


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

snake_case__				:	Dict					       =					collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
snake_case__				:	Optional[Any]					       =					'https://storage.googleapis.com/cvdf-datasets/mnist/'


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    int:
   lowerCamelCase_						:   str		= numpy.dtype(numpy.uintaa					).newbyteorder(""">"""					)
   return numpy.frombuffer(bytestream.read(4					)						,						dtype=A__					)[0]


@deprecated(A__						,						"""Please use tf.data to implement this functionality."""					)
def    __lowerCamelCase   (      A__						: List[str]					)     ->    Union[str, Any]:
   print("""Extracting"""						,						f.name					)
   with gzip.GzipFile(fileobj=A__					) as bytestream:
      lowerCamelCase_						:   Optional[Any]		= _readaa(A__					)
      if magic != 2051:
         raise ValueError(
             """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)					)
      lowerCamelCase_						:   Optional[Any]		= _readaa(A__					)
      lowerCamelCase_						:   int		= _readaa(A__					)
      lowerCamelCase_						:   List[str]		= _readaa(A__					)
      lowerCamelCase_						:   List[Any]		= bytestream.read(rows * cols * num_images					)
      lowerCamelCase_						:   Any		= numpy.frombuffer(A__						,						dtype=numpy.uinta					)
      lowerCamelCase_						:   Optional[int]		= data.reshape(A__						,						A__						,						A__						,						1					)
      return data


@deprecated(A__						,						"""Please use tf.one_hot on tensors."""					)
def    __lowerCamelCase   (      A__						: Any						,						A__						: Tuple					)     ->    Dict:
   lowerCamelCase_						:   Union[str, Any]		= labels_dense.shape[0]
   lowerCamelCase_						:   str		= numpy.arange(A__					) * num_classes
   lowerCamelCase_						:   str		= numpy.zeros((num_labels, num_classes)					)
   lowerCamelCase_						:   List[str]		= 1
   return labels_one_hot


@deprecated(A__						,						"""Please use tf.data to implement this functionality."""					)
def    __lowerCamelCase   (      A__						: List[str]						,						A__						: List[str]=False						,						A__						: Optional[int]=10					)     ->    Any:
   print("""Extracting"""						,						f.name					)
   with gzip.GzipFile(fileobj=A__					) as bytestream:
      lowerCamelCase_						:   Optional[int]		= _readaa(A__					)
      if magic != 2049:
         raise ValueError(
             """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)					)
      lowerCamelCase_						:   List[Any]		= _readaa(A__					)
      lowerCamelCase_						:   List[str]		= bytestream.read(A__					)
      lowerCamelCase_						:   Union[str, Any]		= numpy.frombuffer(A__						,						dtype=numpy.uinta					)
      if one_hot:
         return _dense_to_one_hot(A__						,						A__					)
      return labels





class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       @deprecated(
           __a		,				"""Please use alternatives such as official/mnist/_DataSet.py"""
           """ from tensorflow/models."""		,				)
       def __init__(						self				:				Union[str, Any]		,				__a				:				List[str]		,				__a				:				int		,				__a				:				Any=False		,				__a				:				List[str]=False		,				__a				:				Tuple=dtypes.floataa		,				__a				:				Tuple=True		,				__a				:				str=None		,				)					->Dict:
          lowerCamelCase_,	lowerCamelCase_						:   Union[str, Any]		= 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							)
          lowerCamelCase_						:   str		= 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:
             lowerCamelCase_						:   str		= 10_000
             lowerCamelCase_						:   Any		= one_hot
          else:
             assert (
                 images.shape[0] == labels.shape[0]
             ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
             lowerCamelCase_						:   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
                lowerCamelCase_						:   str		= images.reshape(
                    images.shape[0]		,				images.shape[1] * images.shape[2]							)
             if dtype == dtypes.floataa:
                # Convert from [0, 255] -> [0.0, 1.0].
                lowerCamelCase_						:   Optional[Any]		= images.astype(numpy.floataa							)
                lowerCamelCase_						:   Optional[Any]		= numpy.multiply(__a		,				1.0 / 255.0							)
          lowerCamelCase_						:   Optional[int]		= images
          lowerCamelCase_						:   List[Any]		= labels
          lowerCamelCase_						:   Optional[Any]		= 0
          lowerCamelCase_						:   Tuple		= 0



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



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



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



       @property
       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Tuple:
          return self._epochs_completed




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







@deprecated(A__						,						"""Please write your own downloading logic."""					)
def    __lowerCamelCase   (      A__						: List[str]						,						A__						: int						,						A__						: int					)     ->    Tuple:
   if not gfile.Exists(A__					):
      gfile.MakeDirs(A__					)
   lowerCamelCase_						:   Tuple		= os.path.join(A__						,						A__					)
   if not gfile.Exists(A__					):
      urllib.request.urlretrieve(A__						,						A__					)  # noqa: S310
      with gfile.GFile(A__					) as f:
         lowerCamelCase_						:   List[str]		= f.size()
      print("""Successfully downloaded"""						,						A__						,						A__						,						"""bytes."""					)
   return filepath



@deprecated(
    A__						,						"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""					)
def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: List[Any]=False						,						A__						: Optional[Any]=False						,						A__						: List[Any]=dtypes.floataa						,						A__						: Union[str, Any]=True						,						A__						: Optional[Any]=5000						,						A__						: Optional[int]=None						,						A__						: Union[str, Any]=DEFAULT_SOURCE_URL						,						)     ->    Union[str, Any]:
   if fake_data:

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

      lowerCamelCase_						:   Any		= fake()
      lowerCamelCase_						:   List[str]		= fake()
      lowerCamelCase_						:   List[str]		= fake()
      return _Datasets(train=A__						,						validation=A__						,						test=A__					)

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

   lowerCamelCase_						:   Union[str, Any]		= """train-images-idx3-ubyte.gz"""
   lowerCamelCase_						:   Tuple		= """train-labels-idx1-ubyte.gz"""
   lowerCamelCase_						:   Union[str, Any]		= """t10k-images-idx3-ubyte.gz"""
   lowerCamelCase_						:   str		= """t10k-labels-idx1-ubyte.gz"""

   lowerCamelCase_						:   Union[str, Any]		= _maybe_download(
       A__						,						A__						,						source_url + train_images_file					)
   with gfile.Open(A__						,						"""rb"""					) as f:
      lowerCamelCase_						:   Tuple		= _extract_images(A__					)

   lowerCamelCase_						:   Any		= _maybe_download(
       A__						,						A__						,						source_url + train_labels_file					)
   with gfile.Open(A__						,						"""rb"""					) as f:
      lowerCamelCase_						:   str		= _extract_labels(A__						,						one_hot=A__					)

   lowerCamelCase_						:   Dict		= _maybe_download(
       A__						,						A__						,						source_url + test_images_file					)
   with gfile.Open(A__						,						"""rb"""					) as f:
      lowerCamelCase_						:   Union[str, Any]		= _extract_images(A__					)

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

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

   lowerCamelCase_						:   str		= train_images[:validation_size]
   lowerCamelCase_						:   Tuple		= train_labels[:validation_size]
   lowerCamelCase_						:   Dict		= train_images[validation_size:]
   lowerCamelCase_						:   Union[str, Any]		= train_labels[validation_size:]

   lowerCamelCase_						:   str		= {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

   lowerCamelCase_						:   int		= _DataSet(A__						,						A__						,						**A__					)
   lowerCamelCase_						:   Optional[Any]		= _DataSet(A__						,						A__						,						**A__					)
   lowerCamelCase_						:   Optional[int]		= _DataSet(A__						,						A__						,						**A__					)

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









import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





