





import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


SCREAMING_SNAKE_CASE_      :			int		  =      logging.get_logger(__name__)

@dataclass
class 		snake_case_      :







	'''simple docstring'''
	__UpperCamelCase        =		42
	__UpperCamelCase        =		42
	__UpperCamelCase        =		None
	__UpperCamelCase        =		None




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   List[str]:

			'''simple docstring'''




			return json.dumps(dataclasses.asdict(self      )				,			indent=2      ) + "\n"





@dataclass(frozen=UpperCamelCase__	)
class 		snake_case_      :







	'''simple docstring'''
	__UpperCamelCase        =		42
	__UpperCamelCase        =		None
	__UpperCamelCase        =		None
	__UpperCamelCase        =		None




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

			'''simple docstring'''




			return json.dumps(dataclasses.asdict(self      )      ) + "\n"





class 		snake_case_      :







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    List[Any]      )					->   Union[str, Any]:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    Tuple      )					->   str:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    Any				,			__lowerCamelCase       :    Any      )					->   List[Any]:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    List[Any]      )					->   Optional[int]:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    int      )					->   Any:

			'''simple docstring'''




			raise NotImplementedError()




	def       UpperCAmelCase (	self       :    Any				,			__lowerCamelCase       :    Union[str, Any]      )					->   Dict:

			'''simple docstring'''




			if len(self.get_labels()      ) > 1:
					__lowercase					=    self.get_labels()[int(example.label      )]
			return example






	@classmethod
	def       UpperCAmelCase (	cls       :    str				,			__lowerCamelCase       :    Optional[Any]				,			__lowerCamelCase       :    str=None      )					->   Union[str, Any]:

			'''simple docstring'''




			with open(lowerCAmelCase__				,			'r'				,			encoding='utf-8-sig'      ) as f:
					return list(csv.reader(lowerCAmelCase__				,			delimiter='\t'				,			quotechar=lowerCAmelCase__      )      )





class 		snake_case_      (       UpperCamelCase__	):







	'''simple docstring'''




	def __init__(	self       :    Tuple				,			__lowerCamelCase       :    Optional[Any]=None				,			__lowerCamelCase       :    int=None				,			__lowerCamelCase       :    List[Any]="classification"				,			__lowerCamelCase       :    str=False      )					->   Any:

			'''simple docstring'''




			__lowercase					=    [] if labels is None else labels
			__lowercase					=    [] if examples is None else examples
			__lowercase					=    mode
			__lowercase					=    verbose




	def __len__(	self       :    List[Any]      )					->   Optional[int]:

			'''simple docstring'''




			return len(self.examples      )




	def __getitem__(	self       :    Any				,			__lowerCamelCase       :    List[Any]      )					->   int:

			'''simple docstring'''




			if isinstance(lowerCAmelCase__				,			lowerCAmelCase__      ):
					return SingleSentenceClassificationProcessor(labels=self.labels				,			examples=self.examples[idx]      )
			return self.examples[idx]




	@classmethod
	def       UpperCAmelCase (	cls       :    List[str]				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    Union[str, Any]=""				,			__lowerCamelCase       :    Optional[Any]=0				,			__lowerCamelCase       :    int=1				,			__lowerCamelCase       :    int=None				,			__lowerCamelCase       :    Dict=False				,			**__lowerCamelCase       :    Union[str, Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    cls(**lowerCAmelCase__      )
			processor.add_examples_from_csv(
			    lowerCAmelCase__				,			split_name=lowerCAmelCase__				,			column_label=lowerCAmelCase__				,			column_text=lowerCAmelCase__				,			column_id=lowerCAmelCase__				,			skip_first_row=lowerCAmelCase__				,			overwrite_labels=lowerCAmelCase__				,			overwrite_examples=lowerCAmelCase__				,			)
			return processor




	@classmethod
	def       UpperCAmelCase (	cls       :    Any				,			__lowerCamelCase       :    Optional[Any]				,			__lowerCamelCase       :    str=None				,			**__lowerCamelCase       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			__lowercase					=    cls(**lowerCAmelCase__      )
			processor.add_examples(lowerCAmelCase__				,			labels=lowerCAmelCase__      )
			return processor




	def       UpperCAmelCase (	self       :    int				,			__lowerCamelCase       :    Optional[Any]				,			__lowerCamelCase       :    List[str]=""				,			__lowerCamelCase       :    Dict=0				,			__lowerCamelCase       :    List[Any]=1				,			__lowerCamelCase       :    List[Any]=None				,			__lowerCamelCase       :    Tuple=False				,			__lowerCamelCase       :    Tuple=False				,			__lowerCamelCase       :    Optional[int]=False				,			)					->   Dict:

			'''simple docstring'''




			__lowercase					=    self._read_tsv(lowerCAmelCase__      )
			if skip_first_row:
					__lowercase					=    lines[1:]
			__lowercase					=    []
			__lowercase					=    []
			__lowercase					=    []
			for i, line in enumerate(lowerCAmelCase__      ):
					texts.append(line[column_text]      )
					labels.append(line[column_label]      )
					if column_id is not None:
							ids.append(line[column_id]      )
					else:
							__lowercase					=    F"{split_name}-{i}" if split_name else str(lowerCAmelCase__      )
							ids.append(lowerCAmelCase__      )

			return self.add_examples(
			    lowerCAmelCase__				,			lowerCAmelCase__				,			lowerCAmelCase__				,			overwrite_labels=lowerCAmelCase__				,			overwrite_examples=lowerCAmelCase__      )




	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    Union[str, Any]=None				,			__lowerCamelCase       :    Dict=None				,			__lowerCamelCase       :    List[Any]=False				,			__lowerCamelCase       :    Dict=False      )					->   List[Any]:

			'''simple docstring'''




			if labels is not None and len(lowerCAmelCase__      ) != len(lowerCAmelCase__      ):
					raise ValueError(
					    F"Text and labels have mismatched lengths {len(lowerCAmelCase__      )} and {len(lowerCAmelCase__      )}"      )
			if ids is not None and len(lowerCAmelCase__      ) != len(lowerCAmelCase__      ):
					raise ValueError(F"Text and ids have mismatched lengths {len(lowerCAmelCase__      )} and {len(lowerCAmelCase__      )}"      )
			if ids is None:
					__lowercase					=    [None] * len(lowerCAmelCase__      )
			if labels is None:
					__lowercase					=    [None] * len(lowerCAmelCase__      )
			__lowercase					=    []
			__lowercase					=    set()
			for text_or_text_and_label, label, guid in zip(lowerCAmelCase__				,			lowerCAmelCase__				,			lowerCAmelCase__      ):
					if isinstance(lowerCAmelCase__				,			(tuple, list)      ) and label is None:
							__lowercase					=    text_or_text_and_label
					else:
							__lowercase					=    text_or_text_and_label
					added_labels.add(lowerCAmelCase__      )
					examples.append(InputExample(guid=lowerCAmelCase__				,			text_a=lowerCAmelCase__				,			text_b=lowerCAmelCase__				,			label=lowerCAmelCase__      )      )

			# Update examples
			if overwrite_examples:
					__lowercase					=    examples
			else:
					self.examples.extend(lowerCAmelCase__      )

			# Update labels
			if overwrite_labels:
					__lowercase					=    list(lowerCAmelCase__      )
			else:
					__lowercase					=    list(set(self.labels      ).union(lowerCAmelCase__      )      )

			return self.examples






	def       UpperCAmelCase (	self       :    Optional[int]				,			__lowerCamelCase       :    Tuple				,			__lowerCamelCase       :    Dict=None				,			__lowerCamelCase       :    Optional[Any]=False				,			__lowerCamelCase       :    str=0				,			__lowerCamelCase       :    Tuple=True				,			__lowerCamelCase       :    Optional[Any]=None				,			)					->   int:

			'''simple docstring'''




			if max_length is None:
					__lowercase					=    tokenizer.max_len

			__lowercase					=    {label: i for i, label in enumerate(self.labels      )}

			__lowercase					=    []
			for ex_index, example in enumerate(self.examples      ):
					if ex_index % 10_000 == 0:
							logger.info(F"Tokenizing example {ex_index}"      )

					__lowercase					=    tokenizer.encode(
					    example.text_a				,			add_special_tokens=lowerCAmelCase__				,			max_length=min(lowerCAmelCase__				,			tokenizer.max_len      )				,			)
					all_input_ids.append(lowerCAmelCase__      )

			__lowercase					=    max(len(lowerCAmelCase__      ) for input_ids in all_input_ids      )

			__lowercase					=    []
			for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__				,			self.examples      )      ):
					if ex_index % 10_000 == 0:
							logger.info(F"Writing example {ex_index}/{len(self.examples      )}"      )
					# 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(lowerCAmelCase__      )

					# Zero-pad up to the sequence length.
					__lowercase					=    batch_length - len(lowerCAmelCase__      )
					if pad_on_left:
							__lowercase					=    ([pad_token] * padding_length) + input_ids
							__lowercase					=    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
					else:
							__lowercase					=    input_ids + ([pad_token] * padding_length)
							__lowercase					=    attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

					if len(lowerCAmelCase__      ) != batch_length:
							raise ValueError(F"Error with input length {len(lowerCAmelCase__      )} vs {batch_length}"      )
					if len(lowerCAmelCase__      ) != batch_length:
							raise ValueError(F"Error with input length {len(lowerCAmelCase__      )} vs {batch_length}"      )

					if self.mode == "classification":
							__lowercase					=    label_map[example.label]
					elif self.mode == "regression":
							__lowercase					=    float(example.label      )
					else:
							raise ValueError(self.mode      )

					if ex_index < 5 and self.verbose:
							logger.info('*** Example ***'      )
							logger.info(F"guid: {example.guid}"      )
							logger.info(F"input_ids: {' '.join([str(lowerCAmelCase__      ) for x in input_ids]      )}"      )
							logger.info(F"attention_mask: {' '.join([str(lowerCAmelCase__      ) for x in attention_mask]      )}"      )
							logger.info(F"label: {example.label} (id = {label})"      )

					features.append(InputFeatures(input_ids=lowerCAmelCase__				,			attention_mask=lowerCAmelCase__				,			label=lowerCAmelCase__      )      )

			if return_tensors is None:
					return features
			elif return_tensors == "tf":
					if not is_tf_available():
							raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'      )
					import tensorflow as tf

					def gen():
							for ex in features:
									yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

					__lowercase					=    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([]      ))				,			)
					return dataset
			elif return_tensors == "pt":
					if not is_torch_available():
							raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'      )
					import torch
					from torch.utils.data import TensorDataset

					__lowercase					=    torch.tensor([f.input_ids for f in features]				,			dtype=torch.long      )
					__lowercase					=    torch.tensor([f.attention_mask for f in features]				,			dtype=torch.long      )
					if self.mode == "classification":
							__lowercase					=    torch.tensor([f.label for f in features]				,			dtype=torch.long      )
					elif self.mode == "regression":
							__lowercase					=    torch.tensor([f.label for f in features]				,			dtype=torch.float      )

					__lowercase					=    TensorDataset(lowerCAmelCase__				,			lowerCAmelCase__				,			lowerCAmelCase__      )
					return dataset
			else:
					raise ValueError('return_tensors should be one of \'tf\' or \'pt\''      )











import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE_      :			Any		  =      '''\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
'''

SCREAMING_SNAKE_CASE_      :			Tuple		  =      '''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

SCREAMING_SNAKE_CASE_      :			Dict		  =      '''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''

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







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Any      )					->   str:

			'''simple docstring'''




			return datasets.MetricInfo(
			    description=_DESCRIPTION				,			citation=_CITATION				,			homepage='https://github.com/chakki-works/seqeval'				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
			        {
			            'predictions': datasets.Sequence(datasets.Value('string'				,			id='label'      )				,			id='sequence'      ),
			            'references': datasets.Sequence(datasets.Value('string'				,			id='label'      )				,			id='sequence'      ),
			        }      )				,			codebase_urls=['https://github.com/chakki-works/seqeval']				,			reference_urls=['https://github.com/chakki-works/seqeval']				,			)






	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    bool = False				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[List[int]] = None				,			__lowerCamelCase       :    Union[str, int] = "warn"				,			)					->   Tuple:

			'''simple docstring'''




			if scheme is not None:
					try:
							__lowercase					=    importlib.import_module('seqeval.scheme'      )
							__lowercase					=    getattr(__lowerCamelCase				,			__lowerCamelCase      )
					except AttributeError:
							raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"      )
			__lowercase					=    classification_report(
			    y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase				,			suffix=__lowerCamelCase				,			output_dict=__lowerCamelCase				,			scheme=__lowerCamelCase				,			mode=__lowerCamelCase				,			sample_weight=__lowerCamelCase				,			zero_division=__lowerCamelCase				,			)
			report.pop('macro avg'      )
			report.pop('weighted avg'      )
			__lowercase					=    report.pop('micro avg'      )

			__lowercase					=    {
			    type_name: {
			        'precision': score['precision'],
			        'recall': score['recall'],
			        'f1': score['f1-score'],
			        'number': score['support'],
			    }
			    for type_name, score in report.items()
			}
			__lowercase					=    overall_score['precision']
			__lowercase					=    overall_score['recall']
			__lowercase					=    overall_score['f1-score']
			__lowercase					=    accuracy_score(y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase      )

			return scores




