





'''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


__magic_name__   :						List[str]    =			logging.getLogger(__name__)


@dataclass
class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     UpperCAmelCase__       :						str
     UpperCAmelCase__       :						List[str]
     UpperCAmelCase__       :						Optional[List[str]]







@dataclass
class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     UpperCAmelCase__       :						List[int]
     UpperCAmelCase__       :						List[int]
     UpperCAmelCase__       :						Optional[List[int]]      =   None
     UpperCAmelCase__       :						Optional[List[int]]      =   None







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   '''train'''
     UpperCAmelCase__       :						List[str]      =   '''dev'''
     UpperCAmelCase__       :						Union[str, Any]      =   '''test'''







class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase						,   lowerCamelCase	):
       raise NotImplementedError

     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase	):
       raise NotImplementedError






     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=False						,   lowerCamelCase="[CLS]"						,   lowerCamelCase=1						,   lowerCamelCase="[SEP]"						,   lowerCamelCase=False						,   lowerCamelCase=False						,   lowerCamelCase=0						,   lowerCamelCase=0						,   lowerCamelCase=-100						,   lowerCamelCase=0						,   lowerCamelCase=True						,   ):
       _snake_case     		=				{label: i for i, label in enumerate(lowerCamelCase	)}

       _snake_case     		=				[]
       for ex_index, example in enumerate(lowerCamelCase	):
         if ex_index % 10_000 == 0:
           logger.info("Writing example %d of %d"						,   lowerCamelCase						,   len(lowerCamelCase	)	)

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

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
         _snake_case     		=				tokenizer.num_special_tokens_to_add()
         if len(lowerCamelCase	) > 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(lowerCamelCase	)

         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(lowerCamelCase	)

         # 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(lowerCamelCase	)

         # Zero-pad up to the sequence length.
         _snake_case     		=				max_seq_length - len(lowerCamelCase	)
         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(lowerCamelCase	) == max_seq_length
         assert len(lowerCamelCase	) == max_seq_length
         assert len(lowerCamelCase	) == max_seq_length
         assert len(lowerCamelCase	) == max_seq_length

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

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

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


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


 class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



      '''simple docstring'''






      UpperCAmelCase__       :						List[InputFeatures]
      UpperCAmelCase__       :						int      =   nn.CrossEntropyLoss().ignore_index

      def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase=False						,   lowerCamelCase = Split.train						,   ):
        # Load data features from cache or dataset file
        _snake_case     		=				os.path.join(
            lowerCamelCase						,   "cached_{}_{}_{}".format(mode.value						,   tokenizer.__class__.__name__						,   str(lowerCamelCase	)	)						,   )

        # 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(lowerCamelCase	):
          if os.path.exists(lowerCamelCase	) and not overwrite_cache:
            logger.info(F'''Loading features from cached file {cached_features_file}'''	)
            _snake_case     		=				torch.load(lowerCamelCase	)
          else:
            logger.info(F'''Creating features from dataset file at {data_dir}'''	)
            _snake_case     		=				token_classification_task.read_examples_from_file(lowerCamelCase						,   lowerCamelCase	)
            # TODO clean up all this to leverage built-in features of tokenizers
            _snake_case     		=				token_classification_task.convert_examples_to_features(
                lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   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=lowerCamelCase						,   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						,   lowerCamelCase	)

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






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


if is_tf_available():
 import tensorflow as tf


 class       __SCREAMING_SNAKE_CASE	:



      '''simple docstring'''






      UpperCAmelCase__       :						List[InputFeatures]
      UpperCAmelCase__       :						int      =   -100

      def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase=False						,   lowerCamelCase = Split.train						,   ):
        _snake_case     		=				token_classification_task.read_examples_from_file(lowerCamelCase						,   lowerCamelCase	)
        # TODO clean up all this to leverage built-in features of tokenizers
        _snake_case     		=				token_classification_task.convert_examples_to_features(
            lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   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=lowerCamelCase						,   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(
              lowerCamelCase						,   ({"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(
              lowerCamelCase						,   ({"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	):
        _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						,   lowerCamelCase	):
        return self.features[i]









'''simple docstring'''



from __future__ import annotations

import numpy as np




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


