
"""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

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

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






def    _snake_case					(		snake_case__ :  Optional[int]  ):
	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  ):
	print('Extracting'						,   f.name  )
	with gzip.GzipFile(fileobj=snake_case__  ) as bytestream:
		A     = _readaa(snake_case__  )
		if magic != 2051:
			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__ :  Tuple						,   snake_case__ :  str  ):
	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__ :  Union[str, Any]						,   snake_case__ :  Optional[int]=False						,   snake_case__ :  List[str]=10  ):
	print('Extracting'						,   f.name  )
	with gzip.GzipFile(fileobj=snake_case__  ) as bytestream:
		A     = _readaa(snake_case__  )
		if magic != 2049:
			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 					lowerCAmelCase_					:



						'''simple docstring'''


						@deprecated(
						    A_	,'Please use alternatives such as official/mnist/_DataSet.py'
						    ' from tensorflow/models.'	,)
						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Any	,A_  :   Dict=False	,A_  :   Optional[int]=False	,A_  :   List[str]=dtypes.floataa	,A_  :   Optional[Any]=True	,A_  :   Union[str, Any]=None	,)  -> Optional[Any]:
							A ,   A     = random_seed.get_seed(A_   )
							# 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(A_   ).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_0000
								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(A_	,1.0 / 2_55.0   )
							A     = images
							A     = labels
							A     = 0
							A     = 0


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> int:
							return self._images


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[int]:
							return self._labels


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Dict:
							return self._num_examples


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> int:
							return self._epochs_completed




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Dict	,A_  :   Tuple=False	,A_  :   List[Any]=True   )  -> Union[str, Any]:
							if fake_data:
								A     = [1] * 784
								A     = [1] + [0] * 9 if self.one_hot else 0
								return (
								    [fake_image for _ in range(A_   )],
								    [fake_label for _ in range(A_   )],
								)
							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(A_   )
								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(A_   )
									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__ :  List[str]						,   snake_case__ :  Optional[int]						,   snake_case__ :  List[Any]  ):
	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__ :  List[Any]						,   snake_case__ :  Dict=False						,   snake_case__ :  List[Any]=False						,   snake_case__ :  List[str]=dtypes.floataa						,   snake_case__ :  List[Any]=True						,   snake_case__ :  Any=5000						,   snake_case__ :  Tuple=None						,   snake_case__ :  Optional[int]=DEFAULT_SOURCE_URL						,   ):
	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"""






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__lowerCAmelCase       :			Tuple   	=8


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:int=BITS						)					->       Optional[Any]:
      '''simple docstring'''


      lowercase								=      x.device

      lowercase								=      (x * 2_5_5).int().clamp(0   ,						2_5_5						)

      lowercase								=      2 ** torch.arange(bits - 1   ,						-1   ,						-1   ,						device=lowerCAmelCase__						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b c h w -> b c 1 h w"""						)

      lowercase								=      ((x & mask) != 0).float()
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b c d h w -> b (c d) h w"""						)
      lowercase								=      bits * 2 - 1
      return bits


def      UpperCAmelCase__			(							lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:int=BITS						)					->       List[Any]:
      '''simple docstring'''


      lowercase								=      x.device

      lowercase								=      (x > 0).int()
      lowercase								=      2 ** torch.arange(bits - 1   ,						-1   ,						-1   ,						device=lowerCAmelCase__   ,						dtype=torch.intaa						)

      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b (c d) h w -> b c d h w"""   ,						d=8						)
      lowercase								=      reduce(x * mask   ,						"""b c d h w -> b c h w"""   ,						"""sum"""						)
      return (dec / 2_5_5).clamp(0.0   ,						1.0						)


def      UpperCAmelCase__			(							self	:Union[str, Any]   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:float = 0.0   ,						lowerCAmelCase__	:bool = True   ,						lowerCAmelCase__	:Optional[Any]=None   ,						lowerCAmelCase__	:bool = True   ,						)					->       Union[DDIMSchedulerOutput, Tuple]:
      '''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"""						)

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      lowercase								=      timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      lowercase								=      self.alphas_cumprod[timestep]
      lowercase								=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      lowercase								=      1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      lowercase								=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

      # 4. Clip "predicted x_0"
      lowercase								=      self.bit_scale
      if self.config.clip_sample:
            lowercase								=      torch.clamp(lowerCAmelCase__   ,						-scale   ,						lowerCAmelCase__						)

      # 5. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      lowercase								=      self._get_variance(lowerCAmelCase__   ,						lowerCAmelCase__						)
      lowercase								=      eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            lowercase								=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

      # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      lowercase								=      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

      # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      lowercase								=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if eta > 0:
            # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
            lowercase								=      model_output.device if torch.is_tensor(lowerCAmelCase__						) else """cpu"""
            lowercase								=      torch.randn(model_output.shape   ,						dtype=model_output.dtype   ,						generator=lowerCAmelCase__						).to(lowerCAmelCase__						)
            lowercase								=      self._get_variance(lowerCAmelCase__   ,						lowerCAmelCase__						) ** 0.5 * eta * noise

            lowercase								=      prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)


def      UpperCAmelCase__			(							self	:Any   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:List[str]="epsilon"   ,						lowerCAmelCase__	:Optional[int]=None   ,						lowerCAmelCase__	:bool = True   ,						)					->       Union[DDPMSchedulerOutput, Tuple]:
      '''simple docstring'''


      lowercase								=      timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
            lowercase				,				lowercase								=      torch.split(lowerCAmelCase__   ,						sample.shape[1]   ,						dim=1						)
      else:
            lowercase								=      None

      # 1. compute alphas, betas
      lowercase								=      self.alphas_cumprod[t]
      lowercase								=      self.alphas_cumprod[t - 1] if t > 0 else self.one
      lowercase								=      1 - alpha_prod_t
      lowercase								=      1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if prediction_type == "epsilon":
            lowercase								=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            lowercase								=      model_output
      else:
            raise ValueError(f'Unsupported prediction_type {prediction_type}.'						)

      # 3. Clip "predicted x_0"
      lowercase								=      self.bit_scale
      if self.config.clip_sample:
            lowercase								=      torch.clamp(lowerCAmelCase__   ,						-scale   ,						lowerCAmelCase__						)

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      lowercase								=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      lowercase								=      self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      lowercase								=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      lowercase								=      0
      if t > 0:
            lowercase								=      torch.randn(
                model_output.size()   ,						dtype=model_output.dtype   ,						layout=model_output.layout   ,						generator=lowerCAmelCase__						).to(model_output.device						)
            lowercase								=      (self._get_variance(lowerCAmelCase__   ,						predicted_variance=lowerCAmelCase__						) ** 0.5) * noise

      lowercase								=      pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)






class 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 1.0						,			):







            """simple docstring"""



            super().__init__()
            lowercase								=      bit_scale
            lowercase								=      (
                ddim_bit_scheduler_step if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else ddpm_bit_scheduler_step
            )

            self.register_modules(unet=__lowerCAmelCase						,			scheduler=__lowerCAmelCase		)







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 50						,			__lowerCAmelCase = None						,			__lowerCAmelCase = 1						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      torch.randn(
                (batch_size, self.unet.config.in_channels, height, width)						,			generator=__lowerCAmelCase						,			)
            lowercase								=      decimal_to_bits(__lowerCAmelCase		) * self.bit_scale
            lowercase								=      latents.to(self.device		)

            self.scheduler.set_timesteps(__lowerCAmelCase		)

            for t in self.progress_bar(self.scheduler.timesteps		):
                  # predict the noise residual
                  lowercase								=      self.unet(__lowerCAmelCase						,			__lowerCAmelCase		).sample

                  # compute the previous noisy sample x_t -> x_t-1
                  lowercase								=      self.scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample

            lowercase								=      bits_to_decimal(__lowerCAmelCase		)

            if output_type == "pil":
                  lowercase								=      self.numpy_to_pil(__lowerCAmelCase		)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



