
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


_snake_case		  =   logging.get_logger(__name__)


@dataclass
class 			UpperCamelCase_				:




      '''simple docstring'''







      a		:str
      a		:str
      a		:Optional[str]						 =      None
      a		:Optional[str]						 =      None







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


@dataclass(frozen=A     )
class 			UpperCamelCase_				:




      '''simple docstring'''







      a		:List[int]
      a		:Optional[List[int]]						 =      None
      a		:Optional[List[int]]						 =      None
      a		:Optional[Union[int, float]]						 =      None







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


class 			UpperCamelCase_				:




      '''simple docstring'''







      def lowercase__		( self					,					_UpperCAmelCase):
            raise NotImplementedError()







      def lowercase__		( self					,					_UpperCAmelCase):
            raise NotImplementedError()







      def lowercase__		( self					,					_UpperCAmelCase):
            raise NotImplementedError()







      def lowercase__		( self					,					_UpperCAmelCase):
            raise NotImplementedError()







      def lowercase__		( self):
            raise NotImplementedError()







      def lowercase__		( self					,					_UpperCAmelCase):
            if len(self.get_labels()) > 1:
                  lowerCAmelCase_						=      self.get_labels()[int(example.label)]
            return example







      @classmethod
      def lowercase__		( cls					,					_UpperCAmelCase					,					_UpperCAmelCase=None):
            with open(_UpperCAmelCase					,					'''r'''					,					encoding='''utf-8-sig''') as f:
                  return list(csv.reader(_UpperCAmelCase					,					delimiter='''\t'''					,					quotechar=_UpperCAmelCase))


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      def __init__( self					,					_UpperCAmelCase=None					,					_UpperCAmelCase=None					,					_UpperCAmelCase="classification"					,					_UpperCAmelCase=False):
            lowerCAmelCase_						=      [] if labels is None else labels
            lowerCAmelCase_						=      [] if examples is None else examples
            lowerCAmelCase_						=      mode
            lowerCAmelCase_						=      verbose







      def __len__( self):
            return len(self.examples)







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







      @classmethod
      def lowercase__		( cls					,					_UpperCAmelCase					,					_UpperCAmelCase=""					,					_UpperCAmelCase=0					,					_UpperCAmelCase=1					,					_UpperCAmelCase=None					,					_UpperCAmelCase=False					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      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 lowercase__		( cls					,					_UpperCAmelCase					,					_UpperCAmelCase=None					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      cls(**_UpperCAmelCase)
            processor.add_examples(_UpperCAmelCase					,					labels=_UpperCAmelCase)
            return processor







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase=""					,					_UpperCAmelCase=0					,					_UpperCAmelCase=1					,					_UpperCAmelCase=None					,					_UpperCAmelCase=False					,					_UpperCAmelCase=False					,					_UpperCAmelCase=False					,					):
            lowerCAmelCase_						=      self._read_tsv(_UpperCAmelCase)
            if skip_first_row:
                  lowerCAmelCase_						=      lines[1:]
            lowerCAmelCase_						=      []
            lowerCAmelCase_						=      []
            lowerCAmelCase_						=      []
            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:
                        lowerCAmelCase_						=      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 lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase=None					,					_UpperCAmelCase=None					,					_UpperCAmelCase=False					,					_UpperCAmelCase=False):
            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:
                  lowerCAmelCase_						=      [None] * len(_UpperCAmelCase)
            if labels is None:
                  lowerCAmelCase_						=      [None] * len(_UpperCAmelCase)
            lowerCAmelCase_						=      []
            lowerCAmelCase_						=      set()
            for text_or_text_and_label, label, guid in zip(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
                  if isinstance(_UpperCAmelCase					,					(tuple, list)) and label is None:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      text_or_text_and_label
                  else:
                        lowerCAmelCase_						=      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:
                  lowerCAmelCase_						=      examples
            else:
                  self.examples.extend(_UpperCAmelCase)

            # Update labels
            if overwrite_labels:
                  lowerCAmelCase_						=      list(_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      list(set(self.labels).union(_UpperCAmelCase))

            return self.examples







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase=None					,					_UpperCAmelCase=False					,					_UpperCAmelCase=0					,					_UpperCAmelCase=True					,					_UpperCAmelCase=None					,					):
            if max_length is None:
                  lowerCAmelCase_						=      tokenizer.max_len

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

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

                  lowerCAmelCase_						=      tokenizer.encode(
                      example.text_a					,					add_special_tokens=_UpperCAmelCase					,					max_length=min(_UpperCAmelCase					,					tokenizer.max_len)					,					)
                  all_input_ids.append(_UpperCAmelCase)

            lowerCAmelCase_						=      max(len(_UpperCAmelCase) for input_ids in all_input_ids)

            lowerCAmelCase_						=      []
            for ex_index, (input_ids, example) in enumerate(zip(_UpperCAmelCase					,					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.
                  lowerCAmelCase_						=      [1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase)

                  # Zero-pad up to the sequence length.
                  lowerCAmelCase_						=      batch_length - len(_UpperCAmelCase)
                  if pad_on_left:
                        lowerCAmelCase_						=      ([pad_token] * padding_length) + input_ids
                        lowerCAmelCase_						=      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                  else:
                        lowerCAmelCase_						=      input_ids + ([pad_token] * padding_length)
                        lowerCAmelCase_						=      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":
                        lowerCAmelCase_						=      label_map[example.label]
                  elif self.mode == "regression":
                        lowerCAmelCase_						=      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)

                  lowerCAmelCase_						=      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

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

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





_snake_case		  =   {
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      return c.isdigit()
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      lowerCAmelCase_						=      []

      # iterate over the string in reverse order
      for c in expression.split()[::-1]:
            # push operand to stack
            if is_operand(A						):
                  stack.append(int(A						)						)

            else:
                  # pop values from stack can calculate the result
                  # push the result onto the stack again
                  lowerCAmelCase_						=      stack.pop()
                  lowerCAmelCase_						=      stack.pop()
                  stack.append(calc[c](A		,					A						)						)

      return stack.pop()


# Driver code
if __name__ == "__main__":
 _snake_case		  =   "+ 9 * 2 6"
 print(evaluate(test_expression))

 _snake_case		  =   "/ * 10 2 + 4 1 "
 print(evaluate(test_expression))



