




"""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 		_snake_case	(   _snake_case				:   int				)       ->   List[str]:






      '''simple docstring'''


      _A     =  numpy.dtype(numpy.uintaa				).newbyteorder('>'				)
      return numpy.frombuffer(bytestream.read(4				)							,    dtype=_snake_case				)[0]






@deprecated(_snake_case							,    'Please use tf.data to implement this functionality.'				)
def 		_snake_case	(   _snake_case				:   Dict				)       ->   Union[str, Any]:






      '''simple docstring'''


      print('Extracting'							,    f.name				)
      with gzip.GzipFile(fileobj=_snake_case				) as bytestream:
            _A     =  _readaa(_snake_case				)
            if magic != 20_51:
                  raise ValueError(
                      'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)				)
            _A     =  _readaa(_snake_case				)
            _A     =  _readaa(_snake_case				)
            _A     =  _readaa(_snake_case				)
            _A     =  bytestream.read(rows * cols * num_images				)
            _A     =  numpy.frombuffer(_snake_case							,    dtype=numpy.uinta				)
            _A     =  data.reshape(_snake_case							,    _snake_case							,    _snake_case							,    1				)
            return data






@deprecated(_snake_case							,    'Please use tf.one_hot on tensors.'				)
def 		_snake_case	(   _snake_case				:   List[str]							,    _snake_case				:   List[str]				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  labels_dense.shape[0]
      _A     =  numpy.arange(_snake_case				) * num_classes
      _A     =  numpy.zeros((num_labels, num_classes)				)
      _A     =  1
      return labels_one_hot






@deprecated(_snake_case							,    'Please use tf.data to implement this functionality.'				)
def 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   int=False							,    _snake_case				:   Tuple=10				)       ->   int:






      '''simple docstring'''


      print('Extracting'							,    f.name				)
      with gzip.GzipFile(fileobj=_snake_case				) as bytestream:
            _A     =  _readaa(_snake_case				)
            if magic != 20_49:
                  raise ValueError(
                      'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)				)
            _A     =  _readaa(_snake_case				)
            _A     =  bytestream.read(_snake_case				)
            _A     =  numpy.frombuffer(_snake_case							,    dtype=numpy.uinta				)
            if one_hot:
                  return _dense_to_one_hot(_snake_case							,    _snake_case				)
            return labels





class 							lowercase_			:



 '''simple docstring'''

 @deprecated(
     _UpperCAmelCase       ,				'Please use alternatives such as official/mnist/_DataSet.py'
     ' from tensorflow/models.'       ,				)
 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			List[str]=False       ,				_UpperCAmelCase			:			Tuple=False       ,				_UpperCAmelCase			:			str=dtypes.floataa       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			List[str]=None       ,				):
       _A  ,       _A     =  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						)
       _A     =  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:
             _A     =  10_000
             _A     =  one_hot
       else:
             assert (
                 images.shape[0] == labels.shape[0]
             ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
             _A     =  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
                   _A     =  images.reshape(
                       images.shape[0]       ,				images.shape[1] * images.shape[2]						)
             if dtype == dtypes.floataa:
                   # Convert from [0, 255] -> [0.0, 1.0].
                   _A     =  images.astype(numpy.floataa						)
                   _A     =  numpy.multiply(_UpperCAmelCase       ,				1.0 / 255.0						)
       _A     =  images
       _A     =  labels
       _A     =  0
       _A     =  0

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       return self._images

 @property
 def      lowerCAmelCase_				(					self			:			int						):
       return self._labels

 @property
 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       return self._num_examples

 @property
 def      lowerCAmelCase_				(					self			:			Any						):
       return self._epochs_completed



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






@deprecated(_snake_case							,    'Please write your own downloading logic.'				)
def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   Dict							,    _snake_case				:   List[Any]				)       ->   Union[str, Any]:






      '''simple docstring'''


      if not gfile.Exists(_snake_case				):
            gfile.MakeDirs(_snake_case				)
      _A     =  os.path.join(_snake_case							,    _snake_case				)
      if not gfile.Exists(_snake_case				):
            urllib.request.urlretrieve(_snake_case							,    _snake_case				)  # noqa: S310
            with gfile.GFile(_snake_case				) as f:
                  _A     =  f.size()
            print('Successfully downloaded'							,    _snake_case							,    _snake_case							,    'bytes.'				)
      return filepath







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






      '''simple docstring'''


      if fake_data:

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

            _A     =  fake()
            _A     =  fake()
            _A     =  fake()
            return _Datasets(train=_snake_case							,    validation=_snake_case							,    test=_snake_case				)

      if not source_url:  # empty string check
            _A     =  DEFAULT_SOURCE_URL

      _A     =  'train-images-idx3-ubyte.gz'
      _A     =  'train-labels-idx1-ubyte.gz'
      _A     =  't10k-images-idx3-ubyte.gz'
      _A     =  't10k-labels-idx1-ubyte.gz'

      _A     =  _maybe_download(
          _snake_case							,    _snake_case							,    source_url + train_images_file				)
      with gfile.Open(_snake_case							,    'rb'				) as f:
            _A     =  _extract_images(_snake_case				)

      _A     =  _maybe_download(
          _snake_case							,    _snake_case							,    source_url + train_labels_file				)
      with gfile.Open(_snake_case							,    'rb'				) as f:
            _A     =  _extract_labels(_snake_case							,    one_hot=_snake_case				)

      _A     =  _maybe_download(
          _snake_case							,    _snake_case							,    source_url + test_images_file				)
      with gfile.Open(_snake_case							,    'rb'				) as f:
            _A     =  _extract_images(_snake_case				)

      _A     =  _maybe_download(
          _snake_case							,    _snake_case							,    source_url + test_labels_file				)
      with gfile.Open(_snake_case							,    'rb'				) as f:
            _A     =  _extract_labels(_snake_case							,    one_hot=_snake_case				)

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

      _A     =  train_images[:validation_size]
      _A     =  train_labels[:validation_size]
      _A     =  train_images[validation_size:]
      _A     =  train_labels[validation_size:]

      _A     =  {'dtype': dtype, 'reshape': reshape, 'seed': seed}

      _A     =  _DataSet(_snake_case							,    _snake_case							,    **_snake_case				)
      _A     =  _DataSet(_snake_case							,    _snake_case							,    **_snake_case				)
      _A     =  _DataSet(_snake_case							,    _snake_case							,    **_snake_case				)

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







"""simple docstring"""

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      (KDPMaDiscreteScheduler,)
 UpperCAmelCase       :	Any     =      10

 def      lowerCAmelCase_				(					self			:			Dict       ,				**_UpperCAmelCase			:			Optional[Any]						):
       _A     =  {
           'num_train_timesteps': 1_100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_UpperCAmelCase						)
       return config

 def      lowerCAmelCase_				(					self			:			Any						):
       for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,				[0.0002, 0.002, 0.02]						):
             self.check_over_configs(beta_start=_UpperCAmelCase       ,				beta_end=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config(prediction_type='v_prediction'						)
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _A     =  sample.to(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_9_3_4E-0_7						) < 1E-2
             assert abs(result_mean.item() - 6.1_1_1_2E-1_0						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7						) < 1E-2
             assert abs(result_mean.item() - 0.0002						) < 1E-3

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _A     =  sample.to(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3



 def      lowerCAmelCase_				(					self			:			Any						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps       ,				device=_UpperCAmelCase						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter.to(_UpperCAmelCase						) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if str(_UpperCAmelCase						).startswith('cpu'						):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3

