

'''simple docstring'''







from random import shuffle

import tensorflow as tf
from numpy import array
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Any:
       A_		   = int(UpperCAmelCase__			)
       assert noofclusters < len(UpperCAmelCase__			)

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

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

       # 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.

       A_		   = tf.Graph()

       with graph.as_default():
              # SESSION OF COMPUTATION

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

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

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

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

              # Initialize all variables
              sess.run(UpperCAmelCase__			)

              ##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.
              A_		   = 1_00
              for _ in range(UpperCAmelCase__			):
                     ##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(UpperCAmelCase__			)			):
                            A_		   = 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.
                            A_		   = [
                                sess.run(UpperCAmelCase__,    feed_dict={va: vect, va: sess.run(UpperCAmelCase__			)}			)
                                for centroid in centroids
                            ]
                            # Now use the cluster assignment node, with the distances
                            # as the input
                            A_		   = sess.run(
                                UpperCAmelCase__,    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(UpperCAmelCase__			):
                            # Collect all the vectors assigned to this cluster
                            A_		   = [
                                vectors[i]
                                for i in range(len(UpperCAmelCase__			)			)
                                if sess.run(assignments[i]			) == cluster_n
                            ]
                            # Compute new centroid location
                            A_		   = sess.run(
                                UpperCAmelCase__,    feed_dict={mean_input: array(UpperCAmelCase__			)}			)
                            # Assign value to appropriate variable
                            sess.run(
                                cent_assigns[cluster_n],    feed_dict={centroid_value: new_location}			)

        # Return centroids and assignments
              A_		   = sess.run(UpperCAmelCase__			)
              A_		   = sess.run(UpperCAmelCase__			)
              return centroids, assignments



'''simple docstring'''







import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase						    =      get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
@require_tokenizers
class    A__ ( _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						ReformerTokenizer
    lowercase		   =						ReformerTokenizerFast
    lowercase		   =						True
    lowercase		   =						False
    lowercase		   =						True



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           super().setUp()

           A_		   = ReformerTokenizer(UpperCamelCase__						,			keep_accents=UpperCamelCase__    )
           tokenizer.save_pretrained(self.tmpdirname    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = """<s>"""
           A_		   = 1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__    )						,			UpperCamelCase__    )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__    )						,			UpperCamelCase__    )



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = list(self.get_tokenizer().get_vocab().keys()    )

           self.assertEqual(vocab_keys[0]						,			"""<unk>"""    )
           self.assertEqual(vocab_keys[1]						,			"""<s>"""    )
           self.assertEqual(vocab_keys[-1]						,			"""j"""    )
           self.assertEqual(len(UpperCamelCase__    )						,			1000    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           self.assertEqual(self.get_tokenizer().vocab_size						,			1000    )



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           if not self.test_rust_tokenizer:
                  return

           A_		   = self.get_tokenizer()
           A_		   = self.get_rust_tokenizer()

           A_		   = """I was born in 92000, and this is falsé."""

           A_		   = tokenizer.tokenize(UpperCamelCase__    )
           A_		   = rust_tokenizer.tokenize(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__						,			add_special_tokens=UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )

           A_		   = self.get_rust_tokenizer()
           A_		   = tokenizer.encode(UpperCamelCase__    )
           A_		   = rust_tokenizer.encode(UpperCamelCase__    )
           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__=15    )  -> int:





           '''simple docstring'''
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                         A_		   = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__    )

                         # Simple input
                         A_		   = """This is a simple input"""
                         A_		   = ["""This is a simple input 1""", """This is a simple input 2"""]
                         A_		   = ("""This is a simple input""", """This is a pair""")
                         A_		   = [
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Simple input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Simple input
                         self.assertRaises(
                             UpperCamelCase__						,			tokenizer_r.batch_encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""						,			)

                         # Pair input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Pair input
                         self.assertRaises(UpperCamelCase__						,			tokenizer_r.encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""    )

                         # Pair input
                         self.assertRaises(
                             UpperCamelCase__						,			tokenizer_r.batch_encode_plus						,			UpperCamelCase__						,			max_length=UpperCamelCase__						,			padding="""max_length"""						,			)



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           pass



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = ReformerTokenizer(UpperCamelCase__						,			keep_accents=UpperCamelCase__    )

           A_		   = tokenizer.tokenize("""This is a test"""    )
           self.assertListEqual(UpperCamelCase__						,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCamelCase__    )						,			[285, 46, 10, 170, 382]						,			)

           A_		   = tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
           self.assertListEqual(
               UpperCamelCase__						,			[
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """9""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """é""",
                   """.""",
               ]						,			)
           A_		   = tokenizer.convert_tokens_to_ids(UpperCamelCase__    )
           self.assertListEqual(
               UpperCamelCase__						,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,			)

           A_		   = tokenizer.convert_ids_to_tokens(UpperCamelCase__    )
           self.assertListEqual(
               UpperCamelCase__						,			[
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """<unk>""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """<unk>""",
                   """.""",
               ]						,			)



    @cached_property
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""    )



    @slow
    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = """Hello World!"""
           A_		   = [126, 32, 262, 152, 38, 72, 287]

           self.assertListEqual(UpperCamelCase__						,			self.big_tokenizer.encode(UpperCamelCase__    )    )



    @slow
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = (
               """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
               """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
           )
           A_		   = [
               108,
               265,
               24,
               111,
               4,
               258,
               156,
               35,
               28,
               275,
               3,
               259,
               297,
               260,
               84,
               4,
               35,
               110,
               44,
               8,
               259,
               91,
               268,
               21,
               11,
               209,
               274,
               109,
               266,
               277,
               117,
               86,
               93,
               315,
               258,
               278,
               258,
               277,
               258,
               0,
               258,
               288,
               258,
               319,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               287,
               258,
               315,
               258,
               289,
               258,
               278,
               99,
               269,
               266,
               262,
               8,
               259,
               241,
               4,
               217,
               230,
               268,
               266,
               55,
               168,
               106,
               75,
               193,
               266,
               223,
               27,
               49,
               26,
               282,
               25,
               264,
               299,
               19,
               26,
               0,
               258,
               277,
               117,
               86,
               93,
               176,
               183,
               270,
               11,
               262,
               42,
               61,
               265,
           ]

           self.assertListEqual(UpperCamelCase__						,			self.big_tokenizer.encode(UpperCamelCase__    )    )



    @require_torch
    @slow
    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           import torch

           from transformers import ReformerConfig, ReformerModel

           # Build sequence
           A_		   = list(self.big_tokenizer.get_vocab().keys()    )[:10]
           A_		   = """ """.join(UpperCamelCase__    )
           A_		   = self.big_tokenizer.encode_plus(UpperCamelCase__						,			return_tensors="""pt"""    )
           A_		   = self.big_tokenizer.batch_encode_plus([sequence, sequence]						,			return_tensors="""pt"""    )

           A_		   = ReformerConfig()
           # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
           A_		   = encoded_sequence["""input_ids"""].shape
           A_		   = ReformerModel(UpperCamelCase__    )

           # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
                  model(**UpperCamelCase__    )
                  model(**UpperCamelCase__    )







    @slow
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           # fmt: off
           A_		   = {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
           # fmt: on

           # This tokenizer does not know some characters like ")".
           # That is the reason why we use very simple texts here.
           # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
           A_		   = [
               """This is a very simple sentence.""",
               """The quick brown fox jumps over the lazy dog.""",
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCamelCase__						,			model_name="""google/reformer-crime-and-punishment"""						,			revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""						,			padding=UpperCamelCase__						,			sequences=UpperCamelCase__						,			)
