





"""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_								=		collections.namedtuple("_Datasets", ["train", "validation", "test"])

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



def      __lowerCamelCase   (							a_     :		Optional[Any]							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  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_     :		Optional[Any]							)			->      int:
      print('''Extracting''' ,   f.name							)
      with gzip.GzipFile(fileobj=a_							) as bytestream:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  _readaa(a_							)
            if magic != 20_51:
                  raise ValueError(
                      '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)							)
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  _readaa(a_							)
            __SCREAMING_SNAKE_CASE   :List[Any]						=  _readaa(a_							)
            __SCREAMING_SNAKE_CASE   :List[str]						=  _readaa(a_							)
            __SCREAMING_SNAKE_CASE   :Any						=  bytestream.read(rows * cols * num_images							)
            __SCREAMING_SNAKE_CASE   :List[str]						=  numpy.frombuffer(a_ ,   dtype=numpy.uinta							)
            __SCREAMING_SNAKE_CASE   :List[str]						=  data.reshape(a_ ,   a_ ,   a_ ,   1							)
            return data



@deprecated(a_ ,   '''Please use tf.one_hot on tensors.'''							)
def      __lowerCamelCase   (							a_     :		Any ,   a_     :		Union[str, Any]							)			->      Optional[Any]:
      __SCREAMING_SNAKE_CASE   :int						=  labels_dense.shape[0]
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  numpy.arange(a_							) * num_classes
      __SCREAMING_SNAKE_CASE   :Any						=  numpy.zeros((num_labels, num_classes)							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  1
      return labels_one_hot



@deprecated(a_ ,   '''Please use tf.data to implement this functionality.'''							)
def      __lowerCamelCase   (							a_     :		Tuple ,   a_     :		Optional[int]=False ,   a_     :		List[str]=10							)			->      List[Any]:
      print('''Extracting''' ,   f.name							)
      with gzip.GzipFile(fileobj=a_							) as bytestream:
            __SCREAMING_SNAKE_CASE   :str						=  _readaa(a_							)
            if magic != 20_49:
                  raise ValueError(
                      '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)							)
            __SCREAMING_SNAKE_CASE   :Any						=  _readaa(a_							)
            __SCREAMING_SNAKE_CASE   :int						=  bytestream.read(a_							)
            __SCREAMING_SNAKE_CASE   :str						=  numpy.frombuffer(a_ ,   dtype=numpy.uinta							)
            if one_hot:
                  return _dense_to_one_hot(a_ ,   a_							)
            return labels






class 					_SCREAMING_SNAKE_CASE:




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Any						=  random_seed.get_seed(SCREAMING_SNAKE_CASE__							)
             # If op level seed is not set, use whatever graph level seed is returned
             numpy.random.seed(seeda if seed is None else seeda							)
             __SCREAMING_SNAKE_CASE   :int						=  dtypes.as_dtype(SCREAMING_SNAKE_CASE__							).base_dtype
             if dtype not in (dtypes.uinta, dtypes.floataa):
                   raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype							)
             if fake_data:
                   __SCREAMING_SNAKE_CASE   :int						=  1_00_00
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  one_hot
             else:
                   assert (
                       images.shape[0] == labels.shape[0]
                   ), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
                   __SCREAMING_SNAKE_CASE   :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
                         __SCREAMING_SNAKE_CASE   :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].
                         __SCREAMING_SNAKE_CASE   :Optional[int]						=  images.astype(numpy.floataa							)
                         __SCREAMING_SNAKE_CASE   :Optional[int]						=  numpy.multiply(SCREAMING_SNAKE_CASE__		,1.0 / 2_5_5.0							)
             __SCREAMING_SNAKE_CASE   :Any						=  images
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  labels
             __SCREAMING_SNAKE_CASE   :Any						=  0
             __SCREAMING_SNAKE_CASE   :Any						=  0




       @property
       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             return self._images




       @property
       def     _UpperCamelCase     (						self							)			->      Optional[Any]:





             """simple docstring"""






             return self._labels




       @property
       def     _UpperCamelCase     (						self							)			->      Union[str, Any]:





             """simple docstring"""






             return self._num_examples




       @property
       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






             return self._epochs_completed




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True							)			->      List[str]:





             """simple docstring"""






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




@deprecated(a_ ,   '''Please write your own downloading logic.'''							)
def      __lowerCamelCase   (							a_     :		int ,   a_     :		Dict ,   a_     :		Any							)			->      Tuple:
      if not gfile.Exists(a_							):
            gfile.MakeDirs(a_							)
      __SCREAMING_SNAKE_CASE   :int						=  os.path.join(a_ ,   a_							)
      if not gfile.Exists(a_							):
            urllib.request.urlretrieve(a_ ,   a_							)  # noqa: S310
            with gfile.GFile(a_							) as f:
                  __SCREAMING_SNAKE_CASE   :Dict						=  f.size()
            print('''Successfully downloaded''' ,   a_ ,   a_ ,   '''bytes.'''							)
      return filepath







@deprecated(
    a_ ,   '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''							)
def      __lowerCamelCase   (							a_     :		Tuple ,   a_     :		List[Any]=False ,   a_     :		str=False ,   a_     :		Optional[Any]=dtypes.floataa ,   a_     :		List[Any]=True ,   a_     :		Optional[Any]=50_00 ,   a_     :		Optional[Any]=None ,   a_     :		Dict=DEFAULT_SOURCE_URL ,   )			->      int:
      if fake_data:

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

            __SCREAMING_SNAKE_CASE   :str						=  fake()
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  fake()
            __SCREAMING_SNAKE_CASE   :Any						=  fake()
            return _Datasets(train=a_ ,   validation=a_ ,   test=a_							)

      if not source_url:  # empty string check
            __SCREAMING_SNAKE_CASE   :Any						=  DEFAULT_SOURCE_URL

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  '''train-images-idx3-ubyte.gz'''
      __SCREAMING_SNAKE_CASE   :str						=  '''train-labels-idx1-ubyte.gz'''
      __SCREAMING_SNAKE_CASE   :Dict						=  '''t10k-images-idx3-ubyte.gz'''
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  '''t10k-labels-idx1-ubyte.gz'''

      __SCREAMING_SNAKE_CASE   :Tuple						=  _maybe_download(
          a_ ,   a_ ,   source_url + train_images_file							)
      with gfile.Open(a_ ,   '''rb'''							) as f:
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  _extract_images(a_							)

      __SCREAMING_SNAKE_CASE   :Any						=  _maybe_download(
          a_ ,   a_ ,   source_url + train_labels_file							)
      with gfile.Open(a_ ,   '''rb'''							) as f:
            __SCREAMING_SNAKE_CASE   :Any						=  _extract_labels(a_ ,   one_hot=a_							)

      __SCREAMING_SNAKE_CASE   :Tuple						=  _maybe_download(
          a_ ,   a_ ,   source_url + test_images_file							)
      with gfile.Open(a_ ,   '''rb'''							) as f:
            __SCREAMING_SNAKE_CASE   :Dict						=  _extract_images(a_							)

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  _maybe_download(
          a_ ,   a_ ,   source_url + test_labels_file							)
      with gfile.Open(a_ ,   '''rb'''							) as f:
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  _extract_labels(a_ ,   one_hot=a_							)

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

      __SCREAMING_SNAKE_CASE   :Any						=  train_images[:validation_size]
      __SCREAMING_SNAKE_CASE   :List[str]						=  train_labels[:validation_size]
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  train_images[validation_size:]
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  train_labels[validation_size:]

      __SCREAMING_SNAKE_CASE   :Tuple						=  {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

      __SCREAMING_SNAKE_CASE   :List[Any]						=  _DataSet(a_ ,   a_ ,   **a_							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  _DataSet(a_ ,   a_ ,   **a_							)
      __SCREAMING_SNAKE_CASE   :str						=  _DataSet(a_ ,   a_ ,   **a_							)

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






"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 					_SCREAMING_SNAKE_CASE(   A    ,	unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  ConsistencyModelPipeline
       SCREAMING_SNAKE_CASE_   :      Any							=  UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       SCREAMING_SNAKE_CASE_   :      Dict							=  UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

       # Override required_optional_params to remove num_images_per_prompt
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  frozenset(
           [
               '''num_inference_steps''',
               '''generator''',
               '''latents''',
               '''output_type''',
               '''return_dict''',
               '''callback''',
               '''callback_steps''',
           ]  )




       @property
       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  UNetaDModel.from_pretrained(
                 '''diffusers/consistency-models-test'''		,subfolder='''test_unet'''		,)
             return unet




       @property
       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  UNetaDModel.from_pretrained(
                 '''diffusers/consistency-models-test'''		,subfolder='''test_unet_class_cond'''		,)
             return unet




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__=False							)			->      Union[str, Any]:





             """simple docstring"""






             if class_cond:
                   __SCREAMING_SNAKE_CASE   :str						=  self.dummy_cond_unet
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.dummy_uncond_unet

             # Default to CM multistep sampler
             __SCREAMING_SNAKE_CASE   :List[str]						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)

             __SCREAMING_SNAKE_CASE   :List[str]						=  {
                 '''unet''': unet,
                 '''scheduler''': scheduler,
             }

             return components




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0							)			->      Dict:





             """simple docstring"""






             if str(SCREAMING_SNAKE_CASE__							).startswith('''mps'''							):
                   __SCREAMING_SNAKE_CASE   :Tuple						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Dict						=  {
                 '''batch_size''': 1,
                 '''num_inference_steps''': None,
                 '''timesteps''': [22, 0],
                 '''generator''': generator,
                 '''output_type''': '''np''',
             }

             return inputs




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __SCREAMING_SNAKE_CASE   :List[str]						=  self.get_dummy_components()
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  pipe.to(SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Tuple						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 32, 32, 3)

             __SCREAMING_SNAKE_CASE   :List[str]						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :Any						=  np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  pipe.to(SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  0
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 32, 32, 3)

             __SCREAMING_SNAKE_CASE   :Dict						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :List[Any]						=  np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __SCREAMING_SNAKE_CASE   :Tuple						=  self.get_dummy_components()
             __SCREAMING_SNAKE_CASE   :Any						=  ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  pipe.to(SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  1
             __SCREAMING_SNAKE_CASE   :List[str]						=  None
             __SCREAMING_SNAKE_CASE   :List[str]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 32, 32, 3)

             __SCREAMING_SNAKE_CASE   :List[str]						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :int						=  np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __SCREAMING_SNAKE_CASE   :Any						=  self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  pipe.to(SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  1
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  None
             __SCREAMING_SNAKE_CASE   :List[Any]						=  0
             __SCREAMING_SNAKE_CASE   :Any						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 32, 32, 3)

             __SCREAMING_SNAKE_CASE   :int						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3






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




       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__="cpu"		,SCREAMING_SNAKE_CASE__=torch.floataa		,SCREAMING_SNAKE_CASE__=(1, 3, 64, 64)							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Tuple						=  {
                 '''num_inference_steps''': None,
                 '''timesteps''': [22, 0],
                 '''class_labels''': 0,
                 '''generator''': generator,
                 '''output_type''': '''np''',
             }

             if get_fixed_latents:
                   __SCREAMING_SNAKE_CASE   :int						=  self.get_fixed_latents(seed=SCREAMING_SNAKE_CASE__		,device=SCREAMING_SNAKE_CASE__		,dtype=SCREAMING_SNAKE_CASE__		,shape=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE   :int						=  latents

             return inputs




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__="cpu"		,SCREAMING_SNAKE_CASE__=torch.floataa		,SCREAMING_SNAKE_CASE__=(1, 3, 64, 64)							)			->      int:





             """simple docstring"""






             if type(SCREAMING_SNAKE_CASE__							) == str:
                   __SCREAMING_SNAKE_CASE   :int						=  torch.device(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  randn_tensor(SCREAMING_SNAKE_CASE__		,generator=SCREAMING_SNAKE_CASE__		,device=SCREAMING_SNAKE_CASE__		,dtype=SCREAMING_SNAKE_CASE__							)
             return latents




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  UNetaDModel.from_pretrained('''diffusers/consistency_models'''		,subfolder='''diffusers_cd_imagenet64_l2'''							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)
             __SCREAMING_SNAKE_CASE   :Dict						=  ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             pipe.to(torch_device=SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :int						=  self.get_inputs()
             __SCREAMING_SNAKE_CASE   :List[str]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 64, 64, 3)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  image[0, -3:, -3:, -1]

             __SCREAMING_SNAKE_CASE   :Dict						=  np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 2E-2




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  UNetaDModel.from_pretrained('''diffusers/consistency_models'''		,subfolder='''diffusers_cd_imagenet64_l2'''							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)
             __SCREAMING_SNAKE_CASE   :Any						=  ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             pipe.to(torch_device=SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.get_inputs()
             __SCREAMING_SNAKE_CASE   :int						=  1
             __SCREAMING_SNAKE_CASE   :int						=  None
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 64, 64, 3)

             __SCREAMING_SNAKE_CASE   :str						=  image[0, -3:, -3:, -1]

             __SCREAMING_SNAKE_CASE   :List[str]						=  np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 2E-2




       @require_torch_a
       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  UNetaDModel.from_pretrained('''diffusers/consistency_models'''		,subfolder='''diffusers_cd_imagenet64_l2'''							)
             __SCREAMING_SNAKE_CASE   :Any						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)
             __SCREAMING_SNAKE_CASE   :Any						=  ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             pipe.to(torch_device=SCREAMING_SNAKE_CASE__		,torch_dtype=torch.floataa							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__		,device=SCREAMING_SNAKE_CASE__							)
             # Ensure usage of flash attention in torch 2.0
             with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__		,enable_math=SCREAMING_SNAKE_CASE__		,enable_mem_efficient=SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 64, 64, 3)

             __SCREAMING_SNAKE_CASE   :List[str]						=  image[0, -3:, -3:, -1]

             __SCREAMING_SNAKE_CASE   :List[Any]						=  np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




       @require_torch_a
       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  UNetaDModel.from_pretrained('''diffusers/consistency_models'''		,subfolder='''diffusers_cd_imagenet64_l2'''							)
             __SCREAMING_SNAKE_CASE   :Dict						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)
             __SCREAMING_SNAKE_CASE   :int						=  ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             pipe.to(torch_device=SCREAMING_SNAKE_CASE__		,torch_dtype=torch.floataa							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Dict						=  self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__		,device=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  1
             __SCREAMING_SNAKE_CASE   :int						=  None
             # Ensure usage of flash attention in torch 2.0
             with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__		,enable_math=SCREAMING_SNAKE_CASE__		,enable_mem_efficient=SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE   :str						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 64, 64, 3)

             __SCREAMING_SNAKE_CASE   :str						=  image[0, -3:, -3:, -1]

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3