




from random import shuffle

import tensorflow as tf
from numpy import array

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	Tuple							)					-> Any:
      SCREAMING_SNAKE_CASE_							=			int(A__							)
      assert noofclusters < len(A__							)

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

      # Will help select random centroids from among the available vectors
      SCREAMING_SNAKE_CASE_							=			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.

      SCREAMING_SNAKE_CASE_							=			tf.Graph()

      with graph.as_default():
            # SESSION OF COMPUTATION

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

            ##Variables for cluster assignments of individual vectors(initialized
            ##to 0 at first)
            SCREAMING_SNAKE_CASE_							=			[tf.Variable(0							) for i in range(len(A__							)							)]
            ##These nodes will assign an assignment Variable the appropriate
            ##value
            SCREAMING_SNAKE_CASE_							=			tf.placeholder('int32'							)
            SCREAMING_SNAKE_CASE_							=			[]
            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
            SCREAMING_SNAKE_CASE_							=			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
            SCREAMING_SNAKE_CASE_							=			tf.reduce_mean(A__			,			0							)

            ##Node for computing Euclidean distances
            # Placeholders for input
            SCREAMING_SNAKE_CASE_							=			tf.placeholder('float'			,			[dim]							)
            SCREAMING_SNAKE_CASE_							=			tf.placeholder('float'			,			[dim]							)
            SCREAMING_SNAKE_CASE_							=			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
            SCREAMING_SNAKE_CASE_							=			tf.placeholder('float'			,			[noofclusters]							)
            SCREAMING_SNAKE_CASE_							=			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.
            SCREAMING_SNAKE_CASE_							=			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.
            SCREAMING_SNAKE_CASE_							=			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__							)							):
                        SCREAMING_SNAKE_CASE_							=			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.
                        SCREAMING_SNAKE_CASE_							=			[
                            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
                        SCREAMING_SNAKE_CASE_							=			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
                        SCREAMING_SNAKE_CASE_							=			[
                            vectors[i]
                            for i in range(len(A__							)							)
                            if sess.run(assignments[i]							) == cluster_n
                        ]
                        # Compute new centroid location
                        SCREAMING_SNAKE_CASE_							=			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
            SCREAMING_SNAKE_CASE_							=			sess.run(A__							)
            SCREAMING_SNAKE_CASE_							=			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_						)



