

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				      =						logging.getLogger(__name__)

@dataclass
class    _lowercase				:
   _lowercase     :       Optional[int]  					=    42
   _lowercase     :       Any  					=    42
   _lowercase     :       Optional[int]  					=    42



@dataclass
class    _lowercase				:
   _lowercase     :       Optional[int]  					=    42
   _lowercase     :       int  					=    42
   _lowercase     :       Any  					=    None
   _lowercase     :       Dict  					=    None



class    _lowercase				(					lowercase__			):
   _lowercase     :       int  					=    'train'
   _lowercase     :       Any  					=    'dev'
   _lowercase     :       Any  					=    'test'



class    _lowercase				:

   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Union[str, Any]       ) ->			List[InputExample]:
         """simple docstring"""





         raise NotImplementedError

   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         raise NotImplementedError





   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Tuple=False			,						lowerCamelCase__	:    str="[CLS]"			,						lowerCamelCase__	:    Tuple=1			,						lowerCamelCase__	:    Union[str, Any]="[SEP]"			,						lowerCamelCase__	:    Optional[int]=False			,						lowerCamelCase__	:    Tuple=False			,						lowerCamelCase__	:    int=0			,						lowerCamelCase__	:    Optional[int]=0			,						lowerCamelCase__	:    str=-1_0_0			,						lowerCamelCase__	:    Dict=0			,						lowerCamelCase__	:    Dict=True			,						) ->			List[InputFeatures]:
         """simple docstring"""





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

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

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

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

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

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

               A_							       =		tokenizer.convert_tokens_to_ids(lowercase_       )

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

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

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

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

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


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

      class    _lowercase				(					lowercase__			):
         _lowercase     :       Optional[int]  					=    42
         _lowercase     :       Optional[int]  					=    nn.CrossEntropyLoss().ignore_index

         def __init__(				self	:    Any			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict = None			,						lowerCamelCase__	:    Any=False			,						lowerCamelCase__	:    str = Split.train			,						) ->			str:
               """simple docstring"""





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

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

         def __len__(				self	:    Union[str, Any]       ) ->			Optional[int]:
               """simple docstring"""





               return len(self.features       )





         def __getitem__(				self	:    List[Any]			,						lowerCamelCase__	:    str       ) ->			InputFeatures:
               """simple docstring"""





               return self.features[i]


if is_tf_available():
      import tensorflow as tf

      class    _lowercase				:
         _lowercase     :       Dict  					=    42
         _lowercase     :       Dict  					=    -100

         def __init__(				self	:    int			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    List[str] = None			,						lowerCamelCase__	:    Optional[int]=False			,						lowerCamelCase__	:    Optional[Any] = Split.train			,						) ->			Union[str, Any]:
               """simple docstring"""





               A_							       =		token_classification_task.read_examples_from_file(lowercase_			,						lowercase_       )
               # TODO clean up all this to leverage built-in features of tokenizers
               A_							       =		token_classification_task.convert_examples_to_features(
                   lowercase_			,						lowercase_			,						lowercase_			,						lowercase_			,						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=lowercase_			,						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:
                     A_							       =		tf.data.Dataset.from_generator(
                         lowercase_			,						({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)			,						(
                             {'''input_ids''': tf.TensorShape([None]       ), '''attention_mask''': tf.TensorShape([None]       )},
                             tf.TensorShape([None]       ),
                         )			,						)
               else:
                     A_							       =		tf.data.Dataset.from_generator(
                         lowercase_			,						({'''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	:    int       ) ->			int:
               """simple docstring"""





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

               return self.dataset

         def __len__(				self	:    Optional[int]       ) ->			Any:
               """simple docstring"""





               return len(self.features       )





         def __getitem__(				self	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]       ) ->			InputFeatures:
               """simple docstring"""





               return self.features[i]









import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCAmelCase:						Union[str, Any]   				=       '\\n@inproceedings{lin-2004-rouge,\n    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n    author = "Lin, Chin-Yew",\n    booktitle = "Text Summarization Branches Out",\n    month = jul,\n    year = "2004",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W04-1013",\n    pages = "74--81",\n}\n'

_lowerCAmelCase:						Dict   				=       '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

_lowerCAmelCase:						List[Any]   				=       '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION   )
class       lowercase_					(datasets.Metric   ):





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return datasets.MetricInfo(
											    description=_DESCRIPTION       ,  citation=_CITATION       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
											        {
											            'predictions': datasets.Value('string'       ,  id='sequence'),
											            'references': datasets.Value('string'       ,  id='sequence'),
											        })       ,  codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']       ,  reference_urls=[
											        'https://en.wikipedia.org/wiki/ROUGE_(metric)',
											        'https://github.com/google-research/google-research/tree/master/rouge',
											    ]       ,  )





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=False)  -> Any:
											if rouge_types is None:
																		a__      =['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

											a__      =rouge_scorer.RougeScorer(rouge_types=lowercase_       ,  use_stemmer=lowercase_)
											if use_aggregator:
																		a__      =scoring.BootstrapAggregator()
											else:
																		a__      =[]

											for ref, pred in zip(lowercase_       ,  lowercase_):
																		a__      =scorer.score(lowercase_       ,  lowercase_)
																		if use_aggregator:
																									aggregator.add_scores(lowercase_)
																		else:
																									scores.append(lowercase_)

											if use_aggregator:
																		a__      =aggregator.aggregate()
											else:
																		a__      ={}
																		for key in scores[0]:
																									a__      =[score[key] for score in scores]

											return result

