






"""simple docstring"""
from random import shuffle

import tensorflow as tf
from numpy import array

def 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : int			     =					int(A__						)
			assert noofclusters < len(A__						)

			# Find out the dimensionality
			UpperCAmelCase_     : Any			     =					len(vectors[0]						)

			# Will help select random centroids from among the available vectors
			UpperCAmelCase_     : int			     =					list(range(len(A__						)						)						)
			shuffle(A__						)

			# 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.

			UpperCAmelCase_     : List[Any]			     =					tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

						UpperCAmelCase_     : 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
						UpperCAmelCase_     : Tuple			     =					[
						    tf.Variable(vectors[vector_indices[i]]						) for i in range(A__						)
						]
						##These nodes will assign the centroid Variables the appropriate
						##values
						UpperCAmelCase_     : Optional[int]			     =					tf.placeholder("float64"	,[dim]						)
						UpperCAmelCase_     : Union[str, Any]			     =					[]
						for centroid in centroids:
									cent_assigns.append(tf.assign(A__	,A__						)						)

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

						##Now lets construct the node that will compute the mean
						# The placeholder for the input
						UpperCAmelCase_     : Tuple			     =					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
						UpperCAmelCase_     : Dict			     =					tf.reduce_mean(A__	,0						)

						##Node for computing Euclidean distances
						# Placeholders for input
						UpperCAmelCase_     : Dict			     =					tf.placeholder("float"	,[dim]						)
						UpperCAmelCase_     : Dict			     =					tf.placeholder("float"	,[dim]						)
						UpperCAmelCase_     : Tuple			     =					tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(A__	,A__						)	,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
						UpperCAmelCase_     : Tuple			     =					tf.placeholder("float"	,[noofclusters]						)
						UpperCAmelCase_     : int			     =					tf.argmin(A__	,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.
						UpperCAmelCase_     : str			     =					tf.initialize_all_variables()

						# Initialize all variables
						sess.run(A__						)

						##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.
						UpperCAmelCase_     : Optional[Any]			     =					1_00
						for _ in range(A__						):
									##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(A__						)						):
												UpperCAmelCase_     : Any			     =					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.
												UpperCAmelCase_     : Tuple			     =					[
												    sess.run(A__	,feed_dict={va: vect, va: sess.run(A__						)}						)
												    for centroid in centroids
												]
												# Now use the cluster assignment node, with the distances
												# as the input
												UpperCAmelCase_     : Optional[int]			     =					sess.run(
												    A__	,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(A__						):
												# Collect all the vectors assigned to this cluster
												UpperCAmelCase_     : Tuple			     =					[
												    vectors[i]
												    for i in range(len(A__						)						)
												    if sess.run(assignments[i]						) == cluster_n
												]
												# Compute new centroid location
												UpperCAmelCase_     : int			     =					sess.run(
												    A__	,feed_dict={mean_input: array(A__						)}						)
												# Assign value to appropriate variable
												sess.run(
												    cent_assigns[cluster_n]	,feed_dict={centroid_value: new_location}						)

        # Return centroids and assignments
						UpperCAmelCase_     : List[str]			     =					sess.run(A__						)
						UpperCAmelCase_     : Union[str, Any]			     =					sess.run(A__						)
						return centroids, assignments











"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''rwkv'''
	__magic_name__	      =				{'''max_position_embeddings''': '''context_length'''}





	def __init__(      self					: str	,	lowerCAmelCase_					: str=50_277	,	lowerCAmelCase_					: Optional[int]=1_024	,	lowerCAmelCase_					: Optional[int]=4_096	,	lowerCAmelCase_					: Union[str, Any]=32	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: int=None	,	lowerCAmelCase_					: List[Any]=1e-5	,	lowerCAmelCase_					: str=0	,	lowerCAmelCase_					: Tuple=0	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Optional[Any]=False	,	lowerCAmelCase_					: Any=True	,	**lowerCAmelCase_					: List[Any]	,	)						->			List[str]:
				UpperCAmelCase_     : Tuple			     =					vocab_size
				UpperCAmelCase_     : List[str]			     =					context_length
				UpperCAmelCase_     : Dict			     =					hidden_size
				UpperCAmelCase_     : Optional[int]			     =					num_hidden_layers
				UpperCAmelCase_     : Optional[int]			     =					attention_hidden_size if attention_hidden_size is not None else hidden_size
				UpperCAmelCase_     : Dict			     =					intermediate_size if intermediate_size is not None else 4 * hidden_size
				UpperCAmelCase_     : Any			     =					layer_norm_epsilon
				UpperCAmelCase_     : List[Any]			     =					rescale_every
				UpperCAmelCase_     : List[str]			     =					use_cache

				UpperCAmelCase_     : List[str]			     =					bos_token_id
				UpperCAmelCase_     : Union[str, Any]			     =					eos_token_id

				super().__init__(
				    tie_word_embeddings=lowerCAmelCase_	,	bos_token_id=lowerCAmelCase_	,	eos_token_id=lowerCAmelCase_	,	**lowerCAmelCase_						)



