - start
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self._images[start : self._num_examples]
         _SCREAMING_SNAKE_CASE   :				List[str]							    = self._labels[start : self._num_examples]
         # Shuffle the data
         if shuffle:
           _SCREAMING_SNAKE_CASE   :				Any							    = numpy.arange(self._num_examples				)
           numpy.random.shuffle(snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.images[perm]
           _SCREAMING_SNAKE_CASE   :				List[str]							    = self.labels[perm]
         # Start next epoch
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = 0
         _SCREAMING_SNAKE_CASE   :				Tuple							    = batch_size - rest_num_examples
         _SCREAMING_SNAKE_CASE   :				Tuple							    = self._index_in_epoch
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self._images[start:end]
         _SCREAMING_SNAKE_CASE   :				Union[str, 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   :				str							    = self._index_in_epoch
         return self._images[start:end], self._labels[start:end]





@deprecated(lowerCamelCase__,						"Please write your own downloading logic."				)
def    _lowerCAmelCase    (     lowerCamelCase__							:							int,						lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							List[str]				)  ->    Optional[int]:
  if not gfile.Exists(lowerCamelCase__				):
    gfile.MakeDirs(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = os.path.join(lowerCamelCase__,						lowerCamelCase__				)
  if not gfile.Exists(lowerCamelCase__				):
    urllib.request.urlretrieve(lowerCamelCase__,						lowerCamelCase__				)  # noqa: S310
    with gfile.GFile(lowerCamelCase__				) as f:
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = f.size()
    print("Successfully downloaded",						lowerCamelCase__,						lowerCamelCase__,						"bytes."				)
  return filepath





@deprecated(
    lowerCamelCase__,						"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"				)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							Optional[int]=False,						lowerCamelCase__							:							int=False,						lowerCamelCase__							:							Optional[int]=dtypes.floataa,						lowerCamelCase__							:							Optional[int]=True,						lowerCamelCase__							:							Dict=5_0_0_0,						lowerCamelCase__							:							Optional[int]=None,						lowerCamelCase__							:							Tuple=DEFAULT_SOURCE_URL,						)  ->    Tuple:
  if fake_data:

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

    _SCREAMING_SNAKE_CASE   :				List[str]							    = fake()
    _SCREAMING_SNAKE_CASE   :				Dict							    = fake()
    _SCREAMING_SNAKE_CASE   :				str							    = fake()
    return _Datasets(train=lowerCamelCase__,						validation=lowerCamelCase__,						test=lowerCamelCase__				)

  if not source_url:  # empty string check
    _SCREAMING_SNAKE_CASE   :				Tuple							    = DEFAULT_SOURCE_URL

  _SCREAMING_SNAKE_CASE   :				Any							    = "train-images-idx3-ubyte.gz"
  _SCREAMING_SNAKE_CASE   :				int							    = "train-labels-idx1-ubyte.gz"
  _SCREAMING_SNAKE_CASE   :				str							    = "t10k-images-idx3-ubyte.gz"
  _SCREAMING_SNAKE_CASE   :				Tuple							    = "t10k-labels-idx1-ubyte.gz"

  _SCREAMING_SNAKE_CASE   :				str							    = _maybe_download(
      lowerCamelCase__,						lowerCamelCase__,						source_url + train_images_file				)
  with gfile.Open(lowerCamelCase__,						"rb"				) as f:
    _SCREAMING_SNAKE_CASE   :				Dict							    = _extract_images(lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				List[str]							    = _maybe_download(
      lowerCamelCase__,						lowerCamelCase__,						source_url + train_labels_file				)
  with gfile.Open(lowerCamelCase__,						"rb"				) as f:
    _SCREAMING_SNAKE_CASE   :				Tuple							    = _extract_labels(lowerCamelCase__,						one_hot=lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Any							    = _maybe_download(
      lowerCamelCase__,						lowerCamelCase__,						source_url + test_images_file				)
  with gfile.Open(lowerCamelCase__,						"rb"				) as f:
    _SCREAMING_SNAKE_CASE   :				Dict							    = _extract_images(lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				str							    = _maybe_download(
      lowerCamelCase__,						lowerCamelCase__,						source_url + test_labels_file				)
  with gfile.Open(lowerCamelCase__,						"rb"				) as f:
    _SCREAMING_SNAKE_CASE   :				Any							    = _extract_labels(lowerCamelCase__,						one_hot=lowerCamelCase__				)

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

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = train_images[:validation_size]
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = train_labels[:validation_size]
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = train_images[validation_size:]
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = train_labels[validation_size:]

  _SCREAMING_SNAKE_CASE   :				str							    = {"dtype": dtype, "reshape": reshape, "seed": seed}

  _SCREAMING_SNAKE_CASE   :				int							    = _DataSet(lowerCamelCase__,						lowerCamelCase__,						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = _DataSet(lowerCamelCase__,						lowerCamelCase__,						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = _DataSet(lowerCamelCase__,						lowerCamelCase__,						**lowerCamelCase__				)

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





"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowercase_       :						List[str]     					=	datasets.utils.logging.get_logger(__name__)
@dataclass
class 			UpperCamelCase      (							datasets.BuilderConfig  ):
     A__      =			10000
     A__      =			None
     A__      =			None
class 			UpperCamelCase      (							datasets.ArrowBasedBuilder  ):
     A__      =			ParquetConfig
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return datasets.DatasetInfo(features=self.config.features				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if not self.config.data_files:
         raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = dl_manager.download_and_extract(self.config.data_files				)
       if isinstance(snake_case__  ,     (str, list, tuple)				):
         _SCREAMING_SNAKE_CASE   :				int							    = data_files
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [dl_manager.iter_files(snake_case__				) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,     gen_kwargs={"files": files}				)]
       _SCREAMING_SNAKE_CASE   :				Tuple							    = []
       for split_name, files in data_files.items():
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Tuple							    = [dl_manager.iter_files(snake_case__				) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(snake_case__				):
             with open(snake_case__  ,     "rb"				) as f:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = datasets.Features.from_arrow_schema(pq.read_schema(snake_case__				)				)
             break
         splits.append(datasets.SplitGenerator(name=snake_case__  ,     gen_kwargs={"files": files}				)				)
       return splits
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = table_cast(snake_case__  ,     self.info.features.arrow_schema				)
       return pa_table




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema				) != sorted(self.config.columns				):
           raise ValueError(
               F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''				)
       for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__				)				):
         with open(snake_case__  ,     "rb"				) as f:
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = pq.ParquetFile(snake_case__				)
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size  ,     columns=self.config.columns				)				):
               _SCREAMING_SNAKE_CASE   :				int							    = pa.Table.from_batches([record_batch]				)
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F'''{file_idx}_{batch_idx}''', self._cast_table(snake_case__				)
           except ValueError as e:
             logger.error(F'''Failed to read file \'{file}\' with error {type(snake_case__				)}: {e}'''				)
             raise



