




import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__a          =			logging.get_logger(__name__)

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''linear'''
    lowerCAmelCase        =				'''cosine'''
    lowerCAmelCase        =				'''cosine_with_restarts'''
    lowerCAmelCase        =				'''polynomial'''
    lowerCAmelCase        =				'''constant'''
    lowerCAmelCase        =				'''constant_with_warmup'''
    lowerCAmelCase        =				'''piecewise_constant'''





def lowerCamelCase__       (      _lowercase			,						_lowercase = -1				):

     '''simple docstring'''


     return LambdaLR(_lowercase			,						lambda _lowercase				: 1			,						last_epoch=_lowercase				)





def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase = -1				):

     '''simple docstring'''


     def lr_lambda(_lowercase				):
          if current_step < num_warmup_steps:
               return float(_lowercase				) / float(max(1.0			,						_lowercase				)				)
          return 1.0

     return LambdaLR(_lowercase			,						_lowercase			,						last_epoch=_lowercase				)





def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase = -1				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      {}
     UpperCAmelCase_      :							int       =      step_rules.split(''','''				)
     for rule_str in rule_list[:-1]:
          UpperCAmelCase_      :							Dict       =      rule_str.split(''':'''				)
          UpperCAmelCase_      :							Dict       =      int(_lowercase				)
          UpperCAmelCase_      :							Tuple       =      float(_lowercase				)
          UpperCAmelCase_      :							Optional[int]       =      value
     UpperCAmelCase_      :							Tuple       =      float(rule_list[-1]				)

     def create_rules_function(_lowercase			,						_lowercase				):
          def rule_func(_lowercase				) -> float:
               UpperCAmelCase_      :							Union[str, Any]       =      sorted(rules_dict.keys()				)
               for i, sorted_step in enumerate(_lowercase				):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     UpperCAmelCase_      :							Tuple       =      create_rules_function(_lowercase			,						_lowercase				)

     return LambdaLR(_lowercase			,						_lowercase			,						last_epoch=_lowercase				)





def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase=-1				):

     '''simple docstring'''


     def lr_lambda(_lowercase				):
          if current_step < num_warmup_steps:
               return float(_lowercase				) / float(max(1			,						_lowercase				)				)
          return max(
              0.0			,						float(num_training_steps - current_step				) / float(max(1			,						num_training_steps - num_warmup_steps				)				)				)

     return LambdaLR(_lowercase			,						_lowercase			,						_lowercase				)





def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase = 0.5			,						_lowercase = -1				):

     '''simple docstring'''


     def lr_lambda(_lowercase				):
          if current_step < num_warmup_steps:
               return float(_lowercase				) / float(max(1			,						_lowercase				)				)
          UpperCAmelCase_      :							Dict       =      float(current_step - num_warmup_steps				) / float(max(1			,						num_training_steps - num_warmup_steps				)				)
          return max(0.0			,						0.5 * (1.0 + math.cos(math.pi * float(_lowercase				) * 2.0 * progress				))				)

     return LambdaLR(_lowercase			,						_lowercase			,						_lowercase				)





def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase = 1			,						_lowercase = -1				):

     '''simple docstring'''


     def lr_lambda(_lowercase				):
          if current_step < num_warmup_steps:
               return float(_lowercase				) / float(max(1			,						_lowercase				)				)
          UpperCAmelCase_      :							List[Any]       =      float(current_step - num_warmup_steps				) / float(max(1			,						num_training_steps - num_warmup_steps				)				)
          if progress >= 1.0:
               return 0.0
          return max(0.0			,						0.5 * (1.0 + math.cos(math.pi * ((float(_lowercase				) * progress) % 1.0)				))				)

     return LambdaLR(_lowercase			,						_lowercase			,						_lowercase				)





def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase=1E-7			,						_lowercase=1.0			,						_lowercase=-1				):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      optimizer.defaults['''lr''']
     if not (lr_init > lr_end):
          raise ValueError(f'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''				)

     def lr_lambda(_lowercase				):
          if current_step < num_warmup_steps:
               return float(_lowercase				) / float(max(1			,						_lowercase				)				)
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               UpperCAmelCase_      :							Any       =      lr_init - lr_end
               UpperCAmelCase_      :							Union[str, Any]       =      num_training_steps - num_warmup_steps
               UpperCAmelCase_      :							Optional[Any]       =      1 - (current_step - num_warmup_steps) / decay_steps
               UpperCAmelCase_      :							str       =      lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(_lowercase			,						_lowercase			,						_lowercase				)


__a          =			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}





def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase = None			,						_lowercase = None			,						_lowercase = None			,						_lowercase = 1			,						_lowercase = 1.0			,						_lowercase = -1			,						):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      SchedulerType(_lowercase				)
     UpperCAmelCase_      :							Optional[Any]       =      TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(_lowercase			,						last_epoch=_lowercase				)

     if name == SchedulerType.PIECEWISE_CONSTANT:
          return schedule_func(_lowercase			,						step_rules=_lowercase			,						last_epoch=_lowercase				)

     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
          raise ValueError(f'''{name} requires `num_warmup_steps`, please provide that argument.'''				)

     if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(_lowercase			,						num_warmup_steps=_lowercase			,						last_epoch=_lowercase				)

     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
          raise ValueError(f'''{name} requires `num_training_steps`, please provide that argument.'''				)

     if name == SchedulerType.COSINE_WITH_RESTARTS:
          return schedule_func(
              _lowercase			,						num_warmup_steps=_lowercase			,						num_training_steps=_lowercase			,						num_cycles=_lowercase			,						last_epoch=_lowercase			,						)

     if name == SchedulerType.POLYNOMIAL:
          return schedule_func(
              _lowercase			,						num_warmup_steps=_lowercase			,						num_training_steps=_lowercase			,						power=_lowercase			,						last_epoch=_lowercase			,						)

     return schedule_func(
         _lowercase			,						num_warmup_steps=_lowercase			,						num_training_steps=_lowercase			,						last_epoch=_lowercase				)





from random import shuffle

import tensorflow as tf
from numpy import array







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      int(_lowercase				)
     assert noofclusters < len(_lowercase				)

     # Find out the dimensionality
     UpperCAmelCase_      :							Optional[int]       =      len(vectors[0]				)

     # Will help select random centroids from among the available vectors
     UpperCAmelCase_      :							Optional[int]       =      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.

     UpperCAmelCase_      :							List[str]       =      tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

          UpperCAmelCase_      :							Tuple       =      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(_lowercase				)
          ]
          ##These nodes will assign the centroid Variables the appropriate
          ##values
          UpperCAmelCase_      :							List[Any]       =      tf.placeholder('''float64'''			,						[dim]				)
          UpperCAmelCase_      :							List[Any]       =      []
          for centroid in centroids:
               cent_assigns.append(tf.assign(_lowercase			,						_lowercase				)				)

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

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