
"""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__      :	Any          =    collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

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



def 							a__ (				lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						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 							a__ (				lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     print("Extracting"       ,    f.name		)
     with gzip.GzipFile(fileobj=lowerCAmelCase		) as bytestream:
          UpperCAmelCase__							: Optional[int] 							=						_readaa(lowerCAmelCase		)
          if magic != 2051:
               raise ValueError(
                   "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)		)
          UpperCAmelCase__							: Any 							=						_readaa(lowerCAmelCase		)
          UpperCAmelCase__							: int 							=						_readaa(lowerCAmelCase		)
          UpperCAmelCase__							: Optional[Any] 							=						_readaa(lowerCAmelCase		)
          UpperCAmelCase__							: Tuple 							=						bytestream.read(rows * cols * num_images		)
          UpperCAmelCase__							: Dict 							=						numpy.frombuffer(lowerCAmelCase       ,    dtype=numpy.uinta		)
          UpperCAmelCase__							: List[Any] 							=						data.reshape(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    1		)
          return data



@deprecated(lowerCAmelCase       ,    "Please use tf.one_hot on tensors."		)
def 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[int] 							=						labels_dense.shape[0]
     UpperCAmelCase__							: str 							=						numpy.arange(lowerCAmelCase		) * num_classes
     UpperCAmelCase__							: Union[str, Any] 							=						numpy.zeros((num_labels, num_classes)		)
     UpperCAmelCase__							: str 							=						1
     return labels_one_hot



@deprecated(lowerCAmelCase       ,    "Please use tf.data to implement this functionality."		)
def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       List[Any]=False       ,    lowerCAmelCase	:       List[str]=10		):






     '''simple docstring'''
     print("Extracting"       ,    f.name		)
     with gzip.GzipFile(fileobj=lowerCAmelCase		) as bytestream:
          UpperCAmelCase__							: Union[str, Any] 							=						_readaa(lowerCAmelCase		)
          if magic != 2049:
               raise ValueError(
                   "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)		)
          UpperCAmelCase__							: List[str] 							=						_readaa(lowerCAmelCase		)
          UpperCAmelCase__							: Dict 							=						bytestream.read(lowerCAmelCase		)
          UpperCAmelCase__							: Union[str, Any] 							=						numpy.frombuffer(lowerCAmelCase       ,    dtype=numpy.uinta		)
          if one_hot:
               return _dense_to_one_hot(lowerCAmelCase       ,    lowerCAmelCase		)
          return labels







class   _lowercase						:


     '''simple docstring'''





     @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 ,			)->					List[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						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  )
          UpperCAmelCase__							: Tuple 							=						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:
               UpperCAmelCase__							: List[str] 							=						1_00_00
               UpperCAmelCase__							: Tuple 							=						one_hot
          else:
               assert (
                   images.shape[0] == labels.shape[0]
               ), F"images.shape: {images.shape} labels.shape: {labels.shape}"
               UpperCAmelCase__							: str 							=						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
                    UpperCAmelCase__							: Optional[Any] 							=						images.reshape(
                        images.shape[0] ,			images.shape[1] * images.shape[2]  )
               if dtype == dtypes.floataa:
                    # Convert from [0, 255] -> [0.0, 1.0].
                    UpperCAmelCase__							: Dict 							=						images.astype(numpy.floataa  )
                    UpperCAmelCase__							: Dict 							=						numpy.multiply(__UpperCamelCase ,			1.0 / 255.0  )
          UpperCAmelCase__							: Union[str, Any] 							=						images
          UpperCAmelCase__							: Optional[Any] 							=						labels
          UpperCAmelCase__							: Optional[int] 							=						0
          UpperCAmelCase__							: Tuple 							=						0



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return self._images



     @property
     def  lowerCAmelCase__       (      self  )->					str:
          return self._labels



     @property
     def  lowerCAmelCase__       (      self  )->					Tuple:
          return self._num_examples



     @property
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          return self._epochs_completed



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=False ,			__UpperCamelCase=True  )->					Any:
          if fake_data:
               UpperCAmelCase__							: Dict 							=						[1] * 7_84
               UpperCAmelCase__							: str 							=						[1] + [0] * 9 if self.one_hot else 0
               return (
                   [fake_image for _ in range(__UpperCamelCase  )],
                   [fake_label for _ in range(__UpperCamelCase  )],
               )
          UpperCAmelCase__							: Dict 							=						self._index_in_epoch
          # Shuffle for the first epoch
          if self._epochs_completed == 0 and start == 0 and shuffle:
               UpperCAmelCase__							: str 							=						numpy.arange(self._num_examples  )
               numpy.random.shuffle(__UpperCamelCase  )
               UpperCAmelCase__							: List[Any] 							=						self.images[perma]
               UpperCAmelCase__							: str 							=						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
               UpperCAmelCase__							: str 							=						self._num_examples - start
               UpperCAmelCase__							: int 							=						self._images[start : self._num_examples]
               UpperCAmelCase__							: Optional[int] 							=						self._labels[start : self._num_examples]
               # Shuffle the data
               if shuffle:
                    UpperCAmelCase__							: Any 							=						numpy.arange(self._num_examples  )
                    numpy.random.shuffle(__UpperCamelCase  )
                    UpperCAmelCase__							: int 							=						self.images[perm]
                    UpperCAmelCase__							: Optional[int] 							=						self.labels[perm]
               # Start next epoch
               UpperCAmelCase__							: Optional[Any] 							=						0
               UpperCAmelCase__							: List[Any] 							=						batch_size - rest_num_examples
               UpperCAmelCase__							: List[Any] 							=						self._index_in_epoch
               UpperCAmelCase__							: int 							=						self._images[start:end]
               UpperCAmelCase__							: 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
               UpperCAmelCase__							: List[str] 							=						self._index_in_epoch
               return self._images[start:end], self._labels[start:end]





@deprecated(lowerCAmelCase       ,    "Please write your own downloading logic."		)
def 							a__ (				lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict		):






     '''simple docstring'''
     if not gfile.Exists(lowerCAmelCase		):
          gfile.MakeDirs(lowerCAmelCase		)
     UpperCAmelCase__							: Union[str, Any] 							=						os.path.join(lowerCAmelCase       ,    lowerCAmelCase		)
     if not gfile.Exists(lowerCAmelCase		):
          urllib.request.urlretrieve(lowerCAmelCase       ,    lowerCAmelCase		)  # noqa: S310
          with gfile.GFile(lowerCAmelCase		) as f:
               UpperCAmelCase__							: str 							=						f.size()
          print("Successfully downloaded"       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    "bytes."		)
     return filepath



@deprecated(
    lowerCAmelCase       ,    "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"		)
def 							a__ (				lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       List[str]=False       ,    lowerCAmelCase	:       Union[str, Any]=False       ,    lowerCAmelCase	:       str=dtypes.floataa       ,    lowerCAmelCase	:       Union[str, Any]=True       ,    lowerCAmelCase	:       Tuple=5000       ,    lowerCAmelCase	:       Any=None       ,    lowerCAmelCase	:       Tuple=DEFAULT_SOURCE_URL       ,    ):






     '''simple docstring'''
     if fake_data:

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

          UpperCAmelCase__							: Any 							=						fake()
          UpperCAmelCase__							: Union[str, Any] 							=						fake()
          UpperCAmelCase__							: Tuple 							=						fake()
          return _Datasets(train=lowerCAmelCase       ,    validation=lowerCAmelCase       ,    test=lowerCAmelCase		)

     if not source_url:  # empty string check
          UpperCAmelCase__							: List[Any] 							=						DEFAULT_SOURCE_URL

     UpperCAmelCase__							: Optional[int] 							=						"train-images-idx3-ubyte.gz"
     UpperCAmelCase__							: Optional[int] 							=						"train-labels-idx1-ubyte.gz"
     UpperCAmelCase__							: Dict 							=						"t10k-images-idx3-ubyte.gz"
     UpperCAmelCase__							: Any 							=						"t10k-labels-idx1-ubyte.gz"

     UpperCAmelCase__							: str 							=						_maybe_download(
         lowerCAmelCase       ,    lowerCAmelCase       ,    source_url + train_images_file		)
     with gfile.Open(lowerCAmelCase       ,    "rb"		) as f:
          UpperCAmelCase__							: int 							=						_extract_images(lowerCAmelCase		)

     UpperCAmelCase__							: Optional[int] 							=						_maybe_download(
         lowerCAmelCase       ,    lowerCAmelCase       ,    source_url + train_labels_file		)
     with gfile.Open(lowerCAmelCase       ,    "rb"		) as f:
          UpperCAmelCase__							: List[str] 							=						_extract_labels(lowerCAmelCase       ,    one_hot=lowerCAmelCase		)

     UpperCAmelCase__							: Optional[int] 							=						_maybe_download(
         lowerCAmelCase       ,    lowerCAmelCase       ,    source_url + test_images_file		)
     with gfile.Open(lowerCAmelCase       ,    "rb"		) as f:
          UpperCAmelCase__							: List[str] 							=						_extract_images(lowerCAmelCase		)

     UpperCAmelCase__							: Optional[int] 							=						_maybe_download(
         lowerCAmelCase       ,    lowerCAmelCase       ,    source_url + test_labels_file		)
     with gfile.Open(lowerCAmelCase       ,    "rb"		) as f:
          UpperCAmelCase__							: List[Any] 							=						_extract_labels(lowerCAmelCase       ,    one_hot=lowerCAmelCase		)

     if not 0 <= validation_size <= len(lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						(
              "Validation size should be between 0 and "
              F"{len(lowerCAmelCase		)}. Received: {validation_size}."
          )
          raise ValueError(lowerCAmelCase		)

     UpperCAmelCase__							: str 							=						train_images[:validation_size]
     UpperCAmelCase__							: Any 							=						train_labels[:validation_size]
     UpperCAmelCase__							: Optional[int] 							=						train_images[validation_size:]
     UpperCAmelCase__							: Dict 							=						train_labels[validation_size:]

     UpperCAmelCase__							: Optional[int] 							=						{"dtype": dtype, "reshape": reshape, "seed": seed}

     UpperCAmelCase__							: Optional[Any] 							=						_DataSet(lowerCAmelCase       ,    lowerCAmelCase       ,    **lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						_DataSet(lowerCAmelCase       ,    lowerCAmelCase       ,    **lowerCAmelCase		)
     UpperCAmelCase__							: Any 							=						_DataSet(lowerCAmelCase       ,    lowerCAmelCase       ,    **lowerCAmelCase		)

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





"""simple docstring"""




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class   _lowercase						:


     '''simple docstring'''





     _A								=							42

     # setable values
     _A								=							42
     _A								=							42
     _A								=							None



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          return cls(common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase  )







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     _A								=							42



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return True



     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 10_00 ,			__UpperCamelCase = 0.0001 ,			__UpperCamelCase = 0.02 ,			__UpperCamelCase = "linear" ,			__UpperCamelCase = None ,			__UpperCamelCase = "fixed_small" ,			__UpperCamelCase = True ,			__UpperCamelCase = "epsilon" ,			__UpperCamelCase = jnp.floataa ,			)->					List[str]:
          UpperCAmelCase__							: int 							=						dtype



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					DDPMSchedulerState:
          if common is None:
               UpperCAmelCase__							: int 							=						CommonSchedulerState.create(self  )

          # standard deviation of the initial noise distribution
          UpperCAmelCase__							: Tuple 							=						jnp.array(1.0 ,			dtype=self.dtype  )

          UpperCAmelCase__							: Tuple 							=						jnp.arange(0 ,			self.config.num_train_timesteps  ).round()[::-1]

          return DDPMSchedulerState.create(
              common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					jnp.ndarray:
          return sample



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = ()  )->					DDPMSchedulerState:
          UpperCAmelCase__							: Dict 							=						self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          UpperCAmelCase__							: Optional[int] 							=						(jnp.arange(0 ,			__UpperCamelCase  ) * step_ratio).round()[::-1]

          return state.replace(
              num_inference_steps=__UpperCamelCase ,			timesteps=__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase=None  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: int 							=						jnp.where(t > 0 ,			state.common.alphas_cumprod[t - 1] ,			jnp.array(1.0 ,			dtype=self.dtype  )  )

          # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
          # and sample from it to get previous sample
          # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
          UpperCAmelCase__							: Any 							=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small":
               UpperCAmelCase__							: Dict 							=						jnp.clip(__UpperCamelCase ,			a_min=1E-20  )
          # for rl-diffuser https://arxiv.org/abs/2205.09991
          elif variance_type == "fixed_small_log":
               UpperCAmelCase__							: Tuple 							=						jnp.log(jnp.clip(__UpperCamelCase ,			a_min=1E-20  )  )
          elif variance_type == "fixed_large":
               UpperCAmelCase__							: Union[str, Any] 							=						state.common.betas[t]
          elif variance_type == "fixed_large_log":
               # Glide max_log
               UpperCAmelCase__							: Optional[int] 							=						jnp.log(state.common.betas[t]  )
          elif variance_type == "learned":
               return predicted_variance
          elif variance_type == "learned_range":
               UpperCAmelCase__							: List[str] 							=						variance
               UpperCAmelCase__							: Union[str, Any] 							=						state.common.betas[t]
               UpperCAmelCase__							: Optional[int] 							=						(predicted_variance + 1) / 2
               UpperCAmelCase__							: Any 							=						frac * max_log + (1 - frac) * min_log

          return variance



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			)->					Union[FlaxDDPMSchedulerOutput, Tuple]:
          UpperCAmelCase__							: List[str] 							=						timestep

          if key is None:
               UpperCAmelCase__							: int 							=						jax.random.PRNGKey(0  )

          if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
               UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						jnp.split(__UpperCamelCase ,			sample.shape[1] ,			axis=1  )
          else:
               UpperCAmelCase__							: Optional[Any] 							=						None

          # 1. compute alphas, betas
          UpperCAmelCase__							: Union[str, Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: Tuple 							=						jnp.where(t > 0 ,			state.common.alphas_cumprod[t - 1] ,			jnp.array(1.0 ,			dtype=self.dtype  )  )
          UpperCAmelCase__							: Union[str, Any] 							=						1 - alpha_prod_t
          UpperCAmelCase__							: Tuple 							=						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 self.config.prediction_type == "epsilon":
               UpperCAmelCase__							: List[Any] 							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
               UpperCAmelCase__							: Any 							=						model_output
          elif self.config.prediction_type == "v_prediction":
               UpperCAmelCase__							: Union[str, Any] 							=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
                   " for the FlaxDDPMScheduler."  )

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
               UpperCAmelCase__							: List[Any] 							=						jnp.clip(__UpperCamelCase ,			-1 ,			1  )

          # 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
          UpperCAmelCase__							: List[str] 							=						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
          UpperCAmelCase__							: List[Any] 							=						state.common.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
          UpperCAmelCase__							: Tuple 							=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          def random_variance():
               UpperCAmelCase__							: Any 							=						jax.random.split(__UpperCamelCase ,			num=1  )
               UpperCAmelCase__							: int 							=						jax.random.normal(__UpperCamelCase ,			shape=model_output.shape ,			dtype=self.dtype  )
               return (self._get_variance(__UpperCamelCase ,			__UpperCamelCase ,			predicted_variance=__UpperCamelCase  ) ** 0.5) * noise

          UpperCAmelCase__							: Dict 							=						jnp.where(t > 0 ,			random_variance() ,			jnp.zeros(model_output.shape ,			dtype=self.dtype  )  )

          UpperCAmelCase__							: Any 							=						pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase ,			state=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return add_noise_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return get_velocity_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def __len__(      self  )->					Tuple:
          return self.config.num_train_timesteps



