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

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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCamelCase_    :		Union[str, Any]							=	"""https://storage.googleapis.com/cvdf-datasets/mnist/"""
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       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						):
					print('Extracting'				,    f.name						)
					with gzip.GzipFile(fileobj=__lowerCamelCase						) as bytestream:
										__a		 =       _readaa(__lowerCamelCase						)
										if magic != 2051:
															raise ValueError(
															    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)						)
										__a		 =       _readaa(__lowerCamelCase						)
										__a		 =       _readaa(__lowerCamelCase						)
										__a		 =       _readaa(__lowerCamelCase						)
										__a		 =       bytestream.read(rows * cols * num_images						)
										__a		 =       numpy.frombuffer(__lowerCamelCase				,    dtype=numpy.uinta						)
										__a		 =       data.reshape(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    1						)
										return data
@deprecated(__lowerCamelCase				,    'Please use tf.one_hot on tensors.'						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       labels_dense.shape[0]
					__a		 =       numpy.arange(__lowerCamelCase						) * num_classes
					__a		 =       numpy.zeros((num_labels, num_classes)						)
					__a		 =       1
					return labels_one_hot
@deprecated(__lowerCamelCase				,    'Please use tf.data to implement this functionality.'						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase=False				,    __lowerCamelCase=10						):
					print('Extracting'				,    f.name						)
					with gzip.GzipFile(fileobj=__lowerCamelCase						) as bytestream:
										__a		 =       _readaa(__lowerCamelCase						)
										if magic != 2049:
															raise ValueError(
															    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)						)
										__a		 =       _readaa(__lowerCamelCase						)
										__a		 =       bytestream.read(__lowerCamelCase						)
										__a		 =       numpy.frombuffer(__lowerCamelCase				,    dtype=numpy.uinta						)
										if one_hot:
															return _dense_to_one_hot(__lowerCamelCase				,    __lowerCamelCase						)
										return labels


class 						a__							:



			@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				,						)			->   Dict:
								__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		 =       1_0_0_0_0
													__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 __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								return self._images



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								return self._labels



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return self._num_examples



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								return self._epochs_completed







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=False				,						UpperCAmelCase=True   )			->   List[Any]:
								if fake_data:
													__a		 =       [1] * 7_8_4
													__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(__lowerCamelCase				,    'Please write your own downloading logic.'						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					if not gfile.Exists(__lowerCamelCase						):
										gfile.MakeDirs(__lowerCamelCase						)
					__a		 =       os.path.join(__lowerCamelCase				,    __lowerCamelCase						)
					if not gfile.Exists(__lowerCamelCase						):
										urllib.request.urlretrieve(__lowerCamelCase				,    __lowerCamelCase						)  # noqa: S310
										with gfile.GFile(__lowerCamelCase						) as f:
															__a		 =       f.size()
										print('Successfully downloaded'				,    __lowerCamelCase				,    __lowerCamelCase				,    'bytes.'						)
					return filepath



@deprecated(
    __lowerCamelCase				,    'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase=False				,    __lowerCamelCase=False				,    __lowerCamelCase=dtypes.floataa				,    __lowerCamelCase=True				,    __lowerCamelCase=5000				,    __lowerCamelCase=None				,    __lowerCamelCase=DEFAULT_SOURCE_URL				,    ):
					if fake_data:

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

										__a		 =       fake()
										__a		 =       fake()
										__a		 =       fake()
										return _Datasets(train=__lowerCamelCase				,    validation=__lowerCamelCase				,    test=__lowerCamelCase						)

					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(
					    __lowerCamelCase				,    __lowerCamelCase				,    source_url + train_images_file						)
					with gfile.Open(__lowerCamelCase				,    'rb'						) as f:
										__a		 =       _extract_images(__lowerCamelCase						)

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

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

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

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

					__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(__lowerCamelCase				,    __lowerCamelCase				,    **__lowerCamelCase						)
					__a		 =       _DataSet(__lowerCamelCase				,    __lowerCamelCase				,    **__lowerCamelCase						)
					__a		 =       _DataSet(__lowerCamelCase				,    __lowerCamelCase				,    **__lowerCamelCase						)

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





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 						a__							(       __snake_case		):
			A__    :       torch.FloatTensor
			A__    :       torch.FloatTensor
			A__    :       Optional[torch.FloatTensor]        =      None






class 						a__							(       __snake_case				,						__snake_case		):
			A__    :       Optional[Any]        =      2



			@register_to_config
			def __init__(						self				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 1.007				,						UpperCAmelCase = 8_0				,						UpperCAmelCase = 0.05				,						UpperCAmelCase = 5_0				,						)			->   Optional[Any]:
								# standard deviation of the initial noise distribution
								__a		 =       sigma_max

								# setable values
								__a		 =       None
								__a		 =       None
								__a		 =       None  # sigma(t_i)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   torch.FloatTensor:
								return sample



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   int:
								__a		 =       num_inference_steps
								__a		 =       np.arange(0				,						self.num_inference_steps   )[::-1].copy()
								__a		 =       torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )
								__a		 =       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								__a		 =       torch.tensor(UpperCAmelCase				,						dtype=torch.floataa				,						device=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
													__a		 =       min(self.config.s_churn / self.num_inference_steps				,						2**0.5 - 1   )
								else:
													__a		 =       0

								# sample eps ~ N(0, S_noise^2 * I)
								__a		 =       self.config.s_noise * randn_tensor(sample.shape				,						generator=UpperCAmelCase   ).to(sample.device   )
								__a		 =       sigma + gamma * sigma
								__a		 =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_hat + sigma_hat * model_output
								__a		 =       (sample_hat - pred_original_sample) / sigma_hat
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_prev + sigma_prev * model_output
								__a		 =       (sample_prev - pred_original_sample) / sigma_prev
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								raise NotImplementedError()




