


"""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__  							=logging.getLogger(__name__)





@dataclass
class    lowerCamelCase__						:
      a :     str
      a :     List[str]
      a :     Optional[List[str]]






@dataclass
class    lowerCamelCase__						:
      a :     List[int]
      a :     List[int]
      a :     Optional[List[int]]											=		None
      a :     Optional[List[int]]											=		None






class    lowerCamelCase__						(		_a    ):
      a :     List[Any]											=		"""train"""
      a :     Union[str, Any]											=		"""dev"""
      a :     Union[str, Any]											=		"""test"""






class    lowerCamelCase__						:
      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		A_	:	Union[str, Any]      ,       A_	:	Union[Split, str]							):






       '''simple docstring'''


       raise NotImplementedError
      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		A_	:	str							):






       '''simple docstring'''


       raise NotImplementedError






      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		A_	:	List[InputExample]      ,       A_	:	List[str]      ,       A_	:	int      ,       A_	:	PreTrainedTokenizer      ,       A_	:	Optional[Any]=False      ,       A_	:	List[str]="[CLS]"      ,       A_	:	Optional[int]=1      ,       A_	:	int="[SEP]"      ,       A_	:	List[str]=False      ,       A_	:	Any=False      ,       A_	:	int=0      ,       A_	:	Tuple=0      ,       A_	:	List[Any]=-1_0_0      ,       A_	:	Tuple=0      ,       A_	:	List[str]=True      ,       ):






       '''simple docstring'''


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

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

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

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

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

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

        __lowercase									=	tokenizer.convert_tokens_to_ids(A_							)

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

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

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

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

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


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





       class    lowerCamelCase__						(		_a    ):
             a :     List[InputFeatures]
             a :     int											=		nn.CrossEntropyLoss().ignore_index
             def __init__(		self	:	List[str]      ,       A_	:	TokenClassificationTask      ,       A_	:	str      ,       A_	:	PreTrainedTokenizer      ,       A_	:	List[str]      ,       A_	:	str      ,       A_	:	Optional[int] = None      ,       A_	:	str=False      ,       A_	:	Split = Split.train      ,       ):






              '''simple docstring'''


              __lowercase									=	os.path.join(
                  A_      ,       """cached_{}_{}_{}""".format(mode.value      ,       tokenizer.__class__.__name__      ,       str(A_							)							)      ,       )

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






              '''simple docstring'''


              return len(self.features							)






             def __getitem__(		self	:	str      ,       A_	:	List[Any]							):






              '''simple docstring'''


              return self.features[i]


if is_tf_available():
       import tensorflow as tf





       class    lowerCamelCase__						:
             a :     List[InputFeatures]
             a :     int											=		-1_00
             def __init__(		self	:	Tuple      ,       A_	:	TokenClassificationTask      ,       A_	:	str      ,       A_	:	PreTrainedTokenizer      ,       A_	:	List[str]      ,       A_	:	str      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[Any]=False      ,       A_	:	Split = Split.train      ,       ):






              '''simple docstring'''


              __lowercase									=	token_classification_task.read_examples_from_file(A_      ,       A_							)
              # TODO clean up all this to leverage built-in features of tokenizers
              __lowercase									=	token_classification_task.convert_examples_to_features(
                  A_      ,       A_      ,       A_      ,       A_      ,       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=A_      ,       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:
               __lowercase									=	tf.data.Dataset.from_generator(
                   A_      ,       ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)      ,       (
                       {"""input_ids""": tf.TensorShape([None]							), """attention_mask""": tf.TensorShape([None]							)},
                       tf.TensorShape([None]							),
                   )      ,       )
              else:
               __lowercase									=	tf.data.Dataset.from_generator(
                   A_      ,       ({"""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 SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






              '''simple docstring'''


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

              return self.dataset
             def __len__(		self	:	str							):






              '''simple docstring'''


              return len(self.features							)






             def __getitem__(		self	:	Optional[int]      ,       A_	:	Union[str, Any]							):






              '''simple docstring'''


              return self.features[i]




"""simple docstring"""

from typing import List

from .keymap import KEYMAP, get_character


def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += [key]
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator


def lowerCAmelCase_      (				*UpperCamelCase__	: List[str] ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += keys
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator





class    lowerCamelCase__						(		_a    ):
      def __new__(		cls	:	str      ,       A_	:	Optional[Any]      ,       A_	:	Union[str, Any]      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	super().__new__(cls      ,       A_      ,       A_      ,       A_							)
       if not hasattr(A_      ,       """key_handler"""							):
        setattr(A_      ,       """key_handler"""      ,       {}							)
       setattr(A_      ,       """handle_input"""      ,       KeyHandler.handle_input							)

       for value in attrs.values():
        __lowercase									=	getattr(A_      ,       """handle_key"""      ,       []							)
        for key in handled_keys:
         __lowercase									=	value
       return new_cls






      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	Dict							):






       '''simple docstring'''


       __lowercase									=	get_character()
       if char != KEYMAP["undefined"]:
        __lowercase									=	ord(A_							)
       __lowercase									=	cls.key_handler.get(A_							)
       if handler:
        __lowercase									=	char
        return handler(cls							)
       else:
        return None






def lowerCAmelCase_      (				cls	: int ):






 """simple docstring"""







 return KeyHandler(cls.__name__				,   cls.__bases__				,   cls.__dict__.copy() )
