
from random import shuffle

import tensorflow as tf
from numpy import array



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						int(lowerCAmelCase__		)
					assert noofclusters < len(lowerCAmelCase__		)

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

					# Will help select random centroids from among the available vectors
					lowercase							=						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.

					lowercase							=						tf.Graph()

					with graph.as_default():
										# SESSION OF COMPUTATION

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

										##Variables for cluster assignments of individual vectors(initialized
										##to 0 at first)
										lowercase							=						[tf.Variable(0		) for i in range(len(lowerCAmelCase__		)		)]
										##These nodes will assign an assignment Variable the appropriate
										##value
										lowercase							=						tf.placeholder('''int32'''		)
										lowercase							=						[]
										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
										lowercase							=						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
										lowercase							=						tf.reduce_mean(lowerCAmelCase__ ,			0		)

										##Node for computing Euclidean distances
										# Placeholders for input
										lowercase							=						tf.placeholder('''float''' ,			[dim]		)
										lowercase							=						tf.placeholder('''float''' ,			[dim]		)
										lowercase							=						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
										lowercase							=						tf.placeholder('''float''' ,			[noofclusters]		)
										lowercase							=						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.
										lowercase							=						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.
										lowercase							=						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__		)		):
																				lowercase							=						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.
																				lowercase							=						[
																				    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
																				lowercase							=						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
																				lowercase							=						[
																				    vectors[i]
																				    for i in range(len(lowerCAmelCase__		)		)
																				    if sess.run(assignments[i]		) == cluster_n
																				]
																				# Compute new centroid location
																				lowercase							=						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
										lowercase							=						sess.run(lowerCAmelCase__		)
										lowercase							=						sess.run(lowerCAmelCase__		)
										return centroids, assignments









'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							batch['labels'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase_						,			lowerCAmelCase_  ):
										max_lens.append(max(lowerCAmelCase_						,			lowerCAmelCase_  )  )
						else:
								max_lens.extend(lowerCAmelCase_  )
				return max_lens

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							get_lens(lowerCAmelCase_  )
		pickle_save(lowerCAmelCase_						,			train_ds.len_file  )
		pickle_save(lowerCAmelCase_						,			val_ds.len_file  )


if __name__ == "__main__":
							fire.Fire(save_len_file)




