




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


UpperCAmelCase__    :							str       		=     logging.get_logger(__name__)

@dataclass
class   UpperCAmelCase    :
   __SCREAMING_SNAKE_CASE		:				str
   __SCREAMING_SNAKE_CASE		:				str
   __SCREAMING_SNAKE_CASE		:				Optional[str]      					=       None
   __SCREAMING_SNAKE_CASE		:				Optional[str]      					=       None






   def 			_a    (						self :							Any  )->      List[str]:
     return json.dumps(dataclasses.asdict(self  )    ,       indent=2  ) + "\n"

@dataclass(frozen=A )
class   UpperCAmelCase    :
   __SCREAMING_SNAKE_CASE		:				List[int]
   __SCREAMING_SNAKE_CASE		:				Optional[List[int]]      					=       None
   __SCREAMING_SNAKE_CASE		:				Optional[List[int]]      					=       None
   __SCREAMING_SNAKE_CASE		:				Optional[Union[int, float]]      					=       None






   def 			_a    (						self :							List[Any]  )->      Optional[int]:
     return json.dumps(dataclasses.asdict(self  )  ) + "\n"

class   UpperCAmelCase    :






   def 			_a    (						self :							List[str]    ,       UpperCamelCase_ :							List[Any]  )->      Dict:
     raise NotImplementedError()






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							int  )->      List[Any]:
     raise NotImplementedError()






   def 			_a    (						self :							int    ,       UpperCamelCase_ :							Any  )->      int:
     raise NotImplementedError()






   def 			_a    (						self :							Dict    ,       UpperCamelCase_ :							Union[str, Any]  )->      str:
     raise NotImplementedError()






   def 			_a    (						self :							Any  )->      str:
     raise NotImplementedError()






   def 			_a    (						self :							Dict    ,       UpperCamelCase_ :							str  )->      Dict:
     if len(self.get_labels()  ) > 1:
       _lowercase:			Any      =     self.get_labels()[int(example.label  )]
     return example






   @classmethod
   def 			_a    (						cls :							List[Any]    ,       UpperCamelCase_ :							Dict    ,       UpperCamelCase_ :							Dict=None  )->      str:
     with open(UpperCamelCase_    ,       'r'    ,       encoding='utf-8-sig'  ) as f:
       return list(csv.reader(UpperCamelCase_    ,       delimiter='\t'    ,       quotechar=UpperCamelCase_  )  )

class   UpperCAmelCase    (      A ):






   def __init__(						self :							int    ,       UpperCamelCase_ :							Optional[Any]=None    ,       UpperCamelCase_ :							Tuple=None    ,       UpperCamelCase_ :							Optional[int]="classification"    ,       UpperCamelCase_ :							Dict=False  )->      Union[str, Any]:
     _lowercase:			Optional[int]      =     [] if labels is None else labels
     _lowercase:			str      =     [] if examples is None else examples
     _lowercase:			Any      =     mode
     _lowercase:			Tuple      =     verbose






   def __len__(						self :							List[Any]  )->      List[Any]:
     return len(self.examples  )






   def __getitem__(						self :							int    ,       UpperCamelCase_ :							Any  )->      Tuple:
     if isinstance(UpperCamelCase_    ,       UpperCamelCase_  ):
       return SingleSentenceClassificationProcessor(labels=self.labels    ,       examples=self.examples[idx]  )
     return self.examples[idx]






   @classmethod
   def 			_a    (						cls :							List[str]    ,       UpperCamelCase_ :							Dict    ,       UpperCamelCase_ :							Optional[int]=""    ,       UpperCamelCase_ :							Optional[Any]=0    ,       UpperCamelCase_ :							List[str]=1    ,       UpperCamelCase_ :							Dict=None    ,       UpperCamelCase_ :							List[Any]=False    ,       **UpperCamelCase_ :							Optional[int]  )->      Optional[Any]:
     _lowercase:			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 :							int    ,       UpperCamelCase_ :							Union[str, Any]    ,       UpperCamelCase_ :							Optional[int]=None    ,       **UpperCamelCase_ :							Union[str, Any]  )->      Optional[Any]:
     _lowercase:			int      =     cls(**UpperCamelCase_  )
     processor.add_examples(UpperCamelCase_    ,       labels=UpperCamelCase_  )
     return processor






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							Any    ,       UpperCamelCase_ :							str=""    ,       UpperCamelCase_ :							Optional[int]=0    ,       UpperCamelCase_ :							Optional[int]=1    ,       UpperCamelCase_ :							Optional[int]=None    ,       UpperCamelCase_ :							Union[str, Any]=False    ,       UpperCamelCase_ :							List[str]=False    ,       UpperCamelCase_ :							Optional[Any]=False    ,       )->      Optional[Any]:
     _lowercase:			List[Any]      =     self._read_tsv(UpperCamelCase_  )
     if skip_first_row:
       _lowercase:			Optional[Any]      =     lines[1:]
     _lowercase:			Any      =     []
     _lowercase:			str      =     []
     _lowercase:			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:
         _lowercase:			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 :							List[str]    ,       UpperCamelCase_ :							Dict    ,       UpperCamelCase_ :							int=None    ,       UpperCamelCase_ :							str=None    ,       UpperCamelCase_ :							Dict=False    ,       UpperCamelCase_ :							List[str]=False  )->      int:
     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:
       _lowercase:			Tuple      =     [None] * len(UpperCamelCase_  )
     if labels is None:
       _lowercase:			Tuple      =     [None] * len(UpperCamelCase_  )
     _lowercase:			Optional[Any]      =     []
     _lowercase:			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:
         _lowercase  ,       _lowercase:			Dict      =     text_or_text_and_label
       else:
         _lowercase:			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:
       _lowercase:			Optional[Any]      =     examples
     else:
       self.examples.extend(UpperCamelCase_  )

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

     return self.examples






   def 			_a    (						self :							Optional[int]    ,       UpperCamelCase_ :							Tuple    ,       UpperCamelCase_ :							Dict=None    ,       UpperCamelCase_ :							List[str]=False    ,       UpperCamelCase_ :							Tuple=0    ,       UpperCamelCase_ :							Dict=True    ,       UpperCamelCase_ :							Tuple=None    ,       )->      Optional[Any]:
     if max_length is None:
       _lowercase:			List[str]      =     tokenizer.max_len

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

     _lowercase:			int      =     []
     for ex_index, example in enumerate(self.examples  ):
       if ex_index % 1_00_00 == 0:
         logger.info(f'Tokenizing example {ex_index}'  )

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

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

     _lowercase:			int      =     []
     for ex_index, (input_ids, example) in enumerate(zip(UpperCamelCase_    ,       self.examples  )  ):
       if ex_index % 1_00_00 == 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:			Any      =     [1 if mask_padding_with_zero else 0] * len(UpperCamelCase_  )

       # Zero-pad up to the sequence length.
       _lowercase:			List[str]      =     batch_length - len(UpperCamelCase_  )
       if pad_on_left:
         _lowercase:			List[str]      =     ([pad_token] * padding_length) + input_ids
         _lowercase:			Optional[Any]      =     ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
       else:
         _lowercase:			Union[str, Any]      =     input_ids + ([pad_token] * padding_length)
         _lowercase:			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":
         _lowercase:			Dict      =     label_map[example.label]
       elif self.mode == "regression":
         _lowercase:			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)

       _lowercase:			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

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

       _lowercase:			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				)
