


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


lowerCAmelCase__						=			logging.get_logger(__name__)







@dataclass
class 					lowerCAmelCase__	:




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			42
     __SCREAMING_SNAKE_CASE        =			42
     __SCREAMING_SNAKE_CASE        =			None
     __SCREAMING_SNAKE_CASE        =			None


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







@dataclass(frozen=a)
class 					lowerCAmelCase__	:




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			42
     __SCREAMING_SNAKE_CASE        =			None
     __SCREAMING_SNAKE_CASE        =			None
     __SCREAMING_SNAKE_CASE        =			None


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            return json.dumps(dataclasses.asdict(self)) + "\n"







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Optional[int]:
            raise NotImplementedError()


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    List[str]:
            raise NotImplementedError()


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    int:
            raise NotImplementedError()


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Any:
            raise NotImplementedError()


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            raise NotImplementedError()


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    str:
            if len(self.get_labels()) > 1:
                   _A				:	Optional[Any]       =			self.get_labels()[int(example.label)]
            return example


     @classmethod
     def 			_lowerCamelCase			(						cls			,						__lowerCamelCase			,						__lowerCamelCase=None)      ->    List[str]:
            with open(__lowerCamelCase			,						"r"			,						encoding="utf-8-sig") as f:
                   return list(csv.reader(__lowerCamelCase			,						delimiter="\t"			,						quotechar=__lowerCamelCase))







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''


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


     def __len__(						self)      ->    Union[str, Any]:
            return len(self.examples)


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


     @classmethod
     def 			_lowerCamelCase			(						cls			,						__lowerCamelCase			,						__lowerCamelCase=""			,						__lowerCamelCase=0			,						__lowerCamelCase=1			,						__lowerCamelCase=None			,						__lowerCamelCase=False			,						**__lowerCamelCase)      ->    List[str]:
            _A				:	Optional[int]       =			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 			_lowerCamelCase			(						cls			,						__lowerCamelCase			,						__lowerCamelCase=None			,						**__lowerCamelCase)      ->    Dict:
            _A				:	str       =			cls(**__lowerCamelCase)
            processor.add_examples(__lowerCamelCase			,						labels=__lowerCamelCase)
            return processor


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase=""			,						__lowerCamelCase=0			,						__lowerCamelCase=1			,						__lowerCamelCase=None			,						__lowerCamelCase=False			,						__lowerCamelCase=False			,						__lowerCamelCase=False			,						)      ->    Tuple:
            _A				:	List[str]       =			self._read_tsv(__lowerCamelCase)
            if skip_first_row:
                   _A				:	Optional[Any]       =			lines[1:]
            _A				:	List[str]       =			[]
            _A				:	Tuple       =			[]
            _A				:	Any       =			[]
            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:
                          _A				:	Union[str, Any]       =			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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						__lowerCamelCase=False			,						__lowerCamelCase=False)      ->    Any:
            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:
                   _A				:	List[str]       =			[None] * len(__lowerCamelCase)
            if labels is None:
                   _A				:	int       =			[None] * len(__lowerCamelCase)
            _A				:	List[str]       =			[]
            _A				:	Union[str, Any]       =			set()
            for text_or_text_and_label, label, guid in zip(__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase):
                   if isinstance(__lowerCamelCase			,						(tuple, list)) and label is None:
                          _A   ,      _A				:	str       =			text_or_text_and_label
                   else:
                          _A				:	Union[str, Any]       =			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:
                   _A				:	Dict       =			examples
            else:
                   self.examples.extend(__lowerCamelCase)

            # Update labels
            if overwrite_labels:
                   _A				:	Optional[Any]       =			list(__lowerCamelCase)
            else:
                   _A				:	str       =			list(set(self.labels).union(__lowerCamelCase))

            return self.examples


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase=None			,						__lowerCamelCase=False			,						__lowerCamelCase=0			,						__lowerCamelCase=True			,						__lowerCamelCase=None			,						)      ->    List[Any]:
            if max_length is None:
                   _A				:	List[Any]       =			tokenizer.max_len

            _A				:	List[str]       =			{label: i for i, label in enumerate(self.labels)}

            _A				:	Union[str, Any]       =			[]
            for ex_index, example in enumerate(self.examples):
                   if ex_index % 1_0_0_0_0 == 0:
                          logger.info(F"Tokenizing example {ex_index}")

                   _A				:	List[str]       =			tokenizer.encode(
                       example.text_a			,						add_special_tokens=__lowerCamelCase			,						max_length=min(__lowerCamelCase			,						tokenizer.max_len)			,						)
                   all_input_ids.append(__lowerCamelCase)

            _A				:	Tuple       =			max(len(__lowerCamelCase) for input_ids in all_input_ids)

            _A				:	Any       =			[]
            for ex_index, (input_ids, example) in enumerate(zip(__lowerCamelCase			,						self.examples)):
                   if ex_index % 1_0_0_0_0 == 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				:	Optional[int]       =			[1 if mask_padding_with_zero else 0] * len(__lowerCamelCase)

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

                   _A				:	Union[str, Any]       =			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

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

                   _A				:	Union[str, Any]       =			TensorDataset(__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)
                   return dataset
            else:
                   raise ValueError("return_tensors should be one of 'tf' or 'pt'")




def 							_UpperCAmelCase    (UpperCamelCase__					:  str						):
       _A				:	Tuple       =			str(UpperCamelCase__						).strip()
       if not bin_string:
              raise ValueError("Empty string was passed to the function"						)
       _A				:	Dict       =			bin_string[0] == "-"
       if is_negative:
              _A				:	Any       =			bin_string[1:]
       if not all(char in "01" for char in bin_string						):
              raise ValueError("Non-binary value was passed to the function"						)
       _A				:	Union[str, Any]       =			0
       for char in bin_string:
              _A				:	Union[str, Any]       =			2 * decimal_number + int(UpperCamelCase__						)
       return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()
