
from random import shuffle

import tensorflow as tf
from numpy import array






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	int(__UpperCAmelCase				)
			assert noofclusters < len(__UpperCAmelCase				)

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

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

			# 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(__UpperCAmelCase				)
						]
						##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(__UpperCAmelCase    ,							__UpperCAmelCase				)				)

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

						##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(__UpperCAmelCase    ,							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(__UpperCAmelCase    ,							__UpperCAmelCase				)    ,							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(__UpperCAmelCase    ,							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(__UpperCAmelCase				)

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

        # Return centroids and assignments
						lowerCAmelCase_						=	sess.run(__UpperCAmelCase				)
						lowerCAmelCase_						=	sess.run(__UpperCAmelCase				)
						return centroids, assignments




"""simple docstring"""






from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A			   = logging.get_logger(__name__)

__A			   = {
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[Any]						    = "efficientformer"






    def __init__(   self    ,			_UpperCAmelCase = [3, 2, 6, 4]    ,			_UpperCAmelCase = [48, 96, 224, 448]    ,			_UpperCAmelCase = [True, True, True, True]    ,			_UpperCAmelCase = 448    ,			_UpperCAmelCase = 32    ,			_UpperCAmelCase = 4    ,			_UpperCAmelCase = 7    ,			_UpperCAmelCase = 5    ,			_UpperCAmelCase = 8    ,			_UpperCAmelCase = 4    ,			_UpperCAmelCase = 0.0    ,			_UpperCAmelCase = 16    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 2    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = 0.0    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = 1e-5    ,			_UpperCAmelCase = "gelu"    ,			_UpperCAmelCase = 0.02    ,			_UpperCAmelCase = 1e-1_2    ,			_UpperCAmelCase = 224    ,			_UpperCAmelCase = 1e-0_5    ,			**_UpperCAmelCase    ,			):
       super().__init__(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      hidden_act
       lowercase__:   Union[str, Any]					=      hidden_dropout_prob
       lowercase__:   Union[str, Any]					=      hidden_sizes
       lowercase__:   Any					=      num_hidden_layers
       lowercase__:   int					=      num_attention_heads
       lowercase__:   Dict					=      initializer_range
       lowercase__:   Optional[int]					=      layer_norm_eps
       lowercase__:   int					=      patch_size
       lowercase__:   int					=      num_channels
       lowercase__:   str					=      depths
       lowercase__:   List[str]					=      mlp_expansion_ratio
       lowercase__:   List[str]					=      downsamples
       lowercase__:   List[str]					=      dim
       lowercase__:   Optional[Any]					=      key_dim
       lowercase__:   Union[str, Any]					=      attention_ratio
       lowercase__:   Any					=      resolution
       lowercase__:   Any					=      pool_size
       lowercase__:   List[Any]					=      downsample_patch_size
       lowercase__:   Optional[int]					=      downsample_stride
       lowercase__:   Union[str, Any]					=      downsample_pad
       lowercase__:   List[Any]					=      drop_path_rate
       lowercase__:   Optional[Any]					=      num_metaad_blocks
       lowercase__:   Any					=      distillation
       lowercase__:   Optional[int]					=      use_layer_scale
       lowercase__:   List[str]					=      layer_scale_init_value
       lowercase__:   Dict					=      image_size
       lowercase__:   List[str]					=      batch_norm_eps
