


from random import shuffle

import tensorflow as tf
from numpy import array







def 						__lowerCAmelCase		(		lowercase						:						List[Any]		,							lowercase						:						int				)			->		List[Any]:


    """simple docstring"""





    snake_case					:   Dict         =					int(lowercase				)
    assert noofclusters < len(lowercase				)

    # Find out the dimensionality
    snake_case					:   Dict         =					len(vectors[0]				)

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

    # 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.

    snake_case					:   str         =					tf.Graph()

    with graph.as_default():
        # SESSION OF COMPUTATION

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

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

        ##Now lets construct the node that will compute the mean
        # The placeholder for the input
        snake_case					:   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
        snake_case					:   Optional[Any]         =					tf.reduce_mean(lowercase		,							0				)

        ##Node for computing Euclidean distances
        # Placeholders for input
        snake_case					:   List[str]         =					tf.placeholder("float"		,							[dim]				)
        snake_case					:   Any         =					tf.placeholder("float"		,							[dim]				)
        snake_case					:   Any         =					tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase		,							lowercase				)		,							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
        snake_case					:   Tuple         =					tf.placeholder("float"		,							[noofclusters]				)
        snake_case					:   Optional[int]         =					tf.argmin(lowercase		,							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.
        snake_case					:   List[str]         =					tf.initialize_all_variables()

        # Initialize all variables
        sess.run(lowercase				)

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

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









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Optional[Any]													=						'''big_bird'''







   def __init__(			self		,			UpperCamelCase__=5_0358		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu_new"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=4096		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=True		,			UpperCamelCase__=0		,			UpperCamelCase__=1		,			UpperCamelCase__=2		,			UpperCamelCase__=66		,			UpperCamelCase__="block_sparse"		,			UpperCamelCase__=True		,			UpperCamelCase__=False		,			UpperCamelCase__=64		,			UpperCamelCase__=3		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Tuple:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			sep_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)

       snake_case					:   Union[str, Any]         =					vocab_size
       snake_case					:   List[Any]         =					max_position_embeddings
       snake_case					:   int         =					hidden_size
       snake_case					:   str         =					num_hidden_layers
       snake_case					:   Any         =					num_attention_heads
       snake_case					:   int         =					intermediate_size
       snake_case					:   Union[str, Any]         =					hidden_act
       snake_case					:   Optional[Any]         =					hidden_dropout_prob
       snake_case					:   List[Any]         =					attention_probs_dropout_prob
       snake_case					:   int         =					initializer_range
       snake_case					:   List[str]         =					type_vocab_size
       snake_case					:   Optional[Any]         =					layer_norm_eps
       snake_case					:   Optional[Any]         =					use_cache

       snake_case					:   List[Any]         =					rescale_embeddings
       snake_case					:   Any         =					attention_type
       snake_case					:   List[Any]         =					use_bias
       snake_case					:   int         =					block_size
       snake_case					:   int         =					num_random_blocks
       snake_case					:   Optional[int]         =					classifier_dropout







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case					:   int         =					{0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case					:   Optional[Any]         =					{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]		)





