






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	: Dict      =				logging.get_logger(__name__)
@dataclass
class 					a					:



      """simple docstring"""


      a						:				str
      a						:				str
      a						:				Optional[str]							 =			None
      a						:				Optional[str]							 =			None
      def 			UpperCAmelCase   (   self      :    Any      )							->   Optional[int]:
        return json.dumps(dataclasses.asdict(self      )	,   indent=2      ) + "\n"


@dataclass(frozen=lowercase__     )
class 					a					:



      """simple docstring"""


      a						:				List[int]
      a						:				Optional[List[int]]							 =			None
      a						:				Optional[List[int]]							 =			None
      a						:				Optional[Union[int, float]]							 =			None
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   Any:
        return json.dumps(dataclasses.asdict(self      )      ) + "\n"


class 					a					:



      """simple docstring"""


      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    List[Any]      )							->   Union[str, Any]:
        raise NotImplementedError()
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    List[str]      )							->   Tuple:
        raise NotImplementedError()
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    Tuple      )							->   int:
        raise NotImplementedError()
      def 			UpperCAmelCase   (   self      :    Optional[int]	,   __lowercase      :    Optional[Any]      )							->   int:
        raise NotImplementedError()
      def 			UpperCAmelCase   (   self      :    Any      )							->   int:
        raise NotImplementedError()
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    Union[str, Any]      )							->   Any:
        if len(self.get_labels()      ) > 1:
          __UpperCAmelCase							:			int     =						self.get_labels()[int(example.label      )]
        return example




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


class 					a					(				lowercase__     ):



      """simple docstring"""


      def __init__(   self      :    Dict	,   __lowercase      :    Optional[int]=None	,   __lowercase      :    Union[str, Any]=None	,   __lowercase      :    Any="classification"	,   __lowercase      :    str=False      )							->   Optional[Any]:
        __UpperCAmelCase							:			Dict     =						[] if labels is None else labels
        __UpperCAmelCase							:			List[str]     =						[] if examples is None else examples
        __UpperCAmelCase							:			List[Any]     =						mode
        __UpperCAmelCase							:			List[Any]     =						verbose
      def __len__(   self      :    Optional[int]      )							->   Optional[int]:
        return len(self.examples      )
      def __getitem__(   self      :    Dict	,   __lowercase      :    Union[str, Any]      )							->   Dict:
        if isinstance(__lowercase	,   __lowercase      ):
          return SingleSentenceClassificationProcessor(labels=self.labels	,   examples=self.examples[idx]      )
        return self.examples[idx]
      @classmethod
      def 			UpperCAmelCase   (   cls      :    Optional[int]	,   __lowercase      :    Union[str, Any]	,   __lowercase      :    Any=""	,   __lowercase      :    Optional[int]=0	,   __lowercase      :    Any=1	,   __lowercase      :    Tuple=None	,   __lowercase      :    Optional[Any]=False	,   **__lowercase      :    Union[str, Any]      )							->   Tuple:
        __UpperCAmelCase							:			Union[str, Any]     =						cls(**__lowercase      )
        processor.add_examples_from_csv(
            __lowercase	,   split_name=__lowercase	,   column_label=__lowercase	,   column_text=__lowercase	,   column_id=__lowercase	,   skip_first_row=__lowercase	,   overwrite_labels=__lowercase	,   overwrite_examples=__lowercase	,   )
        return processor
      @classmethod
      def 			UpperCAmelCase   (   cls      :    Optional[int]	,   __lowercase      :    Tuple	,   __lowercase      :    Optional[int]=None	,   **__lowercase      :    Tuple      )							->   Optional[int]:
        __UpperCAmelCase							:			Any     =						cls(**__lowercase      )
        processor.add_examples(__lowercase	,   labels=__lowercase      )
        return processor
      def 			UpperCAmelCase   (   self      :    Union[str, Any]	,   __lowercase      :    Optional[Any]	,   __lowercase      :    Union[str, Any]=""	,   __lowercase      :    Union[str, Any]=0	,   __lowercase      :    str=1	,   __lowercase      :    Dict=None	,   __lowercase      :    Any=False	,   __lowercase      :    Dict=False	,   __lowercase      :    List[Any]=False	,   )							->   List[str]:
        __UpperCAmelCase							:			str     =						self._read_tsv(__lowercase      )
        if skip_first_row:
          __UpperCAmelCase							:			Dict     =						lines[1:]
        __UpperCAmelCase							:			int     =						[]
        __UpperCAmelCase							:			int     =						[]
        __UpperCAmelCase							:			Any     =						[]
        for i, line in enumerate(__lowercase      ):
          texts.append(line[column_text]      )
          labels.append(line[column_label]      )
          if column_id is not None:
            ids.append(line[column_id]      )
          else:
            __UpperCAmelCase							:			Union[str, Any]     =						f"""{split_name}-{i}""" if split_name else str(__lowercase      )
            ids.append(__lowercase      )

        return self.add_examples(
            __lowercase	,   __lowercase	,   __lowercase	,   overwrite_labels=__lowercase	,   overwrite_examples=__lowercase      )
      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    Dict	,   __lowercase      :    Optional[int]=None	,   __lowercase      :    Optional[Any]=None	,   __lowercase      :    Tuple=False	,   __lowercase      :    Tuple=False      )							->   Dict:
        if labels is not None and len(__lowercase      ) != len(__lowercase      ):
          raise ValueError(
              f"""Text and labels have mismatched lengths {len(__lowercase      )} and {len(__lowercase      )}"""      )
        if ids is not None and len(__lowercase      ) != len(__lowercase      ):
          raise ValueError(f"""Text and ids have mismatched lengths {len(__lowercase      )} and {len(__lowercase      )}"""      )
        if ids is None:
          __UpperCAmelCase							:			List[Any]     =						[None] * len(__lowercase      )
        if labels is None:
          __UpperCAmelCase							:			Optional[int]     =						[None] * len(__lowercase      )
        __UpperCAmelCase							:			Any     =						[]
        __UpperCAmelCase							:			Tuple     =						set()
        for text_or_text_and_label, label, guid in zip(__lowercase	,   __lowercase	,   __lowercase      ):
          if isinstance(__lowercase	,   (tuple, list)      ) and label is None:
            __UpperCAmelCase   ,				__UpperCAmelCase							:			Any     =						text_or_text_and_label
          else:
            __UpperCAmelCase							:			Union[str, Any]     =						text_or_text_and_label
          added_labels.add(__lowercase      )
          examples.append(InputExample(guid=__lowercase	,   text_a=__lowercase	,   text_b=__lowercase	,   label=__lowercase      )      )

        # Update examples
        if overwrite_examples:
          __UpperCAmelCase							:			Union[str, Any]     =						examples
        else:
          self.examples.extend(__lowercase      )

        # Update labels
        if overwrite_labels:
          __UpperCAmelCase							:			Any     =						list(__lowercase      )
        else:
          __UpperCAmelCase							:			Optional[int]     =						list(set(self.labels      ).union(__lowercase      )      )

        return self.examples




      def 			UpperCAmelCase   (   self      :    List[str]	,   __lowercase      :    List[Any]	,   __lowercase      :    Dict=None	,   __lowercase      :    Optional[int]=False	,   __lowercase      :    Union[str, Any]=0	,   __lowercase      :    int=True	,   __lowercase      :    Any=None	,   )							->   Any:
        if max_length is None:
          __UpperCAmelCase							:			str     =						tokenizer.max_len

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

        __UpperCAmelCase							:			Optional[Any]     =						[]
        for ex_index, example in enumerate(self.examples      ):
          if ex_index % 10000 == 0:
            logger.info(f"""Tokenizing example {ex_index}"""      )

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

        __UpperCAmelCase							:			Union[str, Any]     =						max(len(__lowercase      ) for input_ids in all_input_ids      )

        __UpperCAmelCase							:			List[Any]     =						[]
        for ex_index, (input_ids, example) in enumerate(zip(__lowercase	,   self.examples      )      ):
          if ex_index % 10000 == 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.
          __UpperCAmelCase							:			Optional[int]     =						[1 if mask_padding_with_zero else 0] * len(__lowercase      )

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

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

          if self.mode == "classification":
            __UpperCAmelCase							:			str     =						label_map[example.label]
          elif self.mode == "regression":
            __UpperCAmelCase							:			Optional[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(__lowercase      ) for x in input_ids]      )}"""      )
            logger.info(f"""attention_mask: {" ".join([str(__lowercase      ) for x in attention_mask]      )}"""      )
            logger.info(f"""label: {example.label} (id = {label})"""      )

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

        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)

          __UpperCAmelCase							:			Union[str, Any]     =						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([]      ))	,   )
          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

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

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










import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


a	: Any      =				set()
a	: List[Any]      =				get_logger(__name__)






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[str] = None	):

  def decorator(__lowerCamelCase    :  Callable	):
    global _emitted_deprecation_warnings

    if inspect.isclass(__lowerCamelCase	):
      __UpperCAmelCase							:			Optional[Any]     =						deprecated_class_or_function.__init__
      __UpperCAmelCase							:			int     =						deprecated_class_or_function.__name__
    else:
      __UpperCAmelCase							:			Any     =						deprecated_class_or_function
      __UpperCAmelCase							:			Any     =						deprecated_function.__name__
      # Support deprecating __init__ class method: class name instead
      __UpperCAmelCase							:			List[str]     =						name if name != """__init__""" else deprecated_function.__qualname__.split("""."""	)[-2]

    __UpperCAmelCase							:			Union[str, Any]     =						(
        f"""{name} is deprecated and will be removed in the next major version of datasets.""" + f""" {help_message}"""
        if help_message
        else """"""
    )

    @wraps(__lowerCamelCase	)
    def wrapper(*__lowerCamelCase    :  List[str]						,	**__lowerCamelCase    :  Tuple	):
      __UpperCAmelCase							:			Optional[Any]     =						hash(__lowerCamelCase	)
      if func_hash not in _emitted_deprecation_warnings:
        warnings.warn(__lowerCamelCase						,	category=__lowerCamelCase						,	stacklevel=2	)
        _emitted_deprecation_warnings.add(__lowerCamelCase	)
      return deprecated_function(*__lowerCamelCase						,	**__lowerCamelCase	)

    __UpperCAmelCase							:			List[str]     =						"""deprecated"""

    if inspect.isclass(__lowerCamelCase	):
      __UpperCAmelCase							:			int     =						wrapper
      return deprecated_class_or_function
    else:
      return wrapper

  return decorator
class 					a					(				enum.EnumMeta     ):



      """simple docstring"""


      def __getattribute__(   cls      :    Optional[int]	,   __lowercase      :    List[Any]      )							->   str:
        __UpperCAmelCase							:			List[Any]     =						super().__getattribute__(__lowercase      )
        if isinstance(__lowercase	,   enum.Enum      ) and obj._on_access:
          obj._on_access()
        return obj
      def __getitem__(   cls      :    Union[str, Any]	,   __lowercase      :    Optional[int]      )							->   Optional[int]:
        __UpperCAmelCase							:			Union[str, Any]     =						super().__getitem__(__lowercase      )
        if member._on_access:
          member._on_access()
        return member




      def __call__(   cls      :    List[str]	,   __lowercase      :    Dict	,   __lowercase      :    List[Any]=None	,   *, __lowercase      :    Dict=None	,   __lowercase      :    Any=None	,   __lowercase      :    Optional[Any]=None	,   __lowercase      :    Dict=1      )							->   List[Any]:
        __UpperCAmelCase							:			Union[str, Any]     =						super().__call__(__lowercase	,   __lowercase	,   module=__lowercase	,   qualname=__lowercase	,   type=__lowercase	,   start=__lowercase      )
        if isinstance(__lowercase	,   enum.Enum      ) and obj._on_access:
          obj._on_access()
        return obj


class 					a					(				enum.Enum							, metaclass=lowercase__     ):



      """simple docstring"""


      def __new__(   cls      :    Dict	,   __lowercase      :    Tuple      )							->   Optional[Any]:
        __UpperCAmelCase							:			List[Any]     =						object.__new__(cls      )
        __UpperCAmelCase							:			Any     =						value
        __UpperCAmelCase							:			int     =						member.deprecate
        return member
      @property
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   Dict:
        return ""




      def 			UpperCAmelCase   (   self      :    str      )							->   Dict:
        __UpperCAmelCase							:			Optional[int]     =						f""" {self.help_message}""" if self.help_message else """"""
        warnings.warn(
            f"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
            + help_message	,   __lowercase	,   stacklevel=3	,   )


