





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				       = logging.get_logger(__name__)



@dataclass
class 							_UpperCamelCase    :
       _UpperCamelCase :    str
       _UpperCamelCase :    str
       _UpperCamelCase :    Optional[str]   			=      None
       _UpperCamelCase :    Optional[str]   			=      None

       def lowercase       (						self:       Optional[Any]						)				->       str:




              """simple docstring"""


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



@dataclass(frozen=lowerCAmelCase_   )
class 							_UpperCamelCase    :
       _UpperCamelCase :    List[int]
       _UpperCamelCase :    Optional[List[int]]   			=      None
       _UpperCamelCase :    Optional[List[int]]   			=      None
       _UpperCamelCase :    Optional[Union[int, float]]   			=      None

       def lowercase       (						self:       Optional[int]						)				->       Optional[Any]:




              """simple docstring"""


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



class 							_UpperCamelCase    :

       def lowercase       (						self:       str    ,      _SCREAMING_SNAKE_CASE:       Any						)				->       Union[str, Any]:




              """simple docstring"""


              raise NotImplementedError()

       def lowercase       (						self:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       str						)				->       int:




              """simple docstring"""


              raise NotImplementedError()

       def lowercase       (						self:       Tuple    ,      _SCREAMING_SNAKE_CASE:       List[Any]						)				->       Dict:




              """simple docstring"""


              raise NotImplementedError()

       def lowercase       (						self:       Any    ,      _SCREAMING_SNAKE_CASE:       Optional[Any]						)				->       Dict:




              """simple docstring"""


              raise NotImplementedError()

       def lowercase       (						self:       int						)				->       Optional[Any]:




              """simple docstring"""


              raise NotImplementedError()

       def lowercase       (						self:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Dict						)				->       Any:




              """simple docstring"""


              if len(self.get_labels()						) > 1:
                     UpperCamelCase_      				=     self.get_labels()[int(example.label						)]
              return example



       @classmethod
       def lowercase       (						cls:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Tuple=None						)				->       Tuple:




              """simple docstring"""


              with open(_SCREAMING_SNAKE_CASE    ,      "r"    ,      encoding="utf-8-sig"						) as f:
                     return list(csv.reader(_SCREAMING_SNAKE_CASE    ,      delimiter="\t"    ,      quotechar=_SCREAMING_SNAKE_CASE						)						)



class 							_UpperCamelCase    (		lowerCAmelCase_   ):

       def __init__(						self:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]=None    ,      _SCREAMING_SNAKE_CASE:       List[str]=None    ,      _SCREAMING_SNAKE_CASE:       Tuple="classification"    ,      _SCREAMING_SNAKE_CASE:       Tuple=False						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_      				=     [] if labels is None else labels
              UpperCamelCase_      				=     [] if examples is None else examples
              UpperCamelCase_      				=     mode
              UpperCamelCase_      				=     verbose

       def __len__(						self:       Dict						)				->       List[str]:




              """simple docstring"""


              return len(self.examples						)

       def __getitem__(						self:       Union[str, Any]    ,      _SCREAMING_SNAKE_CASE:       List[str]						)				->       str:




              """simple docstring"""


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

       @classmethod
       def lowercase       (						cls:       Any    ,      _SCREAMING_SNAKE_CASE:       int    ,      _SCREAMING_SNAKE_CASE:       Tuple=""    ,      _SCREAMING_SNAKE_CASE:       Any=0    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]=1    ,      _SCREAMING_SNAKE_CASE:       str=None    ,      _SCREAMING_SNAKE_CASE:       int=False    ,      **_SCREAMING_SNAKE_CASE:       str						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_      				=     cls(**_SCREAMING_SNAKE_CASE						)
              processor.add_examples_from_csv(
                  _SCREAMING_SNAKE_CASE    ,      split_name=_SCREAMING_SNAKE_CASE    ,      column_label=_SCREAMING_SNAKE_CASE    ,      column_text=_SCREAMING_SNAKE_CASE    ,      column_id=_SCREAMING_SNAKE_CASE    ,      skip_first_row=_SCREAMING_SNAKE_CASE    ,      overwrite_labels=_SCREAMING_SNAKE_CASE    ,      overwrite_examples=_SCREAMING_SNAKE_CASE    ,      )
              return processor

       @classmethod
       def lowercase       (						cls:       Any    ,      _SCREAMING_SNAKE_CASE:       Optional[int]    ,      _SCREAMING_SNAKE_CASE:       Tuple=None    ,      **_SCREAMING_SNAKE_CASE:       List[str]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_      				=     cls(**_SCREAMING_SNAKE_CASE						)
              processor.add_examples(_SCREAMING_SNAKE_CASE    ,      labels=_SCREAMING_SNAKE_CASE						)
              return processor

       def lowercase       (						self:       Tuple    ,      _SCREAMING_SNAKE_CASE:       int    ,      _SCREAMING_SNAKE_CASE:       int=""    ,      _SCREAMING_SNAKE_CASE:       Optional[int]=0    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]=1    ,      _SCREAMING_SNAKE_CASE:       Any=None    ,      _SCREAMING_SNAKE_CASE:       Dict=False    ,      _SCREAMING_SNAKE_CASE:       int=False    ,      _SCREAMING_SNAKE_CASE:       Optional[int]=False    ,      )				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     self._read_tsv(_SCREAMING_SNAKE_CASE						)
              if skip_first_row:
                     UpperCamelCase_      				=     lines[1:]
              UpperCamelCase_      				=     []
              UpperCamelCase_      				=     []
              UpperCamelCase_      				=     []
              for i, line in enumerate(_SCREAMING_SNAKE_CASE						):
                     texts.append(line[column_text]						)
                     labels.append(line[column_label]						)
                     if column_id is not None:
                            ids.append(line[column_id]						)
                     else:
                            UpperCamelCase_      				=     f'''{split_name}-{i}''' if split_name else str(_SCREAMING_SNAKE_CASE						)
                            ids.append(_SCREAMING_SNAKE_CASE						)

              return self.add_examples(
                  _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      overwrite_labels=_SCREAMING_SNAKE_CASE    ,      overwrite_examples=_SCREAMING_SNAKE_CASE						)

       def lowercase       (						self:       Any    ,      _SCREAMING_SNAKE_CASE:       List[str]    ,      _SCREAMING_SNAKE_CASE:       Any=None    ,      _SCREAMING_SNAKE_CASE:       int=None    ,      _SCREAMING_SNAKE_CASE:       int=False    ,      _SCREAMING_SNAKE_CASE:       List[Any]=False						)				->       Optional[Any]:




              """simple docstring"""


              if labels is not None and len(_SCREAMING_SNAKE_CASE						) != len(_SCREAMING_SNAKE_CASE						):
                     raise ValueError(
                         f'''Text and labels have mismatched lengths {len(_SCREAMING_SNAKE_CASE						)} and {len(_SCREAMING_SNAKE_CASE						)}'''						)
              if ids is not None and len(_SCREAMING_SNAKE_CASE						) != len(_SCREAMING_SNAKE_CASE						):
                     raise ValueError(f'''Text and ids have mismatched lengths {len(_SCREAMING_SNAKE_CASE						)} and {len(_SCREAMING_SNAKE_CASE						)}'''						)
              if ids is None:
                     UpperCamelCase_      				=     [None] * len(_SCREAMING_SNAKE_CASE						)
              if labels is None:
                     UpperCamelCase_      				=     [None] * len(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     []
              UpperCamelCase_      				=     set()
              for text_or_text_and_label, label, guid in zip(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      (tuple, list)						) and label is None:
                            UpperCamelCase_				,       UpperCamelCase_      				=     text_or_text_and_label
                     else:
                            UpperCamelCase_      				=     text_or_text_and_label
                     added_labels.add(_SCREAMING_SNAKE_CASE						)
                     examples.append(InputExample(guid=_SCREAMING_SNAKE_CASE    ,      text_a=_SCREAMING_SNAKE_CASE    ,      text_b=_SCREAMING_SNAKE_CASE    ,      label=_SCREAMING_SNAKE_CASE						)						)

              # Update examples
              if overwrite_examples:
                     UpperCamelCase_      				=     examples
              else:
                     self.examples.extend(_SCREAMING_SNAKE_CASE						)

              # Update labels
              if overwrite_labels:
                     UpperCamelCase_      				=     list(_SCREAMING_SNAKE_CASE						)
              else:
                     UpperCamelCase_      				=     list(set(self.labels						).union(_SCREAMING_SNAKE_CASE						)						)

              return self.examples



       def lowercase       (						self:       int    ,      _SCREAMING_SNAKE_CASE:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       Tuple=None    ,      _SCREAMING_SNAKE_CASE:       List[Any]=False    ,      _SCREAMING_SNAKE_CASE:       List[Any]=0    ,      _SCREAMING_SNAKE_CASE:       Dict=True    ,      _SCREAMING_SNAKE_CASE:       List[str]=None    ,      )				->       Union[str, Any]:




              """simple docstring"""


              if max_length is None:
                     UpperCamelCase_      				=     tokenizer.max_len

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

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

                     UpperCamelCase_      				=     tokenizer.encode(
                         example.text_a    ,      add_special_tokens=_SCREAMING_SNAKE_CASE    ,      max_length=min(_SCREAMING_SNAKE_CASE    ,      tokenizer.max_len						)    ,      )
                     all_input_ids.append(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     max(len(_SCREAMING_SNAKE_CASE						) for input_ids in all_input_ids						)

              UpperCamelCase_      				=     []
              for ex_index, (input_ids, example) in enumerate(zip(_SCREAMING_SNAKE_CASE    ,      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_      				=     [1 if mask_padding_with_zero else 0] * len(_SCREAMING_SNAKE_CASE						)

                     # Zero-pad up to the sequence length.
                     UpperCamelCase_      				=     batch_length - len(_SCREAMING_SNAKE_CASE						)
                     if pad_on_left:
                            UpperCamelCase_      				=     ([pad_token] * padding_length) + input_ids
                            UpperCamelCase_      				=     ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                     else:
                            UpperCamelCase_      				=     input_ids + ([pad_token] * padding_length)
                            UpperCamelCase_      				=     attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

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

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

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

              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_      				=     tf.data.Dataset.from_generator(
                         _SCREAMING_SNAKE_CASE    ,      ({"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_      				=     torch.tensor([f.input_ids for f in features]    ,      dtype=torch.long						)
                     UpperCamelCase_      				=     torch.tensor([f.attention_mask for f in features]    ,      dtype=torch.long						)
                     if self.mode == "classification":
                            UpperCamelCase_      				=     torch.tensor([f.label for f in features]    ,      dtype=torch.long						)
                     elif self.mode == "regression":
                            UpperCamelCase_      				=     torch.tensor([f.label for f in features]    ,      dtype=torch.float						)

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








def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						list:
       UpperCamelCase_      				=     sorted(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[2]    ,						reverse=UpperCamelCase_     )

       # Create a list of size equal to the maximum deadline
       # and initialize it with -1
       UpperCamelCase_      				=     max(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[1]     )[1]
       UpperCamelCase_      				=     [-1] * max_deadline

       # Finding the maximum profit and the count of jobs
       UpperCamelCase_      				=     0
       UpperCamelCase_      				=     0
       for job in jobs:
              # Find a free time slot for this job
              # (Note that we start from the last possible slot)
              for i in range(job[1] - 1    ,						-1    ,						-1     ):
                     if time_slots[i] == -1:
                            UpperCamelCase_      				=     job[0]
                            count += 1
                            max_profit += job[2]
                            break
       return [count, max_profit]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

