

"""simple docstring"""




from random import shuffle

import tensorflow as tf
from numpy import array



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      List[str]:




   """simple docstring"""
   lowerCamelCase__						=			int(_SCREAMING_SNAKE_CASE   )
   assert noofclusters < len(_SCREAMING_SNAKE_CASE   )

   # Find out the dimensionality
   lowerCamelCase__						=			len(vectors[0]   )

   # Will help select random centroids from among the available vectors
   lowerCamelCase__						=			list(range(len(_SCREAMING_SNAKE_CASE   )   )   )
   shuffle(_SCREAMING_SNAKE_CASE   )

   # 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__						=			tf.Graph()

   with graph.as_default():
      # SESSION OF COMPUTATION

      lowerCamelCase__						=			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__						=			[
          tf.Variable(vectors[vector_indices[i]]   ) for i in range(_SCREAMING_SNAKE_CASE   )
      ]
      ##These nodes will assign the centroid Variables the appropriate
      ##values
      lowerCamelCase__						=			tf.placeholder("float64"	,    [dim]   )
      lowerCamelCase__						=			[]
      for centroid in centroids:
         cent_assigns.append(tf.assign(_SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE   )   )

      ##Variables for cluster assignments of individual vectors(initialized
      ##to 0 at first)
      lowerCamelCase__						=			[tf.Variable(0   ) for i in range(len(_SCREAMING_SNAKE_CASE   )   )]
      ##These nodes will assign an assignment Variable the appropriate
      ##value
      lowerCamelCase__						=			tf.placeholder("int32"   )
      lowerCamelCase__						=			[]
      for assignment in assignments:
         cluster_assigns.append(tf.assign(_SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE   )   )

      ##Now lets construct the node that will compute the mean
      # The placeholder for the input
      lowerCamelCase__						=			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__						=			tf.reduce_mean(_SCREAMING_SNAKE_CASE	,    0   )

      ##Node for computing Euclidean distances
      # Placeholders for input
      lowerCamelCase__						=			tf.placeholder("float"	,    [dim]   )
      lowerCamelCase__						=			tf.placeholder("float"	,    [dim]   )
      lowerCamelCase__						=			tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE   )	,    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__						=			tf.placeholder("float"	,    [noofclusters]   )
      lowerCamelCase__						=			tf.argmin(_SCREAMING_SNAKE_CASE	,    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__						=			tf.initialize_all_variables()

      # Initialize all variables
      sess.run(_SCREAMING_SNAKE_CASE   )

      ##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__						=			100
      for _ in range(_SCREAMING_SNAKE_CASE   ):
         ##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(_SCREAMING_SNAKE_CASE   )   ):
            lowerCamelCase__						=			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__						=			[
                sess.run(_SCREAMING_SNAKE_CASE	,    feed_dict={va: vect, va: sess.run(_SCREAMING_SNAKE_CASE   )}   )
                for centroid in centroids
            ]
            # Now use the cluster assignment node, with the distances
            # as the input
            lowerCamelCase__						=			sess.run(
                _SCREAMING_SNAKE_CASE	,    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(_SCREAMING_SNAKE_CASE   ):
            # Collect all the vectors assigned to this cluster
            lowerCamelCase__						=			[
                vectors[i]
                for i in range(len(_SCREAMING_SNAKE_CASE   )   )
                if sess.run(assignments[i]   ) == cluster_n
            ]
            # Compute new centroid location
            lowerCamelCase__						=			sess.run(
                _SCREAMING_SNAKE_CASE	,    feed_dict={mean_input: array(_SCREAMING_SNAKE_CASE   )}   )
            # Assign value to appropriate variable
            sess.run(
                cent_assigns[cluster_n]	,    feed_dict={centroid_value: new_location}   )

            # Return centroids and assignments
      lowerCamelCase__						=			sess.run(_SCREAMING_SNAKE_CASE   )
      lowerCamelCase__						=			sess.run(_SCREAMING_SNAKE_CASE   )
      return centroids, assignments






"""simple docstring"""




import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard


@slow
@require_flax
class 		_A       (			unittest.TestCase					):





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"bird"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([canny_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"Chef in the kitchen"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([pose_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2




