


'''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


_lowercase      :      List[Any]            =					logging.getLogger(__name__)






@dataclass
class 	UpperCamelCase__:
    __magic_name__    :				str
    __magic_name__    :				List[str]
    __magic_name__    :				Optional[List[str]]







@dataclass
class 	UpperCamelCase__:
    __magic_name__    :				List[int]
    __magic_name__    :				List[int]
    __magic_name__    :				Optional[List[int]]  		=	None
    __magic_name__    :				Optional[List[int]]  		=	None







class 	UpperCamelCase__(       lowerCAmelCase	):
    __magic_name__    :				Union[str, Any]  		=	"train"
    __magic_name__    :				Any  		=	"dev"
    __magic_name__    :				Any  		=	"test"







class 	UpperCamelCase__:




    @staticmethod
    def 					a__(		lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Union[Split, str]				)->					List[InputExample]:

          """simple docstring"""




          raise NotImplementedError




    @staticmethod
    def 					a__(		lowerCAmelCase      :			str				)->					List[str]:

          """simple docstring"""




          raise NotImplementedError





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

          """simple docstring"""




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

          UpperCAmelCase					    =     []
          for ex_index, example in enumerate(lowerCAmelCase				):
                if ex_index % 10000 == 0:
                      logger.info('''Writing example %d of %d'''				,   lowerCAmelCase				,   len(lowerCAmelCase				)				)

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

                UpperCAmelCase					    =     tokenizer.convert_tokens_to_ids(lowerCAmelCase				)

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

                # Zero-pad up to the sequence length.
                UpperCAmelCase					    =     max_seq_length - len(lowerCAmelCase				)
                if pad_on_left:
                      UpperCAmelCase					    =     ([pad_token] * padding_length) + input_ids
                      UpperCAmelCase					    =     ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                      UpperCAmelCase					    =     ([pad_token_segment_id] * padding_length) + segment_ids
                      UpperCAmelCase					    =     ([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:
                      UpperCAmelCase					    =     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 	UpperCamelCase__(       lowerCAmelCase	):
     __magic_name__    :				List[InputFeatures]
     __magic_name__    :				int  		=	nn.CrossEntropyLoss().ignore_index




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

           """simple docstring"""




           UpperCAmelCase					    =     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.
           UpperCAmelCase					    =     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}"""				)
                       UpperCAmelCase					    =     torch.load(lowerCAmelCase				)
                 else:
                       logger.info(F"""Creating features from dataset file at {data_dir}"""				)
                       UpperCAmelCase					    =     token_classification_task.read_examples_from_file(lowerCAmelCase				,   lowerCAmelCase				)
                       # TODO clean up all this to leverage built-in features of tokenizers
                       UpperCAmelCase					    =     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      :			int				)->					Any:

           """simple docstring"""




           return len(self.features				)





     def __getitem__(		self      :			Optional[Any]				,   lowerCAmelCase      :			List[Any]				)->					InputFeatures:

           """simple docstring"""




           return self.features[i]


if is_tf_available():
 import tensorflow as tf






 class 	UpperCamelCase__:
     __magic_name__    :				List[InputFeatures]
     __magic_name__    :				int  		=	-100




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

           """simple docstring"""




           UpperCAmelCase					    =     token_classification_task.read_examples_from_file(lowerCAmelCase				,   lowerCAmelCase				)
           # TODO clean up all this to leverage built-in features of tokenizers
           UpperCAmelCase					    =     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:
                 UpperCAmelCase					    =     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:
                 UpperCAmelCase					    =     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 					a__(		self      :			List[Any]				)->					Dict:

           """simple docstring"""




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

           return self.dataset




     def __len__(		self      :			Optional[Any]				)->					Union[str, Any]:

           """simple docstring"""




           return len(self.features				)





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

           """simple docstring"""




           return self.features[i]









'''simple docstring'''



from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)





