



'''simple docstring'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_SCREAMING_SNAKE_CASE							:       int						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Tuple							=   "deberta-v2"




			def __init__( self    ,							a__=128_100    ,							a__=1_536    ,							a__=24    ,							a__=24    ,							a__=6_144    ,							a__="gelu"    ,							a__=0.1    ,							a__=0.1    ,							a__=512    ,							a__=0    ,							a__=0.0_2    ,							a__=1e-7    ,							a__=False    ,							a__=-1    ,							a__=0    ,							a__=True    ,							a__=None    ,							a__=0    ,							a__="gelu"    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				intermediate_size
										snake_case_      =				hidden_act
										snake_case_      =				hidden_dropout_prob
										snake_case_      =				attention_probs_dropout_prob
										snake_case_      =				max_position_embeddings
										snake_case_      =				type_vocab_size
										snake_case_      =				initializer_range
										snake_case_      =				relative_attention
										snake_case_      =				max_relative_positions
										snake_case_      =				pad_token_id
										snake_case_      =				position_biased_input

										# Backwards compatibility
										if type(a__							) == str:
																	snake_case_      =				[x.strip() for x in pos_att_type.lower().split("|"							)]

										snake_case_      =				pos_att_type
										snake_case_      =				vocab_size
										snake_case_      =				layer_norm_eps

										snake_case_      =				kwargs.get("pooler_hidden_size"    ,							a__							)
										snake_case_      =				pooler_dropout
										snake_case_      =				pooler_hidden_act




class 			_snake_case		(     lowercase_							):




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

										'''simple docstring'''




										if self.task == "multiple-choice":
																	snake_case_      =				{0: "batch", 1: "choice", 2: "sequence"}
										else:
																	snake_case_      =				{0: "batch", 1: "sequence"}
										if self._config.type_vocab_size > 0:
																	return OrderedDict(
																	    [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]							)
										else:
																	return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										return 12




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							a__ = 3    ,							a__ = 40    ,							a__ = 40    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




										snake_case_      =				super().generate_dummy_inputs(preprocessor=a__    ,							framework=a__							)
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																	del dummy_inputs["token_type_ids"]
										return dummy_inputs










'''simple docstring'''





from random import shuffle

import tensorflow as tf
from numpy import array


def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	Optional[int]					):

							'''simple docstring'''






							snake_case_      =				int(snake_case					)
							assert noofclusters < len(snake_case					)

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

							# Will help select random centroids from among the available vectors
							snake_case_      =				list(range(len(snake_case					)					)					)
							shuffle(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.

							snake_case_      =				tf.Graph()

							with graph.as_default():
														# SESSION OF COMPUTATION

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

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

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

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

														# Initialize all variables
														sess.run(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.
														snake_case_      =				1_0_0
														for _ in range(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(snake_case					)					):
																												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.
																												snake_case_      =				[
																												    sess.run(snake_case							,    feed_dict={va: vect, va: sess.run(snake_case					)}					)
																												    for centroid in centroids
																												]
																												# Now use the cluster assignment node, with the distances
																												# as the input
																												snake_case_      =				sess.run(
																												    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(snake_case					):
																												# Collect all the vectors assigned to this cluster
																												snake_case_      =				[
																												    vectors[i]
																												    for i in range(len(snake_case					)					)
																												    if sess.run(assignments[i]					) == cluster_n
																												]
																												# Compute new centroid location
																												snake_case_      =				sess.run(
																												    snake_case							,    feed_dict={mean_input: array(snake_case					)}					)
																												# Assign value to appropriate variable
																												sess.run(
																												    cent_assigns[cluster_n]							,    feed_dict={centroid_value: new_location}					)

        # Return centroids and assignments
														snake_case_      =				sess.run(snake_case					)
														snake_case_      =				sess.run(snake_case					)
														return centroids, assignments




