


'''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

_UpperCAmelCase :       List[str]										= collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_UpperCAmelCase :       Union[str, Any]										= """https://storage.googleapis.com/cvdf-datasets/mnist/"""

def 							__magic_name__(  lowerCamelCase):
						__lowerCAmelCase											= 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 							__magic_name__(  lowerCamelCase):
						print('''Extracting''', f.name)
						with gzip.GzipFile(fileobj=lowerCAmelCase__) as bytestream:
												__lowerCAmelCase											= _readaa(lowerCAmelCase__)
												if magic != 2_0_5_1:
																		raise ValueError(
																		    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name))
												__lowerCAmelCase											= _readaa(lowerCAmelCase__)
												__lowerCAmelCase											= _readaa(lowerCAmelCase__)
												__lowerCAmelCase											= _readaa(lowerCAmelCase__)
												__lowerCAmelCase											= bytestream.read(rows * cols * num_images)
												__lowerCAmelCase											= numpy.frombuffer(lowerCAmelCase__, dtype=numpy.uinta)
												__lowerCAmelCase											= data.reshape(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, 1)
												return data

@deprecated(lowerCAmelCase__, '''Please use tf.one_hot on tensors.''')
def 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= labels_dense.shape[0]
						__lowerCAmelCase											= numpy.arange(lowerCAmelCase__) * num_classes
						__lowerCAmelCase											= numpy.zeros((num_labels, num_classes))
						__lowerCAmelCase											= 1
						return labels_one_hot

@deprecated(lowerCAmelCase__, '''Please use tf.data to implement this functionality.''')
def 							__magic_name__(  lowerCamelCase, lowerCamelCase=False, lowerCamelCase=1_0):
						print('''Extracting''', f.name)
						with gzip.GzipFile(fileobj=lowerCAmelCase__) as bytestream:
												__lowerCAmelCase											= _readaa(lowerCAmelCase__)
												if magic != 2_0_4_9:
																		raise ValueError(
																		    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name))
												__lowerCAmelCase											= _readaa(lowerCAmelCase__)
												__lowerCAmelCase											= bytestream.read(lowerCAmelCase__)
												__lowerCAmelCase											= numpy.frombuffer(lowerCAmelCase__, dtype=numpy.uinta)
												if one_hot:
																		return _dense_to_one_hot(lowerCAmelCase__, lowerCAmelCase__)
												return labels


class 					a__ :







				"""simple docstring"""



				@deprecated(
				    __lowerCAmelCase				,	'''Please use alternatives such as official/mnist/_DataSet.py'''
				    ''' from tensorflow/models.'''				,	)
				def __init__(self				,	__lowercase				,	__lowercase				,	__lowercase=False				,	__lowercase=False				,	__lowercase=dtypes.floataa				,	__lowercase=True				,	__lowercase=None				,	):
										__lowerCAmelCase				,    __lowerCAmelCase											= random_seed.get_seed(__lowerCAmelCase						)
										# 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											= dtypes.as_dtype(__lowerCAmelCase						).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											= 1_00_00
																__lowerCAmelCase											= one_hot
										else:
																assert (
																    images.shape[0] == labels.shape[0]
																), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
																__lowerCAmelCase											= 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											= images.reshape(
																						    images.shape[0]				,	images.shape[1] * images.shape[2]						)
																if dtype == dtypes.floataa:
																						# Convert from [0, 255] -> [0.0, 1.0].
																						__lowerCAmelCase											= images.astype(numpy.floataa						)
																						__lowerCAmelCase											= numpy.multiply(__lowerCAmelCase				,	1.0 / 2_55.0						)
										__lowerCAmelCase											= images
										__lowerCAmelCase											= labels
										__lowerCAmelCase											= 0
										__lowerCAmelCase											= 0




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




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




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




				@property
				def     _snake_case     (self						):
										return self._epochs_completed




				def     _snake_case     (self				,	__lowercase				,	__lowercase=False				,	__lowercase=True						):



										if fake_data:
																__lowerCAmelCase											= [1] * 7_84
																__lowerCAmelCase											= [1] + [0] * 9 if self.one_hot else 0
																return (
																    [fake_image for _ in range(__lowerCAmelCase						)],
																    [fake_label for _ in range(__lowerCAmelCase						)],
																)
										__lowerCAmelCase											= self._index_in_epoch
										# Shuffle for the first epoch
										if self._epochs_completed == 0 and start == 0 and shuffle:
																__lowerCAmelCase											= numpy.arange(self._num_examples						)
																numpy.random.shuffle(__lowerCAmelCase						)
																__lowerCAmelCase											= self.images[perma]
																__lowerCAmelCase											= 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											= self._num_examples - start
																__lowerCAmelCase											= self._images[start : self._num_examples]
																__lowerCAmelCase											= self._labels[start : self._num_examples]
																# Shuffle the data
																if shuffle:
																						__lowerCAmelCase											= numpy.arange(self._num_examples						)
																						numpy.random.shuffle(__lowerCAmelCase						)
																						__lowerCAmelCase											= self.images[perm]
																						__lowerCAmelCase											= self.labels[perm]
																# Start next epoch
																__lowerCAmelCase											= 0
																__lowerCAmelCase											= batch_size - rest_num_examples
																__lowerCAmelCase											= self._index_in_epoch
																__lowerCAmelCase											= self._images[start:end]
																__lowerCAmelCase											= 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											= self._index_in_epoch
																return self._images[start:end], self._labels[start:end]

@deprecated(lowerCAmelCase__, '''Please write your own downloading logic.''')
def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase):


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




@deprecated(
    lowerCAmelCase__, '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''')
def 							__magic_name__(  lowerCamelCase, lowerCamelCase=False, lowerCamelCase=False, lowerCamelCase=dtypes.floataa, lowerCamelCase=True, lowerCamelCase=5_0_0_0, lowerCamelCase=None, lowerCamelCase=DEFAULT_SOURCE_URL, ):


						if fake_data:

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

												__lowerCAmelCase											= fake()
												__lowerCAmelCase											= fake()
												__lowerCAmelCase											= fake()
												return _Datasets(train=lowerCAmelCase__, validation=lowerCAmelCase__, test=lowerCAmelCase__)

						if not source_url:  # empty string check
												__lowerCAmelCase											= DEFAULT_SOURCE_URL

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

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

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

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

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

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

						__lowerCAmelCase											= train_images[:validation_size]
						__lowerCAmelCase											= train_labels[:validation_size]
						__lowerCAmelCase											= train_images[validation_size:]
						__lowerCAmelCase											= train_labels[validation_size:]

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

						__lowerCAmelCase											= _DataSet(lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__)
						__lowerCAmelCase											= _DataSet(lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__)
						__lowerCAmelCase											= _DataSet(lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__)

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


"""simple docstring"""






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class  _A						(       lowerCAmelCase  ,    lowerCAmelCase	):
      snake_case__			:   Tuple							  =	1

      @register_to_config
      def __init__(						self		,       __lowerCAmelCase = 1000		,       __lowerCAmelCase = None						):







            """simple docstring"""



            self.set_timesteps(__lowerCAmelCase						)

            # standard deviation of the initial noise distribution
            lowercase														=	1.0

            # For now we only support F-PNDM, i.e. the runge-kutta method
            # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
            # mainly at formula (9), (12), (13) and the Algorithm 2.
            lowercase														=	4

            # running values
            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	num_inference_steps
            lowercase														=	torch.linspace(1		,       0		,       num_inference_steps + 1						)[:-1]
            lowercase														=	torch.cat([steps, torch.tensor([0.0]						)]						)

            if self.config.trained_betas is not None:
                  lowercase														=	torch.tensor(self.config.trained_betas		,       dtype=torch.floataa						)
            else:
                  lowercase														=	torch.sin(steps * math.pi / 2						) ** 2

            lowercase														=	(1.0 - self.betas**2) ** 0.5

            lowercase														=	(torch.atana(self.betas		,       self.alphas						) / math.pi * 2)[:-1]
            lowercase														=	timesteps.to(__lowerCAmelCase						)

            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = True		,       ):







            """simple docstring"""



            if self.num_inference_steps is None:
                  raise ValueError(
                      """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""						)

            lowercase														=	(self.timesteps == timestep).nonzero().item()
            lowercase														=	timestep_index + 1

            lowercase														=	sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
            self.ets.append(__lowerCAmelCase						)

            if len(self.ets						) == 1:
                  lowercase														=	self.ets[-1]
            elif len(self.ets						) == 2:
                  lowercase														=	(3 * self.ets[-1] - self.ets[-2]) / 2
            elif len(self.ets						) == 3:
                  lowercase														=	(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
            else:
                  lowercase														=	(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

            lowercase														=	self._get_prev_sample(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            if not return_dict:
                  return (prev_sample,)

            return SchedulerOutput(prev_sample=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return sample

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.alphas[timestep_index]
            lowercase														=	self.betas[timestep_index]

            lowercase														=	self.alphas[prev_timestep_index]
            lowercase														=	self.betas[prev_timestep_index]

            lowercase														=	(sample - sigma * ets) / max(__lowerCAmelCase		,       1E-8						)
            lowercase														=	next_alpha * pred + ets * next_sigma

            return prev_sample







      def __len__(						self						):







            """simple docstring"""



            return self.config.num_train_timesteps



