"""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

__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 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]		) ->    List[str]:
      '''simple docstring'''


      lowercase														=	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 			UpperCAmelCase__       (				lowerCAmelCase__     :List[str]		) ->    Dict:
      '''simple docstring'''


      print("""Extracting"""					,		f.name		)
      with gzip.GzipFile(fileobj=lowerCAmelCase__		) as bytestream:
            lowercase														=	_readaa(lowerCAmelCase__		)
            if magic != 2_0_5_1:
                  raise ValueError(
                      """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)		)
            lowercase														=	_readaa(lowerCAmelCase__		)
            lowercase														=	_readaa(lowerCAmelCase__		)
            lowercase														=	_readaa(lowerCAmelCase__		)
            lowercase														=	bytestream.read(rows * cols * num_images		)
            lowercase														=	numpy.frombuffer(lowerCAmelCase__					,		dtype=numpy.uinta		)
            lowercase														=	data.reshape(lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		1		)
            return data


@deprecated(lowerCAmelCase__					,		"""Please use tf.one_hot on tensors."""		)
def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :Any		) ->    Optional[int]:
      '''simple docstring'''


      lowercase														=	labels_dense.shape[0]
      lowercase														=	numpy.arange(lowerCAmelCase__		) * num_classes
      lowercase														=	numpy.zeros((num_labels, num_classes)		)
      lowercase														=	1
      return labels_one_hot


@deprecated(lowerCAmelCase__					,		"""Please use tf.data to implement this functionality."""		)
def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :Optional[int]=False					,		lowerCAmelCase__     :Dict=1_0		) ->    Tuple:
      '''simple docstring'''


      print("""Extracting"""					,		f.name		)
      with gzip.GzipFile(fileobj=lowerCAmelCase__		) as bytestream:
            lowercase														=	_readaa(lowerCAmelCase__		)
            if magic != 2_0_4_9:
                  raise ValueError(
                      """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)		)
            lowercase														=	_readaa(lowerCAmelCase__		)
            lowercase														=	bytestream.read(lowerCAmelCase__		)
            lowercase														=	numpy.frombuffer(lowerCAmelCase__					,		dtype=numpy.uinta		)
            if one_hot:
                  return _dense_to_one_hot(lowerCAmelCase__					,		lowerCAmelCase__		)
            return labels





class  _A						:

      @deprecated(
          __lowerCAmelCase		,       """Please use alternatives such as official/mnist/_DataSet.py"""
          """ from tensorflow/models."""		,       )
      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=False		,       __lowerCAmelCase=False		,       __lowerCAmelCase=dtypes.floataa		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       ):







            """simple docstring"""



            lowercase							,							lowercase														=	random_seed.get_seed(__lowerCAmelCase						)
            # If op level seed is not set, use whatever graph level seed is returned
            numpy.random.seed(seeda if seed is None else seeda						)
            lowercase														=	dtypes.as_dtype(__lowerCAmelCase						).base_dtype
            if dtype not in (dtypes.uinta, dtypes.floataa):
                  raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype						)
            if fake_data:
                  lowercase														=	1_0000
                  lowercase														=	one_hot
            else:
                  assert (
                      images.shape[0] == labels.shape[0]
                  ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
                  lowercase														=	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
                        lowercase														=	images.reshape(
                            images.shape[0]		,       images.shape[1] * images.shape[2]						)
                  if dtype == dtypes.floataa:
                        # Convert from [0, 255] -> [0.0, 1.0].
                        lowercase														=	images.astype(numpy.floataa						)
                        lowercase														=	numpy.multiply(__lowerCAmelCase		,       1.0 / 2_5_5.0						)
            lowercase														=	images
            lowercase														=	labels
            lowercase														=	0
            lowercase														=	0

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return self._images

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return self._labels

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return self._num_examples

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return self._epochs_completed







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase=False		,       __lowerCAmelCase=True						):







            """simple docstring"""



            if fake_data:
                  lowercase														=	[1] * 784
                  lowercase														=	[1] + [0] * 9 if self.one_hot else 0
                  return (
                      [fake_image for _ in range(__lowerCAmelCase						)],
                      [fake_label for _ in range(__lowerCAmelCase						)],
                  )
            lowercase														=	self._index_in_epoch
            # Shuffle for the first epoch
            if self._epochs_completed == 0 and start == 0 and shuffle:
                  lowercase														=	numpy.arange(self._num_examples						)
                  numpy.random.shuffle(__lowerCAmelCase						)
                  lowercase														=	self.images[perma]
                  lowercase														=	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
                  lowercase														=	self._num_examples - start
                  lowercase														=	self._images[start : self._num_examples]
                  lowercase														=	self._labels[start : self._num_examples]
                  # Shuffle the data
                  if shuffle:
                        lowercase														=	numpy.arange(self._num_examples						)
                        numpy.random.shuffle(__lowerCAmelCase						)
                        lowercase														=	self.images[perm]
                        lowercase														=	self.labels[perm]
                  # Start next epoch
                  lowercase														=	0
                  lowercase														=	batch_size - rest_num_examples
                  lowercase														=	self._index_in_epoch
                  lowercase														=	self._images[start:end]
                  lowercase														=	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
                  lowercase														=	self._index_in_epoch
                  return self._images[start:end], self._labels[start:end]






@deprecated(lowerCAmelCase__					,		"""Please write your own downloading logic."""		)
def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :List[str]		) ->    List[Any]:
      '''simple docstring'''


      if not gfile.Exists(lowerCAmelCase__		):
            gfile.MakeDirs(lowerCAmelCase__		)
      lowercase														=	os.path.join(lowerCAmelCase__					,		lowerCAmelCase__		)
      if not gfile.Exists(lowerCAmelCase__		):
            urllib.request.urlretrieve(lowerCAmelCase__					,		lowerCAmelCase__		)  # noqa: S310
            with gfile.GFile(lowerCAmelCase__		) as f:
                  lowercase														=	f.size()
            print("""Successfully downloaded"""					,		lowerCAmelCase__					,		lowerCAmelCase__					,		"""bytes."""		)
      return filepath






@deprecated(
    lowerCAmelCase__					,		"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""		)
def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :List[Any]=False					,		lowerCAmelCase__     :Optional[Any]=False					,		lowerCAmelCase__     :Union[str, Any]=dtypes.floataa					,		lowerCAmelCase__     :str=True					,		lowerCAmelCase__     :Optional[int]=5_0_0_0					,		lowerCAmelCase__     :int=None					,		lowerCAmelCase__     :Optional[int]=DEFAULT_SOURCE_URL					,		) ->    str:
      '''simple docstring'''


      if fake_data:

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

            lowercase														=	fake()
            lowercase														=	fake()
            lowercase														=	fake()
            return _Datasets(train=lowerCAmelCase__					,		validation=lowerCAmelCase__					,		test=lowerCAmelCase__		)

      if not source_url:  # empty string check
            lowercase														=	DEFAULT_SOURCE_URL

      lowercase														=	"""train-images-idx3-ubyte.gz"""
      lowercase														=	"""train-labels-idx1-ubyte.gz"""
      lowercase														=	"""t10k-images-idx3-ubyte.gz"""
      lowercase														=	"""t10k-labels-idx1-ubyte.gz"""

      lowercase														=	_maybe_download(
          lowerCAmelCase__					,		lowerCAmelCase__					,		source_url + train_images_file		)
      with gfile.Open(lowerCAmelCase__					,		"""rb"""		) as f:
            lowercase														=	_extract_images(lowerCAmelCase__		)

      lowercase														=	_maybe_download(
          lowerCAmelCase__					,		lowerCAmelCase__					,		source_url + train_labels_file		)
      with gfile.Open(lowerCAmelCase__					,		"""rb"""		) as f:
            lowercase														=	_extract_labels(lowerCAmelCase__					,		one_hot=lowerCAmelCase__		)

      lowercase														=	_maybe_download(
          lowerCAmelCase__					,		lowerCAmelCase__					,		source_url + test_images_file		)
      with gfile.Open(lowerCAmelCase__					,		"""rb"""		) as f:
            lowercase														=	_extract_images(lowerCAmelCase__		)

      lowercase														=	_maybe_download(
          lowerCAmelCase__					,		lowerCAmelCase__					,		source_url + test_labels_file		)
      with gfile.Open(lowerCAmelCase__					,		"""rb"""		) as f:
            lowercase														=	_extract_labels(lowerCAmelCase__					,		one_hot=lowerCAmelCase__		)

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

      lowercase														=	train_images[:validation_size]
      lowercase														=	train_labels[:validation_size]
      lowercase														=	train_images[validation_size:]
      lowercase														=	train_labels[validation_size:]

      lowercase														=	{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

      lowercase														=	_DataSet(lowerCAmelCase__					,		lowerCAmelCase__					,		**lowerCAmelCase__		)
      lowercase														=	_DataSet(lowerCAmelCase__					,		lowerCAmelCase__					,		**lowerCAmelCase__		)
      lowercase														=	_DataSet(lowerCAmelCase__					,		lowerCAmelCase__					,		**lowerCAmelCase__		)

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




"""simple docstring"""






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class  _A						:

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	UNetaDConditionModel(
                sample_size=32		,       layers_per_block=1		,       block_out_channels=[32, 64]		,       down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]		,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,       in_channels=3		,       out_channels=6		,       cross_attention_dim=32		,       encoder_hid_dim=32		,       attention_head_dim=8		,       addition_embed_type="""text"""		,       addition_embed_type_num_heads=2		,       cross_attention_norm="""group_norm"""		,       resnet_time_scale_shift="""scale_shift"""		,       act_fn="""gelu"""		,       )
            unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	UNetaDConditionModel(
                sample_size=32		,       layers_per_block=[1, 2]		,       block_out_channels=[32, 64]		,       down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]		,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,       in_channels=6		,       out_channels=6		,       cross_attention_dim=32		,       encoder_hid_dim=32		,       attention_head_dim=8		,       addition_embed_type="""text"""		,       addition_embed_type_num_heads=2		,       cross_attention_norm="""group_norm"""		,       resnet_time_scale_shift="""scale_shift"""		,       act_fn="""gelu"""		,       class_embed_type="""timestep"""		,       mid_block_scale_factor=1.4_1_4		,       time_embedding_act_fn="""gelu"""		,       time_embedding_dim=32		,       )
            unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       )

            torch.manual_seed(0						)
            lowercase														=	IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "image_noising_scheduler": image_noising_scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""prompt"""]
            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            if "image" in inputs:
                  lowercase														=	inputs["""image"""]
            else:
                  lowercase														=	None

            if "mask_image" in inputs:
                  lowercase														=	inputs["""mask_image"""]
            else:
                  lowercase														=	None

            if "original_image" in inputs:
                  lowercase														=	inputs["""original_image"""]
            else:
                  lowercase														=	None

            lowercase							,							lowercase														=	pipe.encode_prompt(__lowerCAmelCase						)

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

            # set all optional components to None
            for optional_component in pipe._optional_components:
                  setattr(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	self.pipeline_class.from_pretrained(__lowerCAmelCase						)
                  pipe_loaded.to(__lowerCAmelCase						)
                  pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase						)

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            for optional_component in pipe._optional_components:
                  self.assertTrue(
                      getattr(__lowerCAmelCase		,       __lowerCAmelCase						) is None		,       f'`{optional_component}` did not stay set to None after loading.'		,       )

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

            lowercase														=	np.abs(to_np(__lowerCAmelCase						) - to_np(__lowerCAmelCase						)						).max()
            self.assertLess(__lowerCAmelCase		,       1E-4						)







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	self.pipeline_class.from_pretrained(__lowerCAmelCase						)
                  pipe_loaded.to(__lowerCAmelCase						)
                  pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase						)

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

            lowercase														=	np.abs(to_np(__lowerCAmelCase						) - to_np(__lowerCAmelCase						)						).max()
            self.assertLess(__lowerCAmelCase		,       1E-4						)



