code: 
  
  
  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__				:    Dict         =    logging.get_logger(__name__)
  
  
  @dataclass
  class 		_SCREAMING_SNAKE_CASE    :
       __SCREAMING_SNAKE_CASE    				=						42
       __SCREAMING_SNAKE_CASE    				=						42
       __SCREAMING_SNAKE_CASE    				=						None
       __SCREAMING_SNAKE_CASE    				=						None
  
  
       def 					__snake_case(     self						):
         return json.dumps(dataclasses.asdict(self						)       ,				indent=2						) + "\n"
  
  
  @dataclass(frozen=A    )
  class 		_SCREAMING_SNAKE_CASE    :
       __SCREAMING_SNAKE_CASE    				=						42
       __SCREAMING_SNAKE_CASE    				=						None
       __SCREAMING_SNAKE_CASE    				=						None
       __SCREAMING_SNAKE_CASE    				=						None
  
  
       def 					__snake_case(     self						):
         return json.dumps(dataclasses.asdict(self						)						) + "\n"
  
  
  class 		_SCREAMING_SNAKE_CASE    :
  
  
       def 					__snake_case(     self       ,				A_						):
         raise NotImplementedError()
  
  
       def 					__snake_case(     self       ,				A_						):
         raise NotImplementedError()
  
  
       def 					__snake_case(     self       ,				A_						):
         raise NotImplementedError()
  
  
       def 					__snake_case(     self       ,				A_						):
         raise NotImplementedError()
  
  
       def 					__snake_case(     self						):
         raise NotImplementedError()
  
  
       def 					__snake_case(     self       ,				A_						):
         if len(self.get_labels()						) > 1:
           _UpperCAmelCase		:       int       =    self.get_labels()[int(example.label						)]
         return example
  
  
  
  
       @classmethod
       def 					__snake_case(     cls       ,				A_       ,				A_=None						):
         with open(A_       ,				"""r"""       ,				encoding="""utf-8-sig"""						) as f:
           return list(csv.reader(A_       ,				delimiter="""\t"""       ,				quotechar=A_						)						)
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def __init__(     self       ,				A_=None       ,				A_=None       ,				A_="classification"       ,				A_=False						):
         _UpperCAmelCase		:       Dict       =    [] if labels is None else labels
         _UpperCAmelCase		:       Optional[Any]       =    [] if examples is None else examples
         _UpperCAmelCase		:       Any       =    mode
         _UpperCAmelCase		:       str       =    verbose
  
  
       def __len__(     self						):
         return len(self.examples						)
  
  
       def __getitem__(     self       ,				A_						):
         if isinstance(A_       ,				A_						):
           return SingleSentenceClassificationProcessor(labels=self.labels       ,				examples=self.examples[idx]						)
         return self.examples[idx]
  
  
       @classmethod
       def 					__snake_case(     cls       ,				A_       ,				A_=""       ,				A_=0       ,				A_=1       ,				A_=None       ,				A_=False       ,				**A_						):
         _UpperCAmelCase		:       List[str]       =    cls(**A_						)
         processor.add_examples_from_csv(
             A_       ,				split_name=A_       ,				column_label=A_       ,				column_text=A_       ,				column_id=A_       ,				skip_first_row=A_       ,				overwrite_labels=A_       ,				overwrite_examples=A_       ,				)
         return processor
  
  
       @classmethod
       def 					__snake_case(     cls       ,				A_       ,				A_=None       ,				**A_						):
         _UpperCAmelCase		:       Tuple       =    cls(**A_						)
         processor.add_examples(A_       ,				labels=A_						)
         return processor
  
  
       def 					__snake_case(     self       ,				A_       ,				A_=""       ,				A_=0       ,				A_=1       ,				A_=None       ,				A_=False       ,				A_=False       ,				A_=False       ,				):
         _UpperCAmelCase		:       Any       =    self._read_tsv(A_						)
         if skip_first_row:
           _UpperCAmelCase		:       Any       =    lines[1:]
         _UpperCAmelCase		:       Union[str, Any]       =    []
         _UpperCAmelCase		:       List[str]       =    []
         _UpperCAmelCase		:       List[Any]       =    []
         for i, line in enumerate(A_						):
           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(A_						)
             ids.append(A_						)
  
         return self.add_examples(
             A_       ,				A_       ,				A_       ,				overwrite_labels=A_       ,				overwrite_examples=A_						)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_=None       ,				A_=None       ,				A_=False       ,				A_=False						):
         if labels is not None and len(A_						) != len(A_						):
           raise ValueError(
               F'''Text and labels have mismatched lengths {len(A_						)} and {len(A_						)}'''						)
         if ids is not None and len(A_						) != len(A_						):
           raise ValueError(F'''Text and ids have mismatched lengths {len(A_						)} and {len(A_						)}'''						)
         if ids is None:
           _UpperCAmelCase		:       List[str]       =    [None] * len(A_						)
         if labels is None:
           _UpperCAmelCase		:       List[str]       =    [None] * len(A_						)
         _UpperCAmelCase		:       Optional[Any]       =    []
         _UpperCAmelCase		:       List[str]       =    set()
         for text_or_text_and_label, label, guid in zip(A_       ,				A_       ,				A_						):
           if isinstance(A_       ,				(tuple, list)						) and label is None:
             _UpperCAmelCase,_UpperCAmelCase		:       Optional[Any]       =    text_or_text_and_label
           else:
             _UpperCAmelCase		:       Any       =    text_or_text_and_label
           added_labels.add(A_						)
           examples.append(InputExample(guid=A_       ,				text_a=A_       ,				text_b=A_       ,				label=A_						)						)
  
         # Update examples
         if overwrite_examples:
           _UpperCAmelCase		:       Union[str, Any]       =    examples
         else:
           self.examples.extend(A_						)
  
         # Update labels
         if overwrite_labels:
           _UpperCAmelCase		:       str       =    list(A_						)
         else:
           _UpperCAmelCase		:       Union[str, Any]       =    list(set(self.labels						).union(A_						)						)
  
         return self.examples
  
  
  
  
       def 					__snake_case(     self       ,				A_       ,				A_=None       ,				A_=False       ,				A_=0       ,				A_=True       ,				A_=None       ,				):
         if max_length is None:
           _UpperCAmelCase		:       Any       =    tokenizer.max_len
  
         _UpperCAmelCase		:       str       =    {label: i for i, label in enumerate(self.labels						)}
  
         _UpperCAmelCase		:       int       =    []
         for ex_index, example in enumerate(self.examples						):
           if ex_index % 1_00_00 == 0:
             logger.info(F'''Tokenizing example {ex_index}'''						)
  
           _UpperCAmelCase		:       List[Any]       =    tokenizer.encode(
               example.text_a       ,				add_special_tokens=A_       ,				max_length=min(A_       ,				tokenizer.max_len						)       ,				)
           all_input_ids.append(A_						)
  
         _UpperCAmelCase		:       int       =    max(len(A_						) for input_ids in all_input_ids						)
  
         _UpperCAmelCase		:       int       =    []
         for ex_index, (input_ids, example) in enumerate(zip(A_       ,				self.examples						)						):
           if ex_index % 1_00_00 == 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[Any]       =    [1 if mask_padding_with_zero else 0] * len(A_						)
  
           # Zero-pad up to the sequence length.
           _UpperCAmelCase		:       List[Any]       =    batch_length - len(A_						)
           if pad_on_left:
             _UpperCAmelCase		:       Optional[int]       =    ([pad_token] * padding_length) + input_ids
             _UpperCAmelCase		:       Dict       =    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
           else:
             _UpperCAmelCase		:       List[str]       =    input_ids + ([pad_token] * padding_length)
             _UpperCAmelCase		:       List[Any]       =    attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
           if len(A_						) != batch_length:
             raise ValueError(F'''Error with input length {len(A_						)} vs {batch_length}'''						)
           if len(A_						) != batch_length:
             raise ValueError(F'''Error with input length {len(A_						)} vs {batch_length}'''						)
  
           if self.mode == "classification":
             _UpperCAmelCase		:       Tuple       =    label_map[example.label]
           elif self.mode == "regression":
             _UpperCAmelCase		:       Union[str, 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(A_						) for x in input_ids]						)}'''						)
             logger.info(F'''attention_mask: {" ".join([str(A_						) for x in attention_mask]						)}'''						)
             logger.info(F'''label: {example.label} (id = {label})'''						)
  
           features.append(InputFeatures(input_ids=A_       ,				attention_mask=A_       ,				label=A_						)						)
  
         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		:       Optional[Any]       =    tf.data.Dataset.from_generator(
               A_       ,				({"""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		:       str       =    torch.tensor([f.attention_mask for f in features]       ,				dtype=torch.long						)
           if self.mode == "classification":
             _UpperCAmelCase		:       Optional[int]       =    torch.tensor([f.label for f in features]       ,				dtype=torch.long						)
           elif self.mode == "regression":
             _UpperCAmelCase		:       Tuple       =    torch.tensor([f.label for f in features]       ,				dtype=torch.float						)
  
           _UpperCAmelCase		:       Any       =    TensorDataset(A_       ,				A_       ,				A_						)
           return dataset
         else:
           raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""						)
  
  
  
  
  
  
style_context: 
  
  
  from itertools import count
  from math import asin, pi, sqrt
  
  
  
  
  def       a__						(		snake_case__				:       float    ):
    return (
        (1 - 2 * point) * sqrt(point - point**2    ) + 2 * point + asin(sqrt(1 - point    )    )
    ) / 4
  
  
  
  
  def       a__						(		snake_case__				:       int    ):
    _UpperCAmelCase		:       int       =    (circles_number + 1 - sqrt(2 * circles_number    )) / (
        2 * (circles_number**2 + 1)
    )
    _UpperCAmelCase		:       Union[str, Any]       =    circles_number * intersection_y
  
    _UpperCAmelCase		:       Tuple       =    intersection_x * intersection_y / 2
    _UpperCAmelCase		:       str       =    circle_bottom_arc_integral(
        1 / 2    ) - circle_bottom_arc_integral(snake_case__    )
  
    return triangle_area + concave_region_area
  
  
  
  
  def       a__						(		snake_case__				:       float = 1 / 1000    ):
    _UpperCAmelCase		:       Dict       =    (1 - pi / 4) / 4
  
    for n in count(1    ):
      if concave_triangle_area(snake_case__    ) / l_section_area < fraction:
        return n
  
    return -1
  
  
  if __name__ == "__main__":
   print(F'{solution() = }')
  
  
  
  
  
  