






'''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

SCREAMING_SNAKE_CASE							=						collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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


def 				lowercase_					(     __A			:							int      )							->   List[str]:



 """simple docstring"""




 lowercase       : Dict						=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 				lowercase_					(     __A			:							Dict      )							->   Union[str, Any]:



 """simple docstring"""




 print('''Extracting'''       ,   f.name      )
 with gzip.GzipFile(fileobj=_snake_case      ) as bytestream:
  lowercase       : Optional[int]						=_readaa(_snake_case      )
  if magic != 2_0_5_1:
   raise ValueError(
       '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)      )
  lowercase       : Optional[int]						=_readaa(_snake_case      )
  lowercase       : Optional[int]						=_readaa(_snake_case      )
  lowercase       : List[str]						=_readaa(_snake_case      )
  lowercase       : str						=bytestream.read(rows * cols * num_images      )
  lowercase       : List[str]						=numpy.frombuffer(_snake_case       ,   dtype=numpy.uinta      )
  lowercase       : List[str]						=data.reshape(_snake_case       ,   _snake_case       ,   _snake_case       ,   1      )
  return data


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



 """simple docstring"""




 lowercase       : List[Any]						=labels_dense.shape[0]
 lowercase       : List[Any]						=numpy.arange(_snake_case      ) * num_classes
 lowercase       : Optional[Any]						=numpy.zeros((num_labels, num_classes)      )
 lowercase       : List[Any]						=1
 return labels_one_hot


@deprecated(_snake_case       ,   '''Please use tf.data to implement this functionality.'''      )
def 				lowercase_					(     __A			:							Dict       ,   __A			:							int=False       ,   __A			:							Tuple=1_0      )							->   int:



 """simple docstring"""




 print('''Extracting'''       ,   f.name      )
 with gzip.GzipFile(fileobj=_snake_case      ) as bytestream:
  lowercase       : Optional[Any]						=_readaa(_snake_case      )
  if magic != 2_0_4_9:
   raise ValueError(
       '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)      )
  lowercase       : int						=_readaa(_snake_case      )
  lowercase       : Tuple						=bytestream.read(_snake_case      )
  lowercase       : List[str]						=numpy.frombuffer(_snake_case       ,   dtype=numpy.uinta      )
  if one_hot:
   return _dense_to_one_hot(_snake_case       ,   _snake_case      )
  return labels
class UpperCAmelCase_       :




      """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  ,   )	->	str:


       '''simple docstring'''

       lowercase	,		lowercase       : Tuple						=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		)
       lowercase       : Optional[int]						=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:
        lowercase       : Dict						=1_0000
        lowercase       : Optional[Any]						=one_hot
       else:
        assert (
            images.shape[0] == labels.shape[0]
        ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
        lowercase       : 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
         lowercase       : Optional[int]						=images.reshape(
             images.shape[0]  ,   images.shape[1] * images.shape[2]		)
        if dtype == dtypes.floataa:
         # Convert from [0, 255] -> [0.0, 1.0].
         lowercase       : int						=images.astype(numpy.floataa		)
         lowercase       : List[str]						=numpy.multiply(_UpperCAmelCase  ,   1.0 / 2_5_5.0		)
       lowercase       : Optional[Any]						=images
       lowercase       : Dict						=labels
       lowercase       : Dict						=0
       lowercase       : Tuple						=0






      @property
      def 					A__   (			self      :		Dict		)	->	int:


       '''simple docstring'''

       return self._images






      @property
      def 					A__   (			self      :		int		)	->	Any:


       '''simple docstring'''

       return self._labels






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


       '''simple docstring'''

       return self._num_examples






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


       '''simple docstring'''

       return self._epochs_completed






      def 					A__   (			self      :		Optional[int]  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Optional[int]=False  ,   UpperCAmelCase      :		List[Any]=True		)	->	Dict:


       '''simple docstring'''

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





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



 """simple docstring"""




 if not gfile.Exists(_snake_case      ):
  gfile.MakeDirs(_snake_case      )
 lowercase       : Any						=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:
   lowercase       : Any						=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 				lowercase_					(     __A			:							Dict       ,   __A			:							List[str]=False       ,   __A			:							Optional[Any]=False       ,   __A			:							int=dtypes.floataa       ,   __A			:							Union[str, Any]=True       ,   __A			:							int=5_0_0_0       ,   __A			:							Any=None       ,   __A			:							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      )

  lowercase       : Optional[Any]						=fake()
  lowercase       : Union[str, Any]						=fake()
  lowercase       : Optional[Any]						=fake()
  return _Datasets(train=_snake_case       ,   validation=_snake_case       ,   test=_snake_case      )

 if not source_url:  # empty string check
  lowercase       : Dict						=DEFAULT_SOURCE_URL

 lowercase       : List[Any]						='''train-images-idx3-ubyte.gz'''
 lowercase       : str						='''train-labels-idx1-ubyte.gz'''
 lowercase       : Any						='''t10k-images-idx3-ubyte.gz'''
 lowercase       : int						='''t10k-labels-idx1-ubyte.gz'''

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

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

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

 lowercase       : int						=_maybe_download(
     _snake_case       ,   _snake_case       ,   source_url + test_labels_file      )
 with gfile.Open(_snake_case       ,   '''rb'''      ) as f:
  lowercase       : List[Any]						=_extract_labels(_snake_case       ,   one_hot=_snake_case      )

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

 lowercase       : int						=train_images[:validation_size]
 lowercase       : Tuple						=train_labels[:validation_size]
 lowercase       : List[str]						=train_images[validation_size:]
 lowercase       : Dict						=train_labels[validation_size:]

 lowercase       : Tuple						={'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

 lowercase       : int						=_DataSet(_snake_case       ,   _snake_case       ,   **_snake_case      )
 lowercase       : Dict						=_DataSet(_snake_case       ,   _snake_case       ,   **_snake_case      )
 lowercase       : List[Any]						=_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

