


"""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      :str	    =		collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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




def   snake_case		(     UpperCamelCase__       :						int    )   ->      Any:
      lowerCamelCase					:					Dict        =	numpy.dtype(numpy.uintaa    ).newbyteorder(""">"""    )
      return numpy.frombuffer(bytestream.read(4    )			,				dtype=UpperCamelCase__    )[0]




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




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




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


class 			A__	:


     """simple docstring"""

     @deprecated(
         __a     ,		"""Please use alternatives such as official/mnist/_DataSet.py"""
         """ from tensorflow/models."""     ,		)
     def __init__(	self:						Tuple     ,		__a:						Tuple     ,		__a:						Dict     ,		__a:						Tuple=False     ,		__a:						int=False     ,		__a:						Optional[int]=dtypes.floataa     ,		__a:						Tuple=True     ,		__a:						int=None     ,		)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					Dict        =	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					:					Optional[Any]        =	10_000
                 lowerCamelCase					:					int        =	one_hot
           else:
                 assert (
                     images.shape[0] == labels.shape[0]
                 ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
                 lowerCamelCase					:					Union[str, Any]        =	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					:					List[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					:					List[str]        =	numpy.multiply(__a     ,		1.0 / 2_55.0	)
           lowerCamelCase					:					Any        =	images
           lowerCamelCase					:					List[str]        =	labels
           lowerCamelCase					:					Dict        =	0
           lowerCamelCase					:					Union[str, Any]        =	0

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

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

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

     @property
     def 		a__						(	self:						Optional[int]	)->    Dict:
           return self._epochs_completed







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




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






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

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

            lowerCamelCase					:					Tuple        =	fake()
            lowerCamelCase					:					List[str]        =	fake()
            lowerCamelCase					:					str        =	fake()
            return _Datasets(train=UpperCamelCase__			,				validation=UpperCamelCase__			,				test=UpperCamelCase__    )

      if not source_url:  # empty string check
            lowerCamelCase					:					List[str]        =	DEFAULT_SOURCE_URL

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

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

      lowerCamelCase					:					Tuple        =	_maybe_download(
          UpperCamelCase__			,				UpperCamelCase__			,				source_url + train_labels_file    )
      with gfile.Open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					int        =	_extract_labels(UpperCamelCase__			,				one_hot=UpperCamelCase__    )

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

      lowerCamelCase					:					List[Any]        =	_maybe_download(
          UpperCamelCase__			,				UpperCamelCase__			,				source_url + test_labels_file    )
      with gfile.Open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					Dict        =	_extract_labels(UpperCamelCase__			,				one_hot=UpperCamelCase__    )

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

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

      lowerCamelCase					:					Union[str, Any]        =	{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

      lowerCamelCase					:					Optional[int]        =	_DataSet(UpperCamelCase__			,				UpperCamelCase__			,				**UpperCamelCase__    )
      lowerCamelCase					:					List[Any]        =	_DataSet(UpperCamelCase__			,				UpperCamelCase__			,				**UpperCamelCase__    )
      lowerCamelCase					:					Union[str, Any]        =	_DataSet(UpperCamelCase__			,				UpperCamelCase__			,				**UpperCamelCase__    )

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








"""simple docstring"""


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__lowerCamelCase      :Any	    =		False


@skip_mps
class 			A__	(						__lowercase ,     __lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=StableDiffusionAttendAndExcitePipeline
     snake_case__	:					Any													=False
     snake_case__	:					Dict													=TEXT_TO_IMAGE_PARAMS
     snake_case__	:					Any													=TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''})
     snake_case__	:					Dict													=TEXT_TO_IMAGE_IMAGE_PARAMS
     snake_case__	:					str													=TEXT_TO_IMAGE_IMAGE_PARAMS

     @classmethod
     def 		a__						(	cls:						Dict	)->    Tuple:
           super().setUpClass()
           torch.use_deterministic_algorithms(__a	)

     @classmethod
     def 		a__						(	cls:						Union[str, Any]	)->    Any:
           super().tearDownClass()
           torch.use_deterministic_algorithms(__a	)

     def 		a__						(	self:						Tuple	)->    Union[str, Any]:
           torch.manual_seed(0	)
           lowerCamelCase					:					str        =	UNetaDConditionModel(
               block_out_channels=(32, 64)     ,		layers_per_block=1     ,		sample_size=32     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,		cross_attention_dim=32     ,		attention_head_dim=(2, 4)     ,		use_linear_projection=__a     ,		)
           lowerCamelCase					:					Union[str, Any]        =	DDIMScheduler(
               beta_start=0.0_00_85     ,		beta_end=0.0_12     ,		beta_schedule="""scaled_linear"""     ,		clip_sample=__a     ,		set_alpha_to_one=__a     ,		)
           torch.manual_seed(0	)
           lowerCamelCase					:					Union[str, Any]        =	AutoencoderKL(
               block_out_channels=[32, 64]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,		latent_channels=4     ,		sample_size=128     ,		)
           torch.manual_seed(0	)
           lowerCamelCase					:					str        =	CLIPTextConfig(
               bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-0_5     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_000     ,		hidden_act="""gelu"""     ,		projection_dim=512     ,		)
           lowerCamelCase					:					Optional[int]        =	CLIPTextModel(__a	)
           lowerCamelCase					:					str        =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

           lowerCamelCase					:					List[str]        =	{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """safety_checker""": None,
               """feature_extractor""": None,
           }

           return components

     def 		a__						(	self:						Tuple     ,		__a:						int     ,		__a:						Union[str, Any]=0	)->    Optional[Any]:
           if str(__a	).startswith("""mps"""	):
                 lowerCamelCase					:					Tuple        =	torch.manual_seed(__a	)
           else:
                 lowerCamelCase					:					str        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					Dict        =	{
               """prompt""": """a cat and a frog""",
               """token_indices""": [2, 5],
               """generator""": generator,
               """num_inference_steps""": 1,
               """guidance_scale""": 6.0,
               """output_type""": """numpy""",
               """max_iter_to_alter""": 2,
               """thresholds""": {0: 0.7},
           }
           return inputs

     def 		a__						(	self:						Dict	)->    str:
           lowerCamelCase					:					Tuple        =	"""cpu"""

           lowerCamelCase					:					List[str]        =	self.get_dummy_components()
           lowerCamelCase					:					List[Any]        =	self.pipeline_class(**__a	)
           pipe.to(__a	)
           pipe.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Any        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					Union[str, Any]        =	pipe(**__a	).images
           lowerCamelCase					:					Tuple        =	image[0, -3:, -3:, -1]

           self.assertEqual(image.shape     ,		(1, 64, 64, 3)	)
           lowerCamelCase					:					Optional[Any]        =	np.array(
               [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96]	)
           lowerCamelCase					:					Optional[Any]        =	np.abs(image_slice.flatten() - expected_slice	).max()
           self.assertLessEqual(__a     ,		1e-3	)

     def 		a__						(	self:						int	)->    Optional[Any]:
           super().test_cpu_offload_forward_pass(expected_max_diff=5e-4	)

     def 		a__						(	self:						Union[str, Any]	)->    Optional[int]:
           # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
           self._test_inference_batch_consistent(batch_sizes=[1, 2]	)

     def 		a__						(	self:						Tuple	)->    int:
           self._test_inference_batch_single_identical(batch_size=2     ,		expected_max_diff=7e-4	)

     def 		a__						(	self:						Dict	)->    List[Any]:
           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3	)

     def 		a__						(	self:						Optional[int]	)->    Dict:
           super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4	)

     def 		a__						(	self:						Any	)->    Tuple:
           super().test_save_load_local(expected_max_difference=5e-4	)







     def 		a__						(	self:						str	)->    str:
           super().test_save_load_optional_components(expected_max_difference=4e-4	)


@require_torch_gpu
@slow
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     @classmethod
     def 		a__						(	cls:						Any	)->    Tuple:
           super().setUpClass()
           torch.use_deterministic_algorithms(__a	)

     @classmethod
     def 		a__						(	cls:						Dict	)->    Optional[int]:
           super().tearDownClass()
           torch.use_deterministic_algorithms(__a	)

     def 		a__						(	self:						int	)->    Optional[int]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







     def 		a__						(	self:						int	)->    Optional[Any]:
           lowerCamelCase					:					List[Any]        =	torch.manual_seed(51	)

           lowerCamelCase					:					List[str]        =	StableDiffusionAttendAndExcitePipeline.from_pretrained(
               """CompVis/stable-diffusion-v1-4"""     ,		safety_checker=__a     ,		torch_dtype=torch.floataa	)
           pipe.to("""cuda"""	)

           lowerCamelCase					:					Dict        =	"""a painting of an elephant with glasses"""
           lowerCamelCase					:					Any        =	[5, 7]

           lowerCamelCase					:					Tuple        =	pipe(
               prompt=__a     ,		token_indices=__a     ,		guidance_scale=7.5     ,		generator=__a     ,		num_inference_steps=5     ,		max_iter_to_alter=5     ,		output_type="""numpy"""     ,		).images[0]

           lowerCamelCase					:					Union[str, Any]        =	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""	)
           assert np.abs((expected_image - image).max()	) < 5e-1




