



"""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       __magic_name__    (   _lowerCamelCase				: List[str]					):
							__a		:		int									=       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				: Optional[int]					):
							print("""Extracting"""  ,       f.name					)
							with gzip.GzipFile(fileobj=_lowerCamelCase					) as bytestream:
														__a		:		int									=       _readaa(_lowerCamelCase					)
														if magic != 2_0_5_1:
																					raise ValueError(
																					    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)					)
														__a		:		List[Any]									=       _readaa(_lowerCamelCase					)
														__a		:		List[Any]									=       _readaa(_lowerCamelCase					)
														__a		:		int									=       _readaa(_lowerCamelCase					)
														__a		:		int									=       bytestream.read(rows * cols * num_images					)
														__a		:		Dict									=       numpy.frombuffer(_lowerCamelCase  ,       dtype=numpy.uinta					)
														__a		:		Any									=       data.reshape(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       1					)
														return data





@deprecated(_lowerCamelCase  ,       """Please use tf.one_hot on tensors."""					)
def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[Any]					):
							__a		:		List[str]									=       labels_dense.shape[0]
							__a		:		int									=       numpy.arange(_lowerCamelCase					) * num_classes
							__a		:		Union[str, Any]									=       numpy.zeros((num_labels, num_classes)					)
							__a		:		int									=       1
							return labels_one_hot





@deprecated(_lowerCamelCase  ,       """Please use tf.data to implement this functionality."""					)
def       __magic_name__    (   _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Union[str, Any]=False  ,       _lowerCamelCase				: Dict=1_0					):
							print("""Extracting"""  ,       f.name					)
							with gzip.GzipFile(fileobj=_lowerCamelCase					) as bytestream:
														__a		:		Optional[Any]									=       _readaa(_lowerCamelCase					)
														if magic != 2_0_4_9:
																					raise ValueError(
																					    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)					)
														__a		:		Union[str, Any]									=       _readaa(_lowerCamelCase					)
														__a		:		List[str]									=       bytestream.read(_lowerCamelCase					)
														__a		:		List[Any]									=       numpy.frombuffer(_lowerCamelCase  ,       dtype=numpy.uinta					)
														if one_hot:
																					return _dense_to_one_hot(_lowerCamelCase  ,       _lowerCamelCase					)
														return labels




class SCREAMING_SNAKE_CASE__					:



						@deprecated(
						    _lowercase						,     """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						,     ):





													'''simple docstring'''

													__a							,     __a		:		List[str]									=       random_seed.get_seed(_lowercase							)
													# 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		:		Any									=       dtypes.as_dtype(_lowercase							).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		:		Union[str, Any]									=       10000
																				__a		:		Optional[Any]									=       one_hot
													else:
																				assert (
																				    images.shape[0] == labels.shape[0]
																				), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																				__a		:		int									=       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		:		List[Any]									=       images.reshape(
																											    images.shape[0]						,     images.shape[1] * images.shape[2]							)
																				if dtype == dtypes.floataa:
																											# Convert from [0, 255] -> [0.0, 1.0].
																											__a		:		Dict									=       images.astype(numpy.floataa							)
																											__a		:		List[Any]									=       numpy.multiply(_lowercase						,     1.0 / 255.0							)
													__a		:		Dict									=       images
													__a		:		Dict									=       labels
													__a		:		int									=       0
													__a		:		Optional[int]									=       0



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self._images



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self._labels



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self._num_examples



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self._epochs_completed





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=False						,     _lowercase=True							):





													'''simple docstring'''

													if fake_data:
																				__a		:		int									=       [1] * 784
																				__a		:		Dict									=       [1] + [0] * 9 if self.one_hot else 0
																				return (
																				    [fake_image for _ in range(_lowercase							)],
																				    [fake_label for _ in range(_lowercase							)],
																				)
													__a		:		Optional[Any]									=       self._index_in_epoch
													# Shuffle for the first epoch
													if self._epochs_completed == 0 and start == 0 and shuffle:
																				__a		:		Tuple									=       numpy.arange(self._num_examples							)
																				numpy.random.shuffle(_lowercase							)
																				__a		:		Dict									=       self.images[perma]
																				__a		:		Optional[int]									=       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		:		Union[str, Any]									=       self._num_examples - start
																				__a		:		Union[str, Any]									=       self._images[start : self._num_examples]
																				__a		:		int									=       self._labels[start : self._num_examples]
																				# Shuffle the data
																				if shuffle:
																											__a		:		Union[str, Any]									=       numpy.arange(self._num_examples							)
																											numpy.random.shuffle(_lowercase							)
																											__a		:		Union[str, Any]									=       self.images[perm]
																											__a		:		int									=       self.labels[perm]
																				# Start next epoch
																				__a		:		List[str]									=       0
																				__a		:		Dict									=       batch_size - rest_num_examples
																				__a		:		Tuple									=       self._index_in_epoch
																				__a		:		Any									=       self._images[start:end]
																				__a		:		List[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
																				__a		:		Optional[Any]									=       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				: Dict  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Optional[Any]					):
							if not gfile.Exists(_lowerCamelCase					):
														gfile.MakeDirs(_lowerCamelCase					)
							__a		:		List[str]									=       os.path.join(_lowerCamelCase  ,       _lowerCamelCase					)
							if not gfile.Exists(_lowerCamelCase					):
														urllib.request.urlretrieve(_lowerCamelCase  ,       _lowerCamelCase					)  # noqa: S310
														with gfile.GFile(_lowerCamelCase					) as f:
																					__a		:		List[str]									=       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				: List[str]  ,       _lowerCamelCase				: Optional[int]=False  ,       _lowerCamelCase				: Union[str, Any]=False  ,       _lowerCamelCase				: Union[str, Any]=dtypes.floataa  ,       _lowerCamelCase				: Optional[Any]=True  ,       _lowerCamelCase				: List[Any]=5_0_0_0  ,       _lowerCamelCase				: Tuple=None  ,       _lowerCamelCase				: Tuple=DEFAULT_SOURCE_URL  ,       ):
							if fake_data:

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

														__a		:		Any									=       fake()
														__a		:		Optional[int]									=       fake()
														__a		:		str									=       fake()
														return _Datasets(train=_lowerCamelCase  ,       validation=_lowerCamelCase  ,       test=_lowerCamelCase					)

							if not source_url:  # empty string check
														__a		:		str									=       DEFAULT_SOURCE_URL

							__a		:		int									=       """train-images-idx3-ubyte.gz"""
							__a		:		Union[str, Any]									=       """train-labels-idx1-ubyte.gz"""
							__a		:		str									=       """t10k-images-idx3-ubyte.gz"""
							__a		:		Optional[int]									=       """t10k-labels-idx1-ubyte.gz"""

							__a		:		Optional[Any]									=       _maybe_download(
							    _lowerCamelCase  ,       _lowerCamelCase  ,       source_url + train_images_file					)
							with gfile.Open(_lowerCamelCase  ,       """rb"""					) as f:
														__a		:		List[Any]									=       _extract_images(_lowerCamelCase					)

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

							__a		:		Optional[int]									=       _maybe_download(
							    _lowerCamelCase  ,       _lowerCamelCase  ,       source_url + test_images_file					)
							with gfile.Open(_lowerCamelCase  ,       """rb"""					) as f:
														__a		:		Optional[Any]									=       _extract_images(_lowerCamelCase					)

							__a		:		Any									=       _maybe_download(
							    _lowerCamelCase  ,       _lowerCamelCase  ,       source_url + test_labels_file					)
							with gfile.Open(_lowerCamelCase  ,       """rb"""					) as f:
														__a		:		str									=       _extract_labels(_lowerCamelCase  ,       one_hot=_lowerCamelCase					)

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

							__a		:		int									=       train_images[:validation_size]
							__a		:		Optional[Any]									=       train_labels[:validation_size]
							__a		:		Optional[Any]									=       train_images[validation_size:]
							__a		:		Tuple									=       train_labels[validation_size:]

							__a		:		int									=       {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

							__a		:		List[str]									=       _DataSet(_lowerCamelCase  ,       _lowerCamelCase  ,       **_lowerCamelCase					)
							__a		:		List[Any]									=       _DataSet(_lowerCamelCase  ,       _lowerCamelCase  ,       **_lowerCamelCase					)
							__a		:		Any									=       _DataSet(_lowerCamelCase  ,       _lowerCamelCase  ,       **_lowerCamelCase					)

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






"""simple docstring"""

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	lowercase__			   =  argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)
	parser.add_argument(
	    "--original_config_file",
	    type=str,
	    required=True,
	    help="The YAML config file corresponding to the original architecture.",
	)
	parser.add_argument(
	    "--num_in_channels",
	    default=None,
	    type=int,
	    help="The number of input channels. If `None` number of input channels will be automatically inferred.",
	)
	parser.add_argument(
	    "--image_size",
	    default=512,
	    type=int,
	    help=(
	        "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
	        " Base. Use 768 for Stable Diffusion v2."
	    ),
	)
	parser.add_argument(
	    "--extract_ema",
	    action="store_true",
	    help=(
	        "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
	        " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
	        " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
	    ),
	)
	parser.add_argument(
	    "--upcast_attention",
	    action="store_true",
	    help=(
	        "Whether the attention computation should always be upcasted. This is necessary when running stable"
	        " diffusion 2.1."
	    ),
	)
	parser.add_argument(
	    "--from_safetensors",
	    action="store_true",
	    help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
	)
	parser.add_argument(
	    "--to_safetensors",
	    action="store_true",
	    help="Whether to store pipeline in safetensors format or not.",
	)
	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
	parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")





	def       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(F'''could not parse string as bool {string}'''					)

	parser.add_argument(
	    "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
	)

	parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

	lowercase__			   =  parser.parse_args()

	lowercase__			   =  download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

