


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


_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   logging.getLogger(__name__)

@dataclass
class   A__ :







    """simple docstring"""

    __magic_name__        =		42
    __magic_name__        =		42
    __magic_name__        =		42






@dataclass
class   A__ :







    """simple docstring"""

    __magic_name__        =		42
    __magic_name__        =		42
    __magic_name__        =		None
    __magic_name__        =		None






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'train'
    __magic_name__        =		'dev'
    __magic_name__        =		'test'






class   A__ :







    """simple docstring"""

    @staticmethod
    def 		a_      (	__snake_case      , __snake_case   ):
         raise NotImplementedError
    @staticmethod
    def 		a_      (	__snake_case   ):
         raise NotImplementedError




    @staticmethod
    def 		a_      (	__snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case=False      , __snake_case="[CLS]"      , __snake_case=1      , __snake_case="[SEP]"      , __snake_case=False      , __snake_case=False      , __snake_case=0      , __snake_case=0      , __snake_case=-1_0_0      , __snake_case=0      , __snake_case=True      , ):
         snake_case					=    {label: i for i, label in enumerate(__snake_case   )}

         snake_case					=    []
         for ex_index, example in enumerate(__snake_case   ):
              if ex_index % 1_0_0_0_0 == 0:
                   logger.info('''Writing example %d of %d'''      , __snake_case      , len(__snake_case   )   )

              snake_case					=    []
              snake_case					=    []
              for word, label in zip(example.words      , example.labels   ):
                   snake_case					=    tokenizer.tokenize(__snake_case   )

                   # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                   if len(__snake_case   ) > 0:
                        tokens.extend(__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(__snake_case   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
              snake_case					=    tokenizer.num_special_tokens_to_add()
              if len(__snake_case   ) > max_seq_length - special_tokens_count:
                   snake_case					=    tokens[: (max_seq_length - special_tokens_count)]
                   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]
              snake_case					=    [sequence_a_segment_id] * len(__snake_case   )

              if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
              else:
                   snake_case					=    [cls_token] + tokens
                   snake_case					=    [pad_token_label_id] + label_ids
                   snake_case					=    [cls_token_segment_id] + segment_ids

              snake_case					=    tokenizer.convert_tokens_to_ids(__snake_case   )

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

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

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

              if "token_type_ids" not in tokenizer.model_input_names:
                   snake_case					=    None

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


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

 class   A__ (   snake_case__     ):







     """simple docstring"""

     __magic_name__        =		42
     __magic_name__        =		nn.CrossEntropyLoss().ignore_index
     def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case = None      , __snake_case=False      , __snake_case = Split.train      , ):
          # Load data features from cache or dataset file
          snake_case					=    os.path.join(
              __snake_case      , '''cached_{}_{}_{}'''.format(mode.value      , tokenizer.__class__.__name__      , str(__snake_case   )   )      , )

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          snake_case					=    cached_features_file + '''.lock'''
          with FileLock(__snake_case   ):
               if os.path.exists(__snake_case   ) and not overwrite_cache:
                    logger.info(F'''Loading features from cached file {cached_features_file}'''   )
                    snake_case					=    torch.load(__snake_case   )
               else:
                    logger.info(F'''Creating features from dataset file at {data_dir}'''   )
                    snake_case					=    token_classification_task.read_examples_from_file(__snake_case      , __snake_case   )
                    # TODO clean up all this to leverage built-in features of tokenizers
                    snake_case					=    token_classification_task.convert_examples_to_features(
                        __snake_case      , __snake_case      , __snake_case      , __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=__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      , __snake_case   )
     def __len__(	self   ):
          return len(self.features   )




     def __getitem__(	self      , __snake_case   ):
          return self.features[i]


if is_tf_available():
 import tensorflow as tf

 class   A__ :







     """simple docstring"""

     __magic_name__        =		42
     __magic_name__        =		-1_00
     def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case = None      , __snake_case=False      , __snake_case = Split.train      , ):
          snake_case					=    token_classification_task.read_examples_from_file(__snake_case      , __snake_case   )
          # TODO clean up all this to leverage built-in features of tokenizers
          snake_case					=    token_classification_task.convert_examples_to_features(
              __snake_case      , __snake_case      , __snake_case      , __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=__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:
               snake_case					=    tf.data.Dataset.from_generator(
                   __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:
               snake_case					=    tf.data.Dataset.from_generator(
                   __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   ):
          snake_case					=    self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

          return self.dataset
     def __len__(	self   ):
          return len(self.features   )




     def __getitem__(	self      , __snake_case   ):
          return self.features[i]








def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    len(UpperCamelCase_    )
     snake_case					=    [[0] * n for i in range(UpperCamelCase_    )]
     for i in range(UpperCamelCase_    ):
          snake_case					=    y_points[i]

     for i in range(2							,UpperCamelCase_    ):
          for j in range(UpperCamelCase_							,UpperCamelCase_    ):
               snake_case					=    (
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




