





"""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

__A											=						collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__A											=						"""https://storage.googleapis.com/cvdf-datasets/mnist/"""
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Optional[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[int]:





  """simple docstring"""


  print('Extracting'	,  f.name     )
  with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE     ) as bytestream:
    lowerCAmelCase__     :List[Any]											=				_readaa(_SCREAMING_SNAKE_CASE     )
    if magic != 2051:
      raise ValueError(
          'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)     )
    lowerCAmelCase__     :List[Any]											=				_readaa(_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :Optional[int]											=				_readaa(_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :Optional[Any]											=				_readaa(_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :Optional[int]											=				bytestream.read(rows * cols * num_images     )
    lowerCAmelCase__     :List[str]											=				numpy.frombuffer(_SCREAMING_SNAKE_CASE	,  dtype=numpy.uinta     )
    lowerCAmelCase__     :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	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				labels_dense.shape[0]
  lowerCAmelCase__     :List[str]											=				numpy.arange(_SCREAMING_SNAKE_CASE     ) * num_classes
  lowerCAmelCase__     :Optional[Any]											=				numpy.zeros((num_labels, num_classes)     )
  lowerCAmelCase__     :Union[str, Any]											=				1
  return labels_one_hot
@deprecated(_SCREAMING_SNAKE_CASE	,  'Please use tf.data to implement this functionality.'     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=False	,  _SCREAMING_SNAKE_CASE=10     )   ->Tuple:





  """simple docstring"""


  print('Extracting'	,  f.name     )
  with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE     ) as bytestream:
    lowerCAmelCase__     :Union[str, Any]											=				_readaa(_SCREAMING_SNAKE_CASE     )
    if magic != 2049:
      raise ValueError(
          'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)     )
    lowerCAmelCase__     :Union[str, Any]											=				_readaa(_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :Optional[Any]											=				bytestream.read(_SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :Optional[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       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=dtypes.floataa       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[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 )
         lowerCAmelCase__     :List[Any]											=				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:
           lowerCAmelCase__     :Any											=				1_0_0_0_0
           lowerCAmelCase__     :Union[str, Any]											=				one_hot
         else:
           assert (
               images.shape[0] == labels.shape[0]
           ), F"images.shape: {images.shape} labels.shape: {labels.shape}"
           lowerCAmelCase__     :Dict											=				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__     :Dict											=				images.reshape(
                 images.shape[0]       ,					images.shape[1] * images.shape[2] )
           if dtype == dtypes.floataa:
             # Convert from [0, 255] -> [0.0, 1.0].
             lowerCAmelCase__     :List[Any]											=				images.astype(numpy.floataa )
             lowerCAmelCase__     :Optional[int]											=				numpy.multiply(__UpperCAmelCase       ,					1.0 / 2_55.0 )
         lowerCAmelCase__     :Union[str, Any]											=				images
         lowerCAmelCase__     :Any											=				labels
         lowerCAmelCase__     :Any											=				0
         lowerCAmelCase__     :Union[str, Any]											=				0






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self._images






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self._labels






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self._num_examples






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self._epochs_completed






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=True ):



         '''simple docstring'''

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





  """simple docstring"""


  if not gfile.Exists(_SCREAMING_SNAKE_CASE     ):
    gfile.MakeDirs(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				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:
      lowerCAmelCase__     :Optional[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	,  _SCREAMING_SNAKE_CASE=False	,  _SCREAMING_SNAKE_CASE=False	,  _SCREAMING_SNAKE_CASE=dtypes.floataa	,  _SCREAMING_SNAKE_CASE=True	,  _SCREAMING_SNAKE_CASE=5000	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=DEFAULT_SOURCE_URL	,  )   ->Tuple:





  """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     )

    lowerCAmelCase__     :int											=				fake()
    lowerCAmelCase__     :Optional[int]											=				fake()
    lowerCAmelCase__     :List[str]											=				fake()
    return _Datasets(train=_SCREAMING_SNAKE_CASE	,  validation=_SCREAMING_SNAKE_CASE	,  test=_SCREAMING_SNAKE_CASE     )

  if not source_url:  # empty string check
    lowerCAmelCase__     :List[str]											=				DEFAULT_SOURCE_URL

  lowerCAmelCase__     :List[str]											=				'train-images-idx3-ubyte.gz'
  lowerCAmelCase__     :Dict											=				'train-labels-idx1-ubyte.gz'
  lowerCAmelCase__     :Dict											=				't10k-images-idx3-ubyte.gz'
  lowerCAmelCase__     :Tuple											=				't10k-labels-idx1-ubyte.gz'

  lowerCAmelCase__     :Dict											=				_maybe_download(
      _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  source_url + train_images_file     )
  with gfile.Open(_SCREAMING_SNAKE_CASE	,  'rb'     ) as f:
    lowerCAmelCase__     :List[str]											=				_extract_images(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Any											=				_maybe_download(
      _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  source_url + train_labels_file     )
  with gfile.Open(_SCREAMING_SNAKE_CASE	,  'rb'     ) as f:
    lowerCAmelCase__     :Tuple											=				_extract_labels(_SCREAMING_SNAKE_CASE	,  one_hot=_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :str											=				_maybe_download(
      _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  source_url + test_images_file     )
  with gfile.Open(_SCREAMING_SNAKE_CASE	,  'rb'     ) as f:
    lowerCAmelCase__     :int											=				_extract_images(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :int											=				_maybe_download(
      _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  source_url + test_labels_file     )
  with gfile.Open(_SCREAMING_SNAKE_CASE	,  'rb'     ) as f:
    lowerCAmelCase__     :Dict											=				_extract_labels(_SCREAMING_SNAKE_CASE	,  one_hot=_SCREAMING_SNAKE_CASE     )

  if not 0 <= validation_size <= len(_SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :List[Any]											=				(
        'Validation size should be between 0 and '
        F"{len(_SCREAMING_SNAKE_CASE     )}. Received: {validation_size}."
    )
    raise ValueError(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :List[Any]											=				train_images[:validation_size]
  lowerCAmelCase__     :Any											=				train_labels[:validation_size]
  lowerCAmelCase__     :Union[str, Any]											=				train_images[validation_size:]
  lowerCAmelCase__     :Union[str, Any]											=				train_labels[validation_size:]

  lowerCAmelCase__     :Any											=				{'dtype': dtype, 'reshape': reshape, 'seed': seed}

  lowerCAmelCase__     :Optional[int]											=				_DataSet(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  **_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Optional[int]											=				_DataSet(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  **_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				_DataSet(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  **_SCREAMING_SNAKE_CASE     )

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


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowerCamelCase__     :		Optional[int]        = [
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   True
  while ask_again:
    lowercase__	: Tuple     		=   input(lowercase_ )
    try:
      if default is not None and len(lowercase_ ) == 0:
        return default
      return convert_value(lowercase_ ) if convert_value is not None else result
    except Exception:
      if error_message is not None:
        print(lowercase_ )

def 		UpperCamelCase						(   lowercase_			,			lowercase_=[]			,			lowercase_=None			,			lowercase_=0 )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   BulletMenu(lowercase_			,			lowercase_ )
  lowercase__	: Any     		=   menu.run(default_choice=lowercase_ )
  return convert_value(lowercase_ ) if convert_value is not None else result

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   int(lowercase_ )
  return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   int(lowercase_ )
  return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: str     		=   int(lowercase_ )
  return DynamoBackend(DYNAMO_BACKENDS[value] ).value

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   int(lowercase_ )
  return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   int(lowercase_ )
  return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return {"yes": True, "no": False}[value.lower()]



class    _snake_case (						argparse.RawDescriptionHelpFormatter				):


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   super()._format_usage(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   usage.replace("""<command> [<args>] """       ,	"""""")
   return usage






