



'''simple docstring'''





from random import shuffle

import tensorflow as tf
from numpy import array

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Dict:
  __lowerCamelCase       :       Optional[Any]           = int(_lowerCAmelCase       )
  assert noofclusters < len(_lowerCAmelCase       )

  # Find out the dimensionality
  __lowerCamelCase       :       Union[str, Any]           = len(vectors[0]       )

  # Will help select random centroids from among the available vectors
  __lowerCamelCase       :       List[str]           = list(range(len(_lowerCAmelCase       )       )       )
  shuffle(_lowerCAmelCase       )

  # GRAPH OF COMPUTATION
  # We initialize a new graph and set it as the default during each run
  # of this algorithm. This ensures that as this function is called
  # multiple times, the default graph doesn't keep getting crowded with
  # unused ops and Variables from previous function calls.

  __lowerCamelCase       :       Tuple           = tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    __lowerCamelCase       :       Optional[Any]           = tf.Session()

    ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

    ##First lets ensure we have a Variable vector for each centroid,
    ##initialized to one of the vectors from the available data points
    __lowerCamelCase       :       List[str]           = [
        tf.Variable(vectors[vector_indices[i]]       ) for i in range(_lowerCAmelCase       )
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    __lowerCamelCase       :       str           = tf.placeholder('float64'       ,[dim]       )
    __lowerCamelCase       :       str           = []
    for centroid in centroids:
      cent_assigns.append(tf.assign(_lowerCAmelCase       ,_lowerCAmelCase       )       )

    ##Variables for cluster assignments of individual vectors(initialized
    ##to 0 at first)
    __lowerCamelCase       :       str           = [tf.Variable(0       ) for i in range(len(_lowerCAmelCase       )       )]
    ##These nodes will assign an assignment Variable the appropriate
    ##value
    __lowerCamelCase       :       Union[str, Any]           = tf.placeholder('int32'       )
    __lowerCamelCase       :       Optional[int]           = []
    for assignment in assignments:
      cluster_assigns.append(tf.assign(_lowerCAmelCase       ,_lowerCAmelCase       )       )

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    __lowerCamelCase       :       int           = tf.placeholder('float'       ,[None, dim]       )
    # The Node/op takes the input and computes a mean along the 0th
    # dimension, i.e. the list of input vectors
    __lowerCamelCase       :       List[str]           = tf.reduce_mean(_lowerCAmelCase       ,0       )

    ##Node for computing Euclidean distances
    # Placeholders for input
    __lowerCamelCase       :       Dict           = tf.placeholder('float'       ,[dim]       )
    __lowerCamelCase       :       Dict           = tf.placeholder('float'       ,[dim]       )
    __lowerCamelCase       :       Union[str, Any]           = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCAmelCase       ,_lowerCAmelCase       )       ,2       )       )       )

    ##This node will figure out which cluster to assign a vector to,
    ##based on Euclidean distances of the vector from the centroids.
    # Placeholder for input
    __lowerCamelCase       :       str           = tf.placeholder('float'       ,[noofclusters]       )
    __lowerCamelCase       :       str           = tf.argmin(_lowerCAmelCase       ,0       )

    ##INITIALIZING STATE VARIABLES

    ##This will help initialization of all Variables defined with respect
    ##to the graph. The Variable-initializer should be defined after
    ##all the Variables have been constructed, so that each of them
    ##will be included in the initialization.
    __lowerCamelCase       :       List[str]           = tf.initialize_all_variables()

    # Initialize all variables
    sess.run(_lowerCAmelCase       )

    ##CLUSTERING ITERATIONS

    # Now perform the Expectation-Maximization steps of K-Means clustering
    # iterations. To keep things simple, we will only do a set number of
    # iterations, instead of using a Stopping Criterion.
    __lowerCamelCase       :       Optional[Any]           = 100
    for _ in range(_lowerCAmelCase       ):
      ##EXPECTATION STEP
      ##Based on the centroid locations till last iteration, compute
      ##the _expected_ centroid assignments.
      # Iterate over each vector
      for vector_n in range(len(_lowerCAmelCase       )       ):
        __lowerCamelCase       :       Tuple           = vectors[vector_n]
        # Compute Euclidean distance between this vector and each
        # centroid. Remember that this list cannot be named
        #'centroid_distances', since that is the input to the
        # cluster assignment node.
        __lowerCamelCase       :       Tuple           = [
            sess.run(_lowerCAmelCase       ,feed_dict={va: vect, va: sess.run(_lowerCAmelCase       )}       )
            for centroid in centroids
        ]
        # Now use the cluster assignment node, with the distances
        # as the input
        __lowerCamelCase       :       List[str]           = sess.run(
            _lowerCAmelCase       ,feed_dict={centroid_distances: distances}       )
        # Now assign the value to the appropriate state variable
        sess.run(
            cluster_assigns[vector_n]       ,feed_dict={assignment_value: assignment}       )

      ##MAXIMIZATION STEP
      # Based on the expected state computed from the Expectation Step,
      # compute the locations of the centroids so as to maximize the
      # overall objective of minimizing within-cluster Sum-of-Squares
      for cluster_n in range(_lowerCAmelCase       ):
        # Collect all the vectors assigned to this cluster
        __lowerCamelCase       :       int           = [
            vectors[i]
            for i in range(len(_lowerCAmelCase       )       )
            if sess.run(assignments[i]       ) == cluster_n
        ]
        # Compute new centroid location
        __lowerCamelCase       :       Tuple           = sess.run(
            _lowerCAmelCase       ,feed_dict={mean_input: array(_lowerCAmelCase       )}       )
        # Assign value to appropriate variable
        sess.run(
            cent_assigns[cluster_n]       ,feed_dict={centroid_value: new_location}       )

        # Return centroids and assignments
    __lowerCamelCase       :       Dict           = sess.run(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = sess.run(_lowerCAmelCase       )
    return centroids, assignments











'''simple docstring'''





# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=StableDiffusionControlNetImgaImgPipeline
  a_												=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
  a_												=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  a_												=IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}     )
  a_												=IMAGE_TO_IMAGE_IMAGE_PARAMS




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					List[str]:
    torch.manual_seed(0   )
    __lowerCamelCase       :       Tuple           = UNetaDConditionModel(
        block_out_channels=(32, 64)			,	layers_per_block=2			,	sample_size=32			,	in_channels=4			,	out_channels=4			,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,	cross_attention_dim=32			,	)
    torch.manual_seed(0   )
    __lowerCamelCase       :       Any           = ControlNetModel(
        block_out_channels=(32, 64)			,	layers_per_block=2			,	in_channels=4			,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,	cross_attention_dim=32			,	conditioning_embedding_out_channels=(16, 32)			,	)
    torch.manual_seed(0   )
    __lowerCamelCase       :       Any           = DDIMScheduler(
        beta_start=0.00085			,	beta_end=0.012			,	beta_schedule='scaled_linear'			,	clip_sample=_a			,	set_alpha_to_one=_a			,	)
    torch.manual_seed(0   )
    __lowerCamelCase       :       Dict           = 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			,	)
    torch.manual_seed(0   )
    __lowerCamelCase       :       Dict           = CLIPTextConfig(
        bos_token_id=0			,	eos_token_id=2			,	hidden_size=32			,	intermediate_size=37			,	layer_norm_eps=1e-05			,	num_attention_heads=4			,	num_hidden_layers=5			,	pad_token_id=1			,	vocab_size=1000			,	)
    __lowerCamelCase       :       Union[str, Any]           = CLIPTextModel(_a   )
    __lowerCamelCase       :       Any           = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

    __lowerCamelCase       :       int           = {
        'unet': unet,
        'controlnet': controlnet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'safety_checker': None,
        'feature_extractor': None,
    }
    return components




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	Any			,	_a      :	List[str]=0   )					->					List[str]:
    if str(_a   ).startswith('mps'   ):
      __lowerCamelCase       :       Any           = torch.manual_seed(_a   )
    else:
      __lowerCamelCase       :       Any           = torch.Generator(device=_a   ).manual_seed(_a   )

    __lowerCamelCase       :       Union[str, Any]           = 2
    __lowerCamelCase       :       Any           = randn_tensor(
        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,	generator=_a			,	device=torch.device(_a   )			,	)
    __lowerCamelCase       :       Optional[Any]           = floats_tensor(control_image.shape			,	rng=random.Random(_a   )   ).to(_a   )
    __lowerCamelCase       :       List[Any]           = image.cpu().permute(0			,	2			,	3			,	1   )[0]
    __lowerCamelCase       :       int           = Image.fromarray(np.uinta(_a   )   ).convert('RGB'   ).resize((64, 64)   )
    __lowerCamelCase       :       int           = {
        'prompt': 'A painting of a squirrel eating a burger',
        'generator': generator,
        'num_inference_steps': 2,
        'guidance_scale': 6.0,
        'output_type': 'numpy',
        'image': image,
        'control_image': control_image,
    }

    return inputs




  def 						_lowercase     ( self      :	List[str]   )					->					Optional[Any]:
    return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3   )




  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()			,	reason='XFormers attention is only available with CUDA and `xformers` installed'			,	)
  def 						_lowercase     ( self      :	str   )					->					List[str]:
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )




  def 						_lowercase     ( self      :	Any   )					->					List[str]:
    self._test_inference_batch_single_identical(expected_max_diff=2e-3   )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=StableDiffusionControlNetImgaImgPipeline
  a_												=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
  a_												=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  a_												=frozenset([]     )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




  def 						_lowercase     ( self      :	List[Any]   )					->					List[str]:
    torch.manual_seed(0   )
    __lowerCamelCase       :       Any           = UNetaDConditionModel(
        block_out_channels=(32, 64)			,	layers_per_block=2			,	sample_size=32			,	in_channels=4			,	out_channels=4			,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,	cross_attention_dim=32			,	)
    torch.manual_seed(0   )

    def init_weights(_a      :	str   ):
      if isinstance(_a			,	torch.nn.Convad   ):
        torch.nn.init.normal(m.weight   )
        m.bias.data.fill_(1.0   )

    __lowerCamelCase       :       Any           = ControlNetModel(
        block_out_channels=(32, 64)			,	layers_per_block=2			,	in_channels=4			,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,	cross_attention_dim=32			,	conditioning_embedding_out_channels=(16, 32)			,	)
    controlneta.controlnet_down_blocks.apply(_a   )

    torch.manual_seed(0   )
    __lowerCamelCase       :       Any           = ControlNetModel(
        block_out_channels=(32, 64)			,	layers_per_block=2			,	in_channels=4			,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,	cross_attention_dim=32			,	conditioning_embedding_out_channels=(16, 32)			,	)
    controlneta.controlnet_down_blocks.apply(_a   )

    torch.manual_seed(0   )
    __lowerCamelCase       :       List[Any]           = DDIMScheduler(
        beta_start=0.00085			,	beta_end=0.012			,	beta_schedule='scaled_linear'			,	clip_sample=_a			,	set_alpha_to_one=_a			,	)
    torch.manual_seed(0   )
    __lowerCamelCase       :       str           = 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			,	)
    torch.manual_seed(0   )
    __lowerCamelCase       :       Optional[int]           = CLIPTextConfig(
        bos_token_id=0			,	eos_token_id=2			,	hidden_size=32			,	intermediate_size=37			,	layer_norm_eps=1e-05			,	num_attention_heads=4			,	num_hidden_layers=5			,	pad_token_id=1			,	vocab_size=1000			,	)
    __lowerCamelCase       :       Dict           = CLIPTextModel(_a   )
    __lowerCamelCase       :       Optional[Any]           = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

    __lowerCamelCase       :       List[str]           = MultiControlNetModel([controlneta, controlneta]   )

    __lowerCamelCase       :       str           = {
        'unet': unet,
        'controlnet': controlnet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'safety_checker': None,
        'feature_extractor': None,
    }
    return components




  def 						_lowercase     ( self      :	Dict			,	_a      :	int			,	_a      :	Union[str, Any]=0   )					->					Union[str, Any]:
    if str(_a   ).startswith('mps'   ):
      __lowerCamelCase       :       Optional[Any]           = torch.manual_seed(_a   )
    else:
      __lowerCamelCase       :       Union[str, Any]           = torch.Generator(device=_a   ).manual_seed(_a   )

    __lowerCamelCase       :       Any           = 2

    __lowerCamelCase       :       Any           = [
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,	generator=_a			,	device=torch.device(_a   )			,	),
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,	generator=_a			,	device=torch.device(_a   )			,	),
    ]

    __lowerCamelCase       :       List[Any]           = floats_tensor(control_image[0].shape			,	rng=random.Random(_a   )   ).to(_a   )
    __lowerCamelCase       :       Union[str, Any]           = image.cpu().permute(0			,	2			,	3			,	1   )[0]
    __lowerCamelCase       :       Dict           = Image.fromarray(np.uinta(_a   )   ).convert('RGB'   ).resize((64, 64)   )
    __lowerCamelCase       :       Dict           = {
        'prompt': 'A painting of a squirrel eating a burger',
        'generator': generator,
        'num_inference_steps': 2,
        'guidance_scale': 6.0,
        'output_type': 'numpy',
        'image': image,
        'control_image': control_image,
    }

    return inputs




  def 						_lowercase     ( self      :	Tuple   )					->					Union[str, Any]:
    __lowerCamelCase       :       Any           = self.get_dummy_components()
    __lowerCamelCase       :       Any           = self.pipeline_class(**_a   )
    pipe.to(_a   )

    __lowerCamelCase       :       Optional[int]           = 10.0
    __lowerCamelCase       :       Tuple           = 4

    __lowerCamelCase       :       str           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       int           = steps
    __lowerCamelCase       :       List[Any]           = scale
    __lowerCamelCase       :       Optional[Any]           = pipe(**_a   )[0]

    __lowerCamelCase       :       Dict           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       List[str]           = steps
    __lowerCamelCase       :       List[Any]           = scale
    __lowerCamelCase       :       Dict           = pipe(**_a			,	control_guidance_start=0.1			,	control_guidance_end=0.2   )[0]

    __lowerCamelCase       :       Any           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       Union[str, Any]           = steps
    __lowerCamelCase       :       Any           = scale
    __lowerCamelCase       :       Optional[int]           = pipe(**_a			,	control_guidance_start=[0.1, 0.3]			,	control_guidance_end=[0.2, 0.7]   )[0]

    __lowerCamelCase       :       Any           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       Tuple           = steps
    __lowerCamelCase       :       List[Any]           = scale
    __lowerCamelCase       :       List[str]           = pipe(**_a			,	control_guidance_start=0.4			,	control_guidance_end=[0.5, 0.8]   )[0]

    # make sure that all outputs are different
    assert np.sum(np.abs(output_a - output_a   )   ) > 1e-3
    assert np.sum(np.abs(output_a - output_a   )   ) > 1e-3
    assert np.sum(np.abs(output_a - output_a   )   ) > 1e-3




  def 						_lowercase     ( self      :	Optional[Any]   )					->					List[str]:
    return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3   )




  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()			,	reason='XFormers attention is only available with CUDA and `xformers` installed'			,	)
  def 						_lowercase     ( self      :	Dict   )					->					Tuple:
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )




  def 						_lowercase     ( self      :	str   )					->					int:
    self._test_inference_batch_single_identical(expected_max_diff=2e-3   )




  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    __lowerCamelCase       :       int           = self.get_dummy_components()
    __lowerCamelCase       :       List[Any]           = self.pipeline_class(**_a   )
    pipe.to(_a   )
    pipe.set_progress_bar_config(disable=_a   )
    with tempfile.TemporaryDirectory() as tmpdir:
      try:
        # save_pretrained is not implemented for Multi-ControlNet
        pipe.save_pretrained(_a   )
      except NotImplementedError:
        pass






@slow
@require_torch_gpu
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	List[str]   )					->					List[str]:
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[Any]           = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'   )

    __lowerCamelCase       :       List[str]           = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
        'runwayml/stable-diffusion-v1-5'			,	safety_checker=_a			,	controlnet=_a   )
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=_a   )

    __lowerCamelCase       :       Optional[int]           = torch.Generator(device='cpu'   ).manual_seed(0   )
    __lowerCamelCase       :       List[Any]           = 'evil space-punk bird'
    __lowerCamelCase       :       List[str]           = load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'   ).resize((512, 512)   )
    __lowerCamelCase       :       Any           = load_image(
        'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'   ).resize((512, 512)   )

    __lowerCamelCase       :       Dict           = pipe(
        _a			,	_a			,	control_image=_a			,	generator=_a			,	output_type='np'			,	num_inference_steps=50			,	strength=0.6			,	)

    __lowerCamelCase       :       Optional[Any]           = output.images[0]

    assert image.shape == (512, 512, 3)

    __lowerCamelCase       :       List[str]           = load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'   )

    assert np.abs(expected_image - image   ).max() < 9e-2






