






from random import shuffle

import tensorflow as tf
from numpy import array

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						__magic_name__      :       int       						= int(lowerCAmelCase__			)
						assert noofclusters < len(lowerCAmelCase__			)

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

						# Will help select random centroids from among the available vectors
						__magic_name__      :       Dict       						= list(range(len(lowerCAmelCase__			)			)			)
						shuffle(lowerCAmelCase__			)

						# 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.

						__magic_name__      :       Any       						= tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

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

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

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

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

												# Initialize all variables
												sess.run(lowerCAmelCase__			)

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

        # Return centroids and assignments
												__magic_name__      :       Optional[int]       						= sess.run(lowerCAmelCase__			)
												__magic_name__      :       Optional[int]       						= sess.run(lowerCAmelCase__			)
												return centroids, assignments








import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xlnet import XLNetTokenizer
else:
		__magic_name__:					Any							=     None


__magic_name__:					Dict							=     logging.get_logger(__name__)

__magic_name__:					Any							=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__magic_name__:					str							=     {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

__magic_name__:					Optional[Any]							=     {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

__magic_name__:					Optional[Any]							=     "▁"

# Segments (not really needed)
__magic_name__:					List[Any]							=     0
__magic_name__:					Dict							=     1
__magic_name__:					List[str]							=     2
__magic_name__:					List[Any]							=     3
__magic_name__:					Optional[int]							=     4





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Dict											=   VOCAB_FILES_NAMES
	lowercase__						:					Union[str, Any]											=   PRETRAINED_VOCAB_FILES_MAP
	lowercase__						:					Tuple											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__						:					List[str]											=   '''left'''
	lowercase__						:					List[str]											=   XLNetTokenizer
	def __init__(    self      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__="<s>"      ,       lowerCAmelCase__="</s>"      ,       lowerCAmelCase__="<unk>"      ,       lowerCAmelCase__="<sep>"      ,       lowerCAmelCase__="<pad>"      ,       lowerCAmelCase__="<cls>"      ,       lowerCAmelCase__="<mask>"      ,       lowerCAmelCase__=["<eop>", "<eod>"]      ,       **lowerCAmelCase__      ,       )							->				Tuple:
							# Mask token behave like a normal word, i.e. include the space before it
							__magic_name__      :       Optional[int]       						= AddedToken(lowerCAmelCase__      ,       lstrip=lowerCAmelCase__      ,       rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			) else mask_token

							super().__init__(
							    vocab_file=lowerCAmelCase__      ,       tokenizer_file=lowerCAmelCase__      ,       do_lower_case=lowerCAmelCase__      ,       remove_space=lowerCAmelCase__      ,       keep_accents=lowerCAmelCase__      ,       bos_token=lowerCAmelCase__      ,       eos_token=lowerCAmelCase__      ,       unk_token=lowerCAmelCase__      ,       sep_token=lowerCAmelCase__      ,       pad_token=lowerCAmelCase__      ,       cls_token=lowerCAmelCase__      ,       mask_token=lowerCAmelCase__      ,       additional_special_tokens=lowerCAmelCase__      ,       **lowerCAmelCase__      ,       )

							__magic_name__      :       List[str]       						= 3
							__magic_name__      :       str       						= do_lower_case
							__magic_name__      :       Union[str, Any]       						= remove_space
							__magic_name__      :       str       						= keep_accents
							__magic_name__      :       Tuple       						= vocab_file
							__magic_name__      :       List[Any]       						= False if not self.vocab_file else True
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
							__magic_name__      :       Any       						= [self.sep_token_id]
							__magic_name__      :       Any       						= [self.cls_token_id]
							if token_ids_a is None:
													return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
							__magic_name__      :       List[str]       						= [self.sep_token_id]
							__magic_name__      :       Optional[Any]       						= [2]

							if token_ids_a is None:
													return len(token_ids_a + sep			) * [0] + cls_segment_id
							return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				Tuple[str]:
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
													    """tokenizer."""			)

							if not os.path.isdir(lowerCAmelCase__			):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
													return
							__magic_name__      :       List[str]       						= os.path.join(
							    lowerCAmelCase__      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

							if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase__			):
													copyfile(self.vocab_file      ,       lowerCAmelCase__			)

							return (out_vocab_file,)
