

'''simple docstring'''



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


UpperCAmelCase :       str			   =     logging.getLogger(__name__)







@dataclass
class 						lowerCAmelCase__   :



 """simple docstring"""



 lowerCAmelCase__  					=      42
 lowerCAmelCase__  					=      42
 lowerCAmelCase__  					=      42







@dataclass
class 						lowerCAmelCase__   :



 """simple docstring"""



 lowerCAmelCase__  					=      42
 lowerCAmelCase__  					=      42
 lowerCAmelCase__  					=      None
 lowerCAmelCase__  					=      None







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "train"
 lowerCAmelCase__  					=      "dev"
 lowerCAmelCase__  					=      "test"







class 						lowerCAmelCase__   :



 """simple docstring"""



 @staticmethod
 def       UpperCAmelCase__		(		__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Union[Split, str]			)  -> List[InputExample]:


      """simple docstring"""




      raise NotImplementedError
 @staticmethod
 def       UpperCAmelCase__		(		__SCREAMING_SNAKE_CASE					:       str			)  -> List[str]:


      """simple docstring"""




      raise NotImplementedError


 @staticmethod
 def       UpperCAmelCase__		(		__SCREAMING_SNAKE_CASE					:       List[InputExample]					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       PreTrainedTokenizer					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=False					,							__SCREAMING_SNAKE_CASE					:       int="[CLS]"					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=1					,							__SCREAMING_SNAKE_CASE					:       Optional[int]="[SEP]"					,							__SCREAMING_SNAKE_CASE					:       int=False					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=False					,							__SCREAMING_SNAKE_CASE					:       Dict=0					,							__SCREAMING_SNAKE_CASE					:       List[str]=0					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=-100					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=0					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=True					,							)  -> List[InputFeatures]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      {label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE			)}

      __SCREAMING_SNAKE_CASE     =      []
      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			)			)

           __SCREAMING_SNAKE_CASE     =      []
           __SCREAMING_SNAKE_CASE     =      []
           for word, label in zip(example.words					,							example.labels			):
                __SCREAMING_SNAKE_CASE     =      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.
           __SCREAMING_SNAKE_CASE     =      tokenizer.num_special_tokens_to_add()
           if len(__SCREAMING_SNAKE_CASE			) > max_seq_length - special_tokens_count:
                __SCREAMING_SNAKE_CASE     =      tokens[: (max_seq_length - special_tokens_count)]
                __SCREAMING_SNAKE_CASE     =      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]
           __SCREAMING_SNAKE_CASE     =      [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:
                __SCREAMING_SNAKE_CASE     =      [cls_token] + tokens
                __SCREAMING_SNAKE_CASE     =      [pad_token_label_id] + label_ids
                __SCREAMING_SNAKE_CASE     =      [cls_token_segment_id] + segment_ids

           __SCREAMING_SNAKE_CASE     =      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.
           __SCREAMING_SNAKE_CASE     =      [1 if mask_padding_with_zero else 0] * len(__SCREAMING_SNAKE_CASE			)

           # Zero-pad up to the sequence length.
           __SCREAMING_SNAKE_CASE     =      max_seq_length - len(__SCREAMING_SNAKE_CASE			)
           if pad_on_left:
                __SCREAMING_SNAKE_CASE     =      ([pad_token] * padding_length) + input_ids
                __SCREAMING_SNAKE_CASE     =      ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                __SCREAMING_SNAKE_CASE     =      ([pad_token_segment_id] * padding_length) + segment_ids
                __SCREAMING_SNAKE_CASE     =      ([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:
                __SCREAMING_SNAKE_CASE     =      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 						lowerCAmelCase__   (      a					):



   """simple docstring"""



   lowerCAmelCase__  					=      42
   lowerCAmelCase__  					=      nn.CrossEntropyLoss().ignore_index
   def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       TokenClassificationTask					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       PreTrainedTokenizer					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[int] = None					,							__SCREAMING_SNAKE_CASE					:       List[Any]=False					,							__SCREAMING_SNAKE_CASE					:       Split = Split.train					,							)  -> Union[str, Any]:


        """simple docstring"""




        __SCREAMING_SNAKE_CASE     =      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.
        __SCREAMING_SNAKE_CASE     =      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}'			)
                  __SCREAMING_SNAKE_CASE     =      torch.load(__SCREAMING_SNAKE_CASE			)
             else:
                  logger.info(f'Creating features from dataset file at {data_dir}'			)
                  __SCREAMING_SNAKE_CASE     =      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
                  __SCREAMING_SNAKE_CASE     =      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					:       Tuple			)  -> str:


        """simple docstring"""




        return len(self.features			)


   def __getitem__(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> InputFeatures:


        """simple docstring"""




        return self.features[i]


if is_tf_available():
  import tensorflow as tf







  class 						lowerCAmelCase__   :



   """simple docstring"""



   lowerCAmelCase__  					=      42
   lowerCAmelCase__  					=      -100
   def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       TokenClassificationTask					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       PreTrainedTokenizer					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[int] = None					,							__SCREAMING_SNAKE_CASE					:       Tuple=False					,							__SCREAMING_SNAKE_CASE					:       Split = Split.train					,							)  -> Optional[Any]:


        """simple docstring"""




        __SCREAMING_SNAKE_CASE     =      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
        __SCREAMING_SNAKE_CASE     =      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:
             __SCREAMING_SNAKE_CASE     =      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:
             __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Tuple:


        """simple docstring"""




        __SCREAMING_SNAKE_CASE     =      self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features			)			)			)

        return self.dataset
   def __len__(		self					:       List[Any]			)  -> Union[str, Any]:


        """simple docstring"""




        return len(self.features			)


   def __getitem__(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> InputFeatures:


        """simple docstring"""




        return self.features[i]









'''simple docstring'''



import numpy as np







def    a__       (  a__      ,    a__      ,    a__ = 1E-1_2      ,    a__ = 1_00      ,    ):




     """simple docstring"""


     assert np.shape(a__						)[0] == np.shape(a__						)[1]
     # Ensure proper dimensionality.
     assert np.shape(a__						)[0] == np.shape(a__						)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(a__						) == np.iscomplexobj(a__						)
     __SCREAMING_SNAKE_CASE     =      np.iscomplexobj(a__						)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(a__      ,    input_matrix.conj().T						)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     __SCREAMING_SNAKE_CASE     =      False
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    a__						)
          # Normalize the resulting output vector.
          __SCREAMING_SNAKE_CASE     =      w / np.linalg.norm(a__						)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          __SCREAMING_SNAKE_CASE     =      vector.conj().T if is_complex else vector.T
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    np.dot(a__      ,    a__						)						)

          # Check convergence.
          __SCREAMING_SNAKE_CASE     =      np.abs(lambda_ - lambda_previous						) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               __SCREAMING_SNAKE_CASE     =      True

          __SCREAMING_SNAKE_CASE     =      lambda_

     if is_complex:
          __SCREAMING_SNAKE_CASE     =      np.real(lambda_						)

     return lambda_, vector







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]						)
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						)
     __SCREAMING_SNAKE_CASE     =      real_input_matrix.astype(np.complexaaa						)
     __SCREAMING_SNAKE_CASE     =      np.triu(1J * complex_input_matrix      ,    1						)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						).astype(np.complexaaa						)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __SCREAMING_SNAKE_CASE     =      real_input_matrix
               __SCREAMING_SNAKE_CASE     =      real_vector
          elif problem_type == "complex":
               __SCREAMING_SNAKE_CASE     =      complex_input_matrix
               __SCREAMING_SNAKE_CASE     =      complex_vector

          # Our implementation.
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      power_iteration(a__      ,    a__						)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      np.linalg.eigh(a__						)
          # Last eigenvalue is the maximum one.
          __SCREAMING_SNAKE_CASE     =      eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __SCREAMING_SNAKE_CASE     =      eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(a__						) - np.abs(a__						)						) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()






