




'''simple docstring'''





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


a		:	str       =      logging.get_logger(__name__)




@dataclass
class      UpperCamelCase       :
  a     :str
  a     :str
  a     :Optional[str]          =					None
  a     :Optional[str]          =					None



  def     a_							(			self ):







      '''simple docstring'''


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






@dataclass(frozen=snake_case__       )
class      UpperCamelCase       :
  a     :List[int]
  a     :Optional[List[int]]          =					None
  a     :Optional[List[int]]          =					None
  a     :Optional[Union[int, float]]          =					None



  def     a_							(			self ):







      '''simple docstring'''


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






class      UpperCamelCase       :



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


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



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase=None ):







      '''simple docstring'''


      with open(UpperCamelCase_		,     """r"""		,     encoding="""utf-8-sig""" ) as f:
          return list(csv.reader(UpperCamelCase_		,     delimiter="""\t"""		,     quotechar=UpperCamelCase_ ) )






class      UpperCamelCase       (	snake_case__       ):



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase="classification"		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      [] if labels is None else labels
      _a				:							str							       =      [] if examples is None else examples
      _a				:							Any							       =      mode
      _a				:							Tuple							       =      verbose



  def __len__(			self ):







      '''simple docstring'''


      return len(self.examples )



  def __getitem__(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


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



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase=""		,     __lowerCAmelCase=0		,     __lowerCAmelCase=1		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      cls(**UpperCamelCase_ )
      processor.add_examples_from_csv(
          UpperCamelCase_		,     split_name=UpperCamelCase_		,     column_label=UpperCamelCase_		,     column_text=UpperCamelCase_		,     column_id=UpperCamelCase_		,     skip_first_row=UpperCamelCase_		,     overwrite_labels=UpperCamelCase_		,     overwrite_examples=UpperCamelCase_		,     )
      return processor



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							int							       =      cls(**UpperCamelCase_ )
      processor.add_examples(UpperCamelCase_		,     labels=UpperCamelCase_ )
      return processor



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=""		,     __lowerCAmelCase=0		,     __lowerCAmelCase=1		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self._read_tsv(UpperCamelCase_ )
      if skip_first_row:
          _a				:							Optional[Any]							       =      lines[1:]
      _a				:							Any							       =      []
      _a				:							str							       =      []
      _a				:							Any							       =      []
      for i, line in enumerate(UpperCamelCase_ ):
          texts.append(line[column_text] )
          labels.append(line[column_label] )
          if column_id is not None:
              ids.append(line[column_id] )
          else:
              _a				:							Optional[int]							       =      f'''{split_name}-{i}''' if split_name else str(UpperCamelCase_ )
              ids.append(UpperCamelCase_ )

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



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      if labels is not None and len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
          raise ValueError(
              f'''Text and labels have mismatched lengths {len(UpperCamelCase_ )} and {len(UpperCamelCase_ )}''' )
      if ids is not None and len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
          raise ValueError(f'''Text and ids have mismatched lengths {len(UpperCamelCase_ )} and {len(UpperCamelCase_ )}''' )
      if ids is None:
          _a				:							Tuple							       =      [None] * len(UpperCamelCase_ )
      if labels is None:
          _a				:							Tuple							       =      [None] * len(UpperCamelCase_ )
      _a				:							Optional[Any]							       =      []
      _a				:							Optional[Any]							       =      set()
      for text_or_text_and_label, label, guid in zip(UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_ ):
          if isinstance(UpperCamelCase_		,     (tuple, list) ) and label is None:
              _a				:							Dict							       =      text_or_text_and_label
          else:
              _a				:							List[str]							       =      text_or_text_and_label
          added_labels.add(UpperCamelCase_ )
          examples.append(InputExample(guid=UpperCamelCase_		,     text_a=UpperCamelCase_		,     text_b=UpperCamelCase_		,     label=UpperCamelCase_ ) )

      # Update examples
      if overwrite_examples:
          _a				:							Optional[Any]							       =      examples
      else:
          self.examples.extend(UpperCamelCase_ )

      # Update labels
      if overwrite_labels:
          _a				:							Tuple							       =      list(UpperCamelCase_ )
      else:
          _a				:							Optional[int]							       =      list(set(self.labels ).union(UpperCamelCase_ ) )

      return self.examples



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=0		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     ):







      '''simple docstring'''


      if max_length is None:
          _a				:							List[str]							       =      tokenizer.max_len

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

      _a				:							int							       =      []
      for ex_index, example in enumerate(self.examples ):
          if ex_index % 1_0000 == 0:
              logger.info(f'''Tokenizing example {ex_index}''' )

          _a				:							List[Any]							       =      tokenizer.encode(
              example.text_a		,     add_special_tokens=UpperCamelCase_		,     max_length=min(UpperCamelCase_		,     tokenizer.max_len )		,     )
          all_input_ids.append(UpperCamelCase_ )

      _a				:							int							       =      max(len(UpperCamelCase_ ) for input_ids in all_input_ids )

      _a				:							int							       =      []
      for ex_index, (input_ids, example) in enumerate(zip(UpperCamelCase_		,     self.examples ) ):
          if ex_index % 1_0000 == 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.
          _a				:							Any							       =      [1 if mask_padding_with_zero else 0] * len(UpperCamelCase_ )

          # Zero-pad up to the sequence length.
          _a				:							List[str]							       =      batch_length - len(UpperCamelCase_ )
          if pad_on_left:
              _a				:							List[str]							       =      ([pad_token] * padding_length) + input_ids
              _a				:							Optional[Any]							       =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
          else:
              _a				:							Union[str, Any]							       =      input_ids + ([pad_token] * padding_length)
              _a				:							int							       =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

          if len(UpperCamelCase_ ) != batch_length:
              raise ValueError(f'''Error with input length {len(UpperCamelCase_ )} vs {batch_length}''' )
          if len(UpperCamelCase_ ) != batch_length:
              raise ValueError(f'''Error with input length {len(UpperCamelCase_ )} vs {batch_length}''' )

          if self.mode == "classification":
              _a				:							Dict							       =      label_map[example.label]
          elif self.mode == "regression":
              _a				:							List[Any]							       =      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(UpperCamelCase_ ) for x in input_ids] )}''' )
              logger.info(f'''attention_mask: {' '.join([str(UpperCamelCase_ ) for x in attention_mask] )}''' )
              logger.info(f'''label: {example.label} (id = {label})''' )

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

      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)

          _a				:							List[Any]							       =      tf.data.Dataset.from_generator(
              UpperCamelCase_		,     ({"""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

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

          _a				:							Optional[int]							       =      TensorDataset(UpperCamelCase_		,     UpperCamelCase_		,     UpperCamelCase_ )
          return dataset
      else:
          raise ValueError("""return_tensors should be one of \'tf\' or \'pt\'""" )








from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCAmelCase__    :							Tuple       		=     [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCAmelCase__    :							Tuple       		=     get_logger(__name__)


UpperCAmelCase__    :							List[Any]       		=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Optional[TaskTemplate]:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     task_template_dict.get('task'				)
  if task_name is None:
    logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(UpperCamelCase				)}'				)
    return None
  _lowercase:			int      =     NAMEaTEMPLATE.get(UpperCamelCase				)
  return template.from_dict(UpperCamelCase				)
