





'''simple docstring'''






from random import shuffle

import tensorflow as tf
from numpy import array







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			__snake_case					:      str							      =     int(_lowerCamelCase )
			assert noofclusters < len(_lowerCamelCase )

			# Find out the dimensionality
			__snake_case					:      Tuple							      =     len(vectors[0] )

			# Will help select random centroids from among the available vectors
			__snake_case					:      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.

			__snake_case					:      Dict							      =     tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

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

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

						##Node for computing Euclidean distances
						# Placeholders for input
						__snake_case					:      int							      =     tf.placeholder("""float"""    ,	[dim] )
						__snake_case					:      Optional[int]							      =     tf.placeholder("""float"""    ,	[dim] )
						__snake_case					:      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
						__snake_case					:      Optional[Any]							      =     tf.placeholder("""float"""    ,	[noofclusters] )
						__snake_case					:      Optional[int]							      =     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.
						__snake_case					:      str							      =     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.
						__snake_case					:      int							      =     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 ) ):
												__snake_case					:      Optional[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(_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
												__snake_case					:      Dict							      =     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
												__snake_case					:      str							      =     [
												    vectors[i]
												    for i in range(len(_lowerCamelCase ) )
												    if sess.run(assignments[i] ) == cluster_n
												]
												# Compute new centroid location
												__snake_case					:      str							      =     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
						__snake_case					:      Tuple							      =     sess.run(_lowerCamelCase )
						__snake_case					:      str							      =     sess.run(_lowerCamelCase )
						return centroids, assignments









'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class  _A (					__lowercase		):
		lowercase__:   Any				     =   ['''image_processor''', '''tokenizer''']
		lowercase__:   Any				     =   '''CLIPImageProcessor'''
		lowercase__:   Optional[Any]				     =   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

		def __init__(   self					:   int  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   Dict=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
								    """ instead."""  ,      __magic_name__  ,      )
								__snake_case					:      List[Any]							      =     kwargs.pop("""feature_extractor"""			)

					__snake_case					:      List[str]							      =     image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
								raise ValueError("""You need to specify an `image_processor`."""			)
					if tokenizer is None:
								raise ValueError("""You need to specify a `tokenizer`."""			)

					super().__init__(__magic_name__  ,      __magic_name__			)

		def __call__(   self					:   int  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Any=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Optional[int]:


					"""simple docstring"""

					if text is None and images is None:
								raise ValueError("""You have to specify either text or images. Both cannot be none."""			)

					if text is not None:
								__snake_case					:      int							      =     self.tokenizer(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if images is not None:
								__snake_case					:      str							      =     self.image_processor(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if text is not None and images is not None:
								__snake_case					:      Union[str, Any]							      =     image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					else:
								return BatchEncoding(data=dict(**__magic_name__			)  ,      tensor_type=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]  ,      *__magic_name__					:   List[Any]  ,      **__magic_name__					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					return self.tokenizer.batch_decode(*__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   List[str]  ,      *__magic_name__					:   Tuple  ,      **__magic_name__					:   List[Any]			)					->    int:


					"""simple docstring"""

					return self.tokenizer.decode(*__magic_name__  ,      **__magic_name__			)

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.tokenizer.model_input_names
					__snake_case					:      str							      =     self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)

		@property
		def 	lowercase__     (   self					:   int			)					->    List[str]:


					"""simple docstring"""

					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,      __magic_name__  ,      )
					return self.image_processor_class

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Dict:


					"""simple docstring"""

					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,      __magic_name__  ,      )
					return self.image_processor


