






'''simple docstring'''

from random import shuffle

import tensorflow as tf
from numpy import array



def 							snake_case					(     a_							: int    ,   a_							: str	)       ->				Optional[Any]:





						"""simple docstring"""

						UpperCamelCase_				:   str					    =							int(a__	)
						assert noofclusters < len(a__	)

						# Find out the dimensionality
						UpperCamelCase_				:   str					    =							len(vectors[0]	)

						# Will help select random centroids from among the available vectors
						UpperCamelCase_				:   int					    =							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.

						UpperCamelCase_				:   Optional[Any]					    =							tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

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

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

												##Node for computing Euclidean distances
												# Placeholders for input
												UpperCamelCase_				:   Optional[int]					    =							tf.placeholder("""float"""    ,   [dim]	)
												UpperCamelCase_				:   Union[str, Any]					    =							tf.placeholder("""float"""    ,   [dim]	)
												UpperCamelCase_				:   Union[str, Any]					    =							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
												UpperCamelCase_				:   Tuple					    =							tf.placeholder("""float"""    ,   [noofclusters]	)
												UpperCamelCase_				:   Tuple					    =							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.
												UpperCamelCase_				:   Any					    =							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.
												UpperCamelCase_				:   str					    =							100
												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__	)	):
																								UpperCamelCase_				:   List[str]					    =							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.
																								UpperCamelCase_				:   Optional[int]					    =							[
																								    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
																								UpperCamelCase_				:   Union[str, Any]					    =							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
																								UpperCamelCase_				:   Optional[Any]					    =							[
																								    vectors[i]
																								    for i in range(len(a__	)	)
																								    if sess.run(assignments[i]	) == cluster_n
																								]
																								# Compute new centroid location
																								UpperCamelCase_				:   List[str]					    =							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
												UpperCamelCase_				:   int					    =							sess.run(a__	)
												UpperCamelCase_				:   int					    =							sess.run(a__	)
												return centroids, assignments














'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase							={
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase							=[
          "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecAudioForAudioFrameClassification",
          "Data2VecAudioForCTC",
          "Data2VecAudioForSequenceClassification",
          "Data2VecAudioForXVector",
          "Data2VecAudioModel",
          "Data2VecAudioPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecTextForCausalLM",
          "Data2VecTextForMaskedLM",
          "Data2VecTextForMultipleChoice",
          "Data2VecTextForQuestionAnswering",
          "Data2VecTextForSequenceClassification",
          "Data2VecTextForTokenClassification",
          "Data2VecTextModel",
          "Data2VecTextPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecVisionForImageClassification",
          "Data2VecVisionForMaskedImageModeling",
          "Data2VecVisionForSemanticSegmentation",
          "Data2VecVisionModel",
          "Data2VecVisionPreTrainedModel",
      ]

if is_tf_available():
      UpperCamelCase							=[
          "TFData2VecVisionForImageClassification",
          "TFData2VecVisionForSemanticSegmentation",
          "TFData2VecVisionModel",
          "TFData2VecVisionPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






