




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__a          =			logging.getLogger(__name__)

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				42


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				None
    lowerCAmelCase        =				None


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''train'''
    lowerCAmelCase        =				'''dev'''
    lowerCAmelCase        =				'''test'''


class   __a:






    """simple docstring"""





    @staticmethod
    def 							a__				(				_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		List[InputExample]:
         raise NotImplementedError





    @staticmethod
    def 							a__				(				_SCREAMING_SNAKE_CASE  )			->		List[str]:
         raise NotImplementedError





    @staticmethod
    def 							a__				(				_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE="[CLS]"       ,_SCREAMING_SNAKE_CASE=1       ,_SCREAMING_SNAKE_CASE="[SEP]"       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=-100       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=True       ,)			->		List[InputFeatures]:
         UpperCAmelCase_      :							Union[str, Any]       =      {label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE  )}

         UpperCAmelCase_      :							List[str]       =      []
         for ex_index, example in enumerate(_SCREAMING_SNAKE_CASE  ):
              if ex_index % 10_000 == 0:
                   logger.info('''Writing example %d of %d'''       ,_SCREAMING_SNAKE_CASE       ,len(_SCREAMING_SNAKE_CASE  )  )

              UpperCAmelCase_      :							str       =      []
              UpperCAmelCase_      :							Any       =      []
              for word, label in zip(example.words       ,example.labels  ):
                   UpperCAmelCase_      :							Optional[int]       =      tokenizer.tokenize(_SCREAMING_SNAKE_CASE  )

                   # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                   if len(_SCREAMING_SNAKE_CASE  ) > 0:
                        tokens.extend(_SCREAMING_SNAKE_CASE  )
                        # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                        label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_SCREAMING_SNAKE_CASE  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
              UpperCAmelCase_      :							str       =      tokenizer.num_special_tokens_to_add()
              if len(_SCREAMING_SNAKE_CASE  ) > max_seq_length - special_tokens_count:
                   UpperCAmelCase_      :							Union[str, Any]       =      tokens[: (max_seq_length - special_tokens_count)]
                   UpperCAmelCase_      :							Dict       =      label_ids[: (max_seq_length - special_tokens_count)]

              # The convention in BERT is:
              # (a) For sequence pairs:
              #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
              #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
              # (b) For single sequences:
              #  tokens:   [CLS] the dog is hairy . [SEP]
              #  type_ids:   0   0   0   0  0     0   0
              #
              # Where "type_ids" are used to indicate whether this is the first
              # sequence or the second sequence. The embedding vectors for `type=0` and
              # `type=1` were learned during pre-training and are added to the wordpiece
              # embedding vector (and position vector). This is not *strictly* necessary
              # since the [SEP] token unambiguously separates the sequences, but it makes
              # it easier for the model to learn the concept of sequences.
              #
              # For classification tasks, the first vector (corresponding to [CLS]) is
              # used as the "sentence vector". Note that this only makes sense because
              # the entire model is fine-tuned.
              tokens += [sep_token]
              label_ids += [pad_token_label_id]
              if sep_token_extra:
                   # roberta uses an extra separator b/w pairs of sentences
                   tokens += [sep_token]
                   label_ids += [pad_token_label_id]
              UpperCAmelCase_      :							Union[str, Any]       =      [sequence_a_segment_id] * len(_SCREAMING_SNAKE_CASE  )

              if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
              else:
                   UpperCAmelCase_      :							Union[str, Any]       =      [cls_token] + tokens
                   UpperCAmelCase_      :							List[Any]       =      [pad_token_label_id] + label_ids
                   UpperCAmelCase_      :							str       =      [cls_token_segment_id] + segment_ids

              UpperCAmelCase_      :							Union[str, Any]       =      tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE  )

              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              UpperCAmelCase_      :							str       =      [1 if mask_padding_with_zero else 0] * len(_SCREAMING_SNAKE_CASE  )

              # Zero-pad up to the sequence length.
              UpperCAmelCase_      :							Optional[Any]       =      max_seq_length - len(_SCREAMING_SNAKE_CASE  )
              if pad_on_left:
                   UpperCAmelCase_      :							str       =      ([pad_token] * padding_length) + input_ids
                   UpperCAmelCase_      :							Optional[Any]       =      ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   UpperCAmelCase_      :							int       =      ([pad_token_segment_id] * padding_length) + segment_ids
                   UpperCAmelCase_      :							int       =      ([pad_token_label_id] * padding_length) + label_ids
              else:
                   input_ids += [pad_token] * padding_length
                   input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                   segment_ids += [pad_token_segment_id] * padding_length
                   label_ids += [pad_token_label_id] * padding_length

              assert len(_SCREAMING_SNAKE_CASE  ) == max_seq_length
              assert len(_SCREAMING_SNAKE_CASE  ) == max_seq_length
              assert len(_SCREAMING_SNAKE_CASE  ) == max_seq_length
              assert len(_SCREAMING_SNAKE_CASE  ) == max_seq_length

              if ex_index < 5:
                   logger.info('''*** Example ***'''  )
                   logger.info('''guid: %s'''       ,example.guid  )
                   logger.info('''tokens: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in tokens]  )  )
                   logger.info('''input_ids: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in input_ids]  )  )
                   logger.info('''input_mask: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in input_mask]  )  )
                   logger.info('''segment_ids: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in segment_ids]  )  )
                   logger.info('''label_ids: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in label_ids]  )  )

              if "token_type_ids" not in tokenizer.model_input_names:
                   UpperCAmelCase_      :							List[str]       =      None

              features.append(
                  InputFeatures(
                      input_ids=_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,label_ids=_SCREAMING_SNAKE_CASE  )  )
         return features


if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data import Dataset

     class   __a(					_a       ):






         """simple docstring"""





         lowerCAmelCase        =				42
         lowerCAmelCase        =				nn.CrossEntropyLoss().ignore_index





         def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE = Split.train       ,)			->		Tuple:
              # Load data features from cache or dataset file
              UpperCAmelCase_      :							Dict       =      os.path.join(
                  _SCREAMING_SNAKE_CASE       ,'''cached_{}_{}_{}'''.format(mode.value       ,tokenizer.__class__.__name__       ,str(_SCREAMING_SNAKE_CASE  )  )       ,)

              # Make sure only the first process in distributed training processes the dataset,
              # and the others will use the cache.
              UpperCAmelCase_      :							Optional[Any]       =      cached_features_file + '''.lock'''
              with FileLock(_SCREAMING_SNAKE_CASE  ):
                   if os.path.exists(_SCREAMING_SNAKE_CASE  ) and not overwrite_cache:
                        logger.info(f'''Loading features from cached file {cached_features_file}'''  )
                        UpperCAmelCase_      :							Optional[Any]       =      torch.load(_SCREAMING_SNAKE_CASE  )
                   else:
                        logger.info(f'''Creating features from dataset file at {data_dir}'''  )
                        UpperCAmelCase_      :							str       =      token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                        # TODO clean up all this to leverage built-in features of tokenizers
                        UpperCAmelCase_      :							Any       =      token_classification_task.convert_examples_to_features(
                            _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,cls_token_at_end=bool(model_type in ['''xlnet''']  )       ,cls_token=tokenizer.cls_token       ,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0       ,sep_token=tokenizer.sep_token       ,sep_token_extra=_SCREAMING_SNAKE_CASE       ,pad_on_left=bool(tokenizer.padding_side == '''left'''  )       ,pad_token=tokenizer.pad_token_id       ,pad_token_segment_id=tokenizer.pad_token_type_id       ,pad_token_label_id=self.pad_token_label_id       ,)
                        logger.info(f'''Saving features into cached file {cached_features_file}'''  )
                        torch.save(self.features       ,_SCREAMING_SNAKE_CASE  )





         def __len__(				self  )			->		List[Any]:
              return len(self.features  )





         def __getitem__(				self       ,_SCREAMING_SNAKE_CASE  )			->		InputFeatures:
              return self.features[i]


if is_tf_available():
     import tensorflow as tf

     class   __a:






         """simple docstring"""





         lowerCAmelCase        =				42
         lowerCAmelCase        =				-100





         def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE = Split.train       ,)			->		List[str]:
              UpperCAmelCase_      :							Tuple       =      token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              # TODO clean up all this to leverage built-in features of tokenizers
              UpperCAmelCase_      :							List[str]       =      token_classification_task.convert_examples_to_features(
                  _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,cls_token_at_end=bool(model_type in ['''xlnet''']  )       ,cls_token=tokenizer.cls_token       ,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0       ,sep_token=tokenizer.sep_token       ,sep_token_extra=_SCREAMING_SNAKE_CASE       ,pad_on_left=bool(tokenizer.padding_side == '''left'''  )       ,pad_token=tokenizer.pad_token_id       ,pad_token_segment_id=tokenizer.pad_token_type_id       ,pad_token_label_id=self.pad_token_label_id       ,)

              def gen():
                   for ex in self.features:
                        if ex.token_type_ids is None:
                             yield (
                                 {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                 ex.label_ids,
                             )
                        else:
                             yield (
                                 {
                                     "input_ids": ex.input_ids,
                                     "attention_mask": ex.attention_mask,
                                     "token_type_ids": ex.token_type_ids,
                                 },
                                 ex.label_ids,
                             )

              if "token_type_ids" not in tokenizer.model_input_names:
                   UpperCAmelCase_      :							Any       =      tf.data.Dataset.from_generator(
                       _SCREAMING_SNAKE_CASE       ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)       ,(
                           {'''input_ids''': tf.TensorShape([None]  ), '''attention_mask''': tf.TensorShape([None]  )},
                           tf.TensorShape([None]  ),
                       )       ,)
              else:
                   UpperCAmelCase_      :							List[Any]       =      tf.data.Dataset.from_generator(
                       _SCREAMING_SNAKE_CASE       ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)       ,(
                           {
                               '''input_ids''': tf.TensorShape([None]  ),
                               '''attention_mask''': tf.TensorShape([None]  ),
                               '''token_type_ids''': tf.TensorShape([None]  ),
                           },
                           tf.TensorShape([None]  ),
                       )       ,)





         def 							a__				(				self  )			->		Optional[Any]:
              UpperCAmelCase_      :							List[str]       =      self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

              return self.dataset





         def __len__(				self  )			->		List[Any]:
              return len(self.features  )





         def __getitem__(				self       ,_SCREAMING_SNAKE_CASE  )			->		InputFeatures:
              return self.features[i]





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