




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


SCREAMING_SNAKE_CASE__ :   Any						=     logging.get_logger(__name__)




@dataclass
class 	UpperCamelCase__			:

    '''simple docstring'''



    lowerCamelCase_   :			str
    lowerCamelCase_   :			str
    lowerCamelCase_   :			Optional[str]			  =   None
    lowerCamelCase_   :			Optional[str]			  =   None







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




@dataclass(frozen=snake_case__  )
class 	UpperCamelCase__			:

    '''simple docstring'''



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







    def 					_lowercase  (					self  )  ->   str:
           return json.dumps(dataclasses.asdict(self  )  ) + "\n"




class 	UpperCamelCase__			:

    '''simple docstring'''



    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   List[Any]:
           raise NotImplementedError()







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   int:
           raise NotImplementedError()







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Tuple:
           raise NotImplementedError()







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Any:
           raise NotImplementedError()







    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           raise NotImplementedError()







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Tuple:
           if len(self.get_labels()  ) > 1:
                  lowerCamelCase     :   Optional[Any]    				=      self.get_labels()[int(example.label  )]
           return example







    @classmethod
    def 					_lowercase  (					cls  ,						UpperCamelCase__  ,						UpperCamelCase__=None  )  ->   int:
           with open(UpperCamelCase__  ,						"r"  ,						encoding="utf-8-sig"  ) as f:
                  return list(csv.reader(UpperCamelCase__  ,						delimiter="\t"  ,						quotechar=UpperCamelCase__  )  )




class 	UpperCamelCase__			(snake_case__  ):

    '''simple docstring'''



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







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







    def __getitem__(					self  ,						UpperCamelCase__  )  ->   List[Any]:
           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__  )  ->   Union[str, Any]:
           lowerCamelCase     :   Any    				=      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__  )  ->   Any:
           lowerCamelCase     :   Tuple    				=      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  ,						)  ->   Union[str, Any]:
           lowerCamelCase     :   Tuple    				=      self._read_tsv(UpperCamelCase__  )
           if skip_first_row:
                  lowerCamelCase     :   List[Any]    				=      lines[1:]
           lowerCamelCase     :   Any    				=      []
           lowerCamelCase     :   str    				=      []
           lowerCamelCase     :   str    				=      []
           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     :   List[str]    				=      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  )  ->   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:
                  lowerCamelCase     :   Any    				=      [None] * len(UpperCamelCase__  )
           if labels is None:
                  lowerCamelCase     :   Any    				=      [None] * len(UpperCamelCase__  )
           lowerCamelCase     :   str    				=      []
           lowerCamelCase     :   int    				=      set()
           for text_or_text_and_label, label, guid in zip(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ):
                  if isinstance(UpperCamelCase__  ,						(tuple, list)  ) and label is None:
                         lowerCamelCase     :   Dict    				=      text_or_text_and_label
                  else:
                         lowerCamelCase     :   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:
                  lowerCamelCase     :   Tuple    				=      examples
           else:
                  self.examples.extend(UpperCamelCase__  )

           # Update labels
           if overwrite_labels:
                  lowerCamelCase     :   int    				=      list(UpperCamelCase__  )
           else:
                  lowerCamelCase     :   List[str]    				=      list(set(self.labels  ).union(UpperCamelCase__  )  )

           return self.examples







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=None  ,						UpperCamelCase__=False  ,						UpperCamelCase__=0  ,						UpperCamelCase__=True  ,						UpperCamelCase__=None  ,						)  ->   Union[str, Any]:
           if max_length is None:
                  lowerCamelCase     :   List[Any]    				=      tokenizer.max_len

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

           lowerCamelCase     :   Union[str, Any]    				=      []
           for ex_index, example in enumerate(self.examples  ):
                  if ex_index % 1_0000 == 0:
                         logger.info(F'''Tokenizing example {ex_index}'''  )

                  lowerCamelCase     :   Tuple    				=      tokenizer.encode(
                      example.text_a  ,						add_special_tokens=UpperCamelCase__  ,						max_length=min(UpperCamelCase__  ,						tokenizer.max_len  )  ,						)
                  all_input_ids.append(UpperCamelCase__  )

           lowerCamelCase     :   Optional[int]    				=      max(len(UpperCamelCase__  ) for input_ids in all_input_ids  )

           lowerCamelCase     :   Dict    				=      []
           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.
                  lowerCamelCase     :   Tuple    				=      [1 if mask_padding_with_zero else 0] * len(UpperCamelCase__  )

                  # Zero-pad up to the sequence length.
                  lowerCamelCase     :   Any    				=      batch_length - len(UpperCamelCase__  )
                  if pad_on_left:
                         lowerCamelCase     :   Any    				=      ([pad_token] * padding_length) + input_ids
                         lowerCamelCase     :   int    				=      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                  else:
                         lowerCamelCase     :   Any    				=      input_ids + ([pad_token] * padding_length)
                         lowerCamelCase     :   Optional[Any]    				=      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     :   Optional[int]    				=      label_map[example.label]
                  elif self.mode == "regression":
                         lowerCamelCase     :   List[str]    				=      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     :   Dict    				=      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     :   Optional[int]    				=      torch.tensor([f.input_ids for f in features]  ,						dtype=torch.long  )
                  lowerCamelCase     :   Dict    				=      torch.tensor([f.attention_mask for f in features]  ,						dtype=torch.long  )
                  if self.mode == "classification":
                         lowerCamelCase     :   Tuple    				=      torch.tensor([f.label for f in features]  ,						dtype=torch.long  )
                  elif self.mode == "regression":
                         lowerCamelCase     :   Union[str, Any]    				=      torch.tensor([f.label for f in features]  ,						dtype=torch.float  )

                  lowerCamelCase     :   Dict    				=      TensorDataset(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )
                  return dataset
           else:
                  raise ValueError("return_tensors should be one of \'tf\' or \'pt\'"  )










SCREAMING_SNAKE_CASE__ :   str						=     {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}

def   A   (   _SCREAMING_SNAKE_CASE					)			->				List[str]:
       return c.isdigit()

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Optional[Any]:
       lowerCamelCase     :   Tuple    				=      []

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

              else:
                     # pop values from stack can calculate the result
                     # push the result onto the stack again
                     lowerCamelCase     :   str    				=      stack.pop()
                     lowerCamelCase     :   Optional[int]    				=      stack.pop()
                     stack.append(calc[c](_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)					)

       return stack.pop()


# Driver code
if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ :   Optional[int]						=     '+ 9 * 2 6'
       print(evaluate(test_expression))

       SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     '/ * 10 2 + 4 1 '
       print(evaluate(test_expression))






