




'''simple docstring'''





import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    "files"							,    [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]							,    )
def 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	Union[str, Any]					):

							'''simple docstring'''






							snake_case_      =				tmp_path_factory.mktemp("dset_infos_dir"					)
							if "full:README.md" in files:
														with open(dataset_infos_dir / "README.md"							,    "w"					) as f:
																					f.write("---\ndataset_info:\n  dataset_size: 42\n---"					)
							if "empty:README.md" in files:
														with open(dataset_infos_dir / "README.md"							,    "w"					) as f:
																					f.write(""					)
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / "dataset_infos.json"							,    "w"					) as f:
																					f.write("{\"default\": {\"dataset_size\": 42}}"					)
							snake_case_      =				DatasetInfosDict.from_directory(snake_case					)
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 4_2


@pytest.mark.parametrize(
    "dataset_info"							,    [
        DatasetInfo(),
        DatasetInfo(
            description="foo"							,    features=Features({"a": Value("int32"					)}					)							,    builder_name="builder"							,    config_name="config"							,    version="1.0.0"							,    splits=[{"name": "train"}]							,    download_size=4_2							,    ),
    ]							,    )
def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	DatasetInfo					):

							'''simple docstring'''






							snake_case_      =				str(snake_case					)
							dataset_info.write_to_directory(snake_case					)
							snake_case_      =				DatasetInfo.from_directory(snake_case					)
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(snake_case							,    "dataset_info.json"					)					)


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				DatasetInfo(
							    description="foo"							,    citation="bar"							,    homepage="https://foo.bar"							,    license="CC0"							,    features=Features({"a": Value("int32"					)}					)							,    post_processed={}							,    supervised_keys=()							,    task_templates=[]							,    builder_name="builder"							,    config_name="config"							,    version="1.0.0"							,    splits=[{"name": "train", "num_examples": 4_2}]							,    download_checksums={}							,    download_size=1_3_3_7							,    post_processing_size=4_4_2							,    dataset_size=1_2_3_4							,    size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4							,    )
							snake_case_      =				dataset_info._to_yaml_dict()
							assert sorted(snake_case					) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML					)
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]							,    (list, dict, int, str)					)
							snake_case_      =				yaml.safe_dump(snake_case					)
							snake_case_      =				yaml.safe_load(snake_case					)
							assert dataset_info_yaml_dict == reloaded


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				DatasetInfo()
							snake_case_      =				dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}




@pytest.mark.parametrize(
    "dataset_infos_dict"							,    [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}					),
        DatasetInfosDict({"my_config_name": DatasetInfo()}					),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"							,    features=Features({"a": Value("int32"					)}					)							,    builder_name="builder"							,    config_name="config"							,    version="1.0.0"							,    splits=[{"name": "train"}]							,    download_size=4_2							,    )
            }					),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=4_2					),
                "v2": DatasetInfo(dataset_size=1_3_3_7					),
            }					),
    ]							,    )
def 					UpperCamelCase_(     snake_case							:	List[Any]							,    snake_case							:	DatasetInfosDict					):

							'''simple docstring'''






							snake_case_      =				str(snake_case					)
							dataset_infos_dict.write_to_directory(snake_case					)
							snake_case_      =				DatasetInfosDict.from_directory(snake_case					)

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														snake_case_      =				config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														snake_case_      =				DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()					)
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(snake_case							,    "README.md"					)					)










'''simple docstring'''





from random import shuffle

import tensorflow as tf
from numpy import array


def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	Optional[int]					):

							'''simple docstring'''






							snake_case_      =				int(snake_case					)
							assert noofclusters < len(snake_case					)

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

							# Will help select random centroids from among the available vectors
							snake_case_      =				list(range(len(snake_case					)					)					)
							shuffle(snake_case					)

							# 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_      =				tf.Graph()

							with graph.as_default():
														# SESSION OF COMPUTATION

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

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

														##Now lets construct the node that will compute the mean
														# The placeholder for the input
														snake_case_      =				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_      =				tf.reduce_mean(snake_case							,    0					)

														##Node for computing Euclidean distances
														# Placeholders for input
														snake_case_      =				tf.placeholder("float"							,    [dim]					)
														snake_case_      =				tf.placeholder("float"							,    [dim]					)
														snake_case_      =				tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(snake_case							,    snake_case					)							,    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_      =				tf.placeholder("float"							,    [noofclusters]					)
														snake_case_      =				tf.argmin(snake_case							,    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_      =				tf.initialize_all_variables()

														# Initialize all variables
														sess.run(snake_case					)

														##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_      =				1_0_0
														for _ in range(snake_case					):
																					##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(snake_case					)					):
																												snake_case_      =				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_      =				[
																												    sess.run(snake_case							,    feed_dict={va: vect, va: sess.run(snake_case					)}					)
																												    for centroid in centroids
																												]
																												# Now use the cluster assignment node, with the distances
																												# as the input
																												snake_case_      =				sess.run(
																												    snake_case							,    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(snake_case					):
																												# Collect all the vectors assigned to this cluster
																												snake_case_      =				[
																												    vectors[i]
																												    for i in range(len(snake_case					)					)
																												    if sess.run(assignments[i]					) == cluster_n
																												]
																												# Compute new centroid location
																												snake_case_      =				sess.run(
																												    snake_case							,    feed_dict={mean_input: array(snake_case					)}					)
																												# Assign value to appropriate variable
																												sess.run(
																												    cent_assigns[cluster_n]							,    feed_dict={centroid_value: new_location}					)

        # Return centroids and assignments
														snake_case_      =				sess.run(snake_case					)
														snake_case_      =				sess.run(snake_case					)
														return centroids, assignments




