
import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : List[str]			  =       ""
		__lowercase : int			  =       (
		    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
		)
		__lowercase : Dict			  =       None  # compression type in fsspec. ex: "gzip"
		__lowercase : Dict			  =       None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz



		def __init__(				self       ,   A_ = ""       ,   A_ = None       ,   A_ = None       ,   **A_ )						->      Optional[Any]:
				"""simple docstring"""
				super().__init__(self       ,   **__UpperCamelCase )
				# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
				UpperCamelCase						=  fsspec.open(
				    __UpperCamelCase       ,   mode='rb'       ,   protocol=__UpperCamelCase       ,   compression=self.compression       ,   client_kwargs={
				        'requote_redirect_url': False,  # see https://github.com/huggingface/datasets/pull/5459
				        'trust_env': True,  # Enable reading proxy env variables.
				        **(target_options or {}).pop('client_kwargs'       ,   {} ),  # To avoid issues if it was already passed.
				    }       ,   **(target_options or {})       ,   )
				UpperCamelCase						=  os.path.basename(self.file.path.split('::' )[0] )
				UpperCamelCase						=  (
				    self.compressed_name[: self.compressed_name.rindex('.' )]
				    if '.' in self.compressed_name
				    else self.compressed_name
				)
				UpperCamelCase						=  None



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				# compressed file paths are always relative to the archive root
				return super()._strip_protocol(__UpperCamelCase ).lstrip('/' )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				if self.dir_cache is None:
						UpperCamelCase						=  {**self.file.fs.info(self.file.path ), 'name': self.uncompressed_name}
						UpperCamelCase						=  {f['name']: f}



		def __UpperCamelCase				(				self       ,   A_ )						->      List[Any]:
				"""simple docstring"""
				return self.file.open().read()



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = "rb"       ,   A_=None       ,   A_=True       ,   A_=None       ,   **A_       ,   )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self._strip_protocol(__UpperCamelCase )
				if mode != "rb":
						raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'''' )
				return self.file.open()






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : List[str]			  =       "bz2"
		__lowercase : Optional[Any]			  =       "bz2"
		__lowercase : Dict			  =       ".bz2"






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       "gzip"
		__lowercase : Optional[Any]			  =       "gzip"
		__lowercase : Optional[int]			  =       ".gz"






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : int			  =       "lz4"
		__lowercase : Optional[int]			  =       "lz4"
		__lowercase : List[Any]			  =       ".lz4"






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Optional[int]			  =       "xz"
		__lowercase : str			  =       "xz"
		__lowercase : List[Any]			  =       ".xz"






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Optional[Any]			  =       "zstd"
		__lowercase : Dict			  =       "zstd"
		__lowercase : Any			  =       ".zst"



		def __init__(				self       ,   A_       ,   A_ = "rb"       ,   A_ = None       ,   A_ = None       ,   A_ = DEFAULT_BLOCK_SIZE       ,   **A_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(
				    fo=__UpperCamelCase       ,   mode=__UpperCamelCase       ,   target_protocol=__UpperCamelCase       ,   target_options=__UpperCamelCase       ,   block_size=__UpperCamelCase       ,   **__UpperCamelCase       ,   )
				# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
				#
				# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
				#     out.close = close
				# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
				#
				# see https://github.com/intake/filesystem_spec/issues/725
				UpperCamelCase						=  self.file.__enter__


				class        lowercase      :



						def __init__(				self       ,   A_ )						->      List[str]:
								"""simple docstring"""
								UpperCamelCase						=  file_



						def __enter__(				self )						->      Any:
								"""simple docstring"""
								self._file.__enter__()
								return self



						def __exit__(				self       ,   *A_       ,   **A_ )						->      Any:
								"""simple docstring"""
								self._file.__exit__(*__UpperCamelCase       ,   **__UpperCamelCase )



						def __iter__(				self )						->      Union[str, Any]:
								"""simple docstring"""
								return iter(self._file )



						def __UpperCamelCase				(				self )						->      Optional[int]:
								"""simple docstring"""
								return next(self._file )



						def __getattr__(				self       ,   A_ )						->      Tuple:
								"""simple docstring"""
								return getattr(self._file       ,   __UpperCamelCase )

				def fixed_enter(*A_       ,   **A_ ):
						return WrappedFile(_enter(*__UpperCamelCase       ,   **__UpperCamelCase ) )

				UpperCamelCase						=  fixed_enter


from random import shuffle

import tensorflow as tf
from numpy import array


def    A (      lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		assert noofclusters < len(lowercase				)

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

		# Will help select random centroids from among the available vectors
		UpperCamelCase						=  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						=  tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

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

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

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