





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    snake_case_				:
  A_										=    42
  A_										=    42
  A_										=    None
  A_										=    None






  def       UpperCAmelCase__						(							self :     Tuple			)->Tuple:






   '''simple docstring'''

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






@dataclass(frozen=__lowercase						)
class    snake_case_				:
  A_										=    42
  A_										=    None
  A_										=    None
  A_										=    None






  def       UpperCAmelCase__						(							self :     List[Any]			)->List[Any]:






   '''simple docstring'''

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






class    snake_case_				:






  def       UpperCAmelCase__						(							self :     List[Any]   ,  _snake_case :     int			)->List[str]:






   '''simple docstring'''

   raise NotImplementedError()






  def       UpperCAmelCase__						(							self :     List[str]   ,  _snake_case :     Optional[int]			)->List[str]:






   '''simple docstring'''

   raise NotImplementedError()






  def       UpperCAmelCase__						(							self :     Optional[Any]   ,  _snake_case :     List[Any]			)->Union[str, Any]:






   '''simple docstring'''

   raise NotImplementedError()






  def       UpperCAmelCase__						(							self :     Any   ,  _snake_case :     Any			)->Union[str, Any]:






   '''simple docstring'''

   raise NotImplementedError()






  def       UpperCAmelCase__						(							self :     List[str]			)->Tuple:






   '''simple docstring'''

   raise NotImplementedError()






  def       UpperCAmelCase__						(							self :     Dict   ,  _snake_case :     str			)->Optional[int]:






   '''simple docstring'''

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






  @classmethod
  def       UpperCAmelCase__						(							cls :     str   ,  _snake_case :     Dict   ,  _snake_case :     Union[str, Any]=None			)->Tuple:






   '''simple docstring'''

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






class    snake_case_				(		__lowercase						):






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






   '''simple docstring'''

   __lowerCAmelCase     :       Optional[Any]						=   [] if labels is None else labels
   __lowerCAmelCase     :       Union[str, Any]						=   [] if examples is None else examples
   __lowerCAmelCase     :       str						=   mode
   __lowerCAmelCase     :       Dict						=   verbose






  def __len__(							self :     Dict			)->Tuple:






   '''simple docstring'''

   return len(self.examples			)






  def __getitem__(							self :     Union[str, Any]   ,  _snake_case :     Dict			)->int:






   '''simple docstring'''

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






  @classmethod
  def       UpperCAmelCase__						(							cls :     List[str]   ,  _snake_case :     str   ,  _snake_case :     List[Any]=""   ,  _snake_case :     Union[str, Any]=0   ,  _snake_case :     List[Any]=1   ,  _snake_case :     List[str]=None   ,  _snake_case :     Any=False   ,  **_snake_case :     List[str]			)->Any:






   '''simple docstring'''

   __lowerCAmelCase     :       int						=   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       UpperCAmelCase__						(							cls :     Dict   ,  _snake_case :     Optional[int]   ,  _snake_case :     int=None   ,  **_snake_case :     Tuple			)->Any:






   '''simple docstring'''

   __lowerCAmelCase     :       List[str]						=   cls(**_UpperCamelCase			)
   processor.add_examples(_UpperCamelCase   ,  labels=_UpperCamelCase			)
   return processor






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






   '''simple docstring'''

   __lowerCAmelCase     :       int						=   self._read_tsv(_UpperCamelCase			)
   if skip_first_row:
    __lowerCAmelCase     :       str						=   lines[1:]
   __lowerCAmelCase     :       Optional[Any]						=   []
   __lowerCAmelCase     :       List[str]						=   []
   __lowerCAmelCase     :       Union[str, Any]						=   []
   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[Any]						=   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       UpperCAmelCase__						(							self :     List[Any]   ,  _snake_case :     Optional[Any]   ,  _snake_case :     Dict=None   ,  _snake_case :     Optional[int]=None   ,  _snake_case :     str=False   ,  _snake_case :     Optional[int]=False			)->List[Any]:






   '''simple docstring'''

   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     :       Tuple						=   [None] * len(_UpperCamelCase			)
   if labels is None:
    __lowerCAmelCase     :       Dict						=   [None] * len(_UpperCamelCase			)
   __lowerCAmelCase     :       str						=   []
   __lowerCAmelCase     :       Union[str, Any]						=   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     :       Optional[Any]						=   text_or_text_and_label
    else:
     __lowerCAmelCase     :       Dict						=   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     :       Dict						=   examples
   else:
    self.examples.extend(_UpperCamelCase			)

   # Update labels
   if overwrite_labels:
    __lowerCAmelCase     :       Union[str, Any]						=   list(_UpperCamelCase			)
   else:
    __lowerCAmelCase     :       List[str]						=   list(set(self.labels			).union(_UpperCamelCase			)			)

   return self.examples






  def       UpperCAmelCase__						(							self :     Any   ,  _snake_case :     Dict   ,  _snake_case :     Union[str, Any]=None   ,  _snake_case :     Optional[Any]=False   ,  _snake_case :     Dict=0   ,  _snake_case :     Optional[int]=True   ,  _snake_case :     int=None   ,  )->Union[str, Any]:






   '''simple docstring'''

   if max_length is None:
    __lowerCAmelCase     :       Union[str, Any]						=   tokenizer.max_len

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

   __lowerCAmelCase     :       Union[str, Any]						=   []
   for ex_index, example in enumerate(self.examples			):
    if ex_index % 10000 == 0:
     logger.info(F'''Tokenizing example {ex_index}'''			)

    __lowerCAmelCase     :       Optional[int]						=   tokenizer.encode(
        example.text_a   ,  add_special_tokens=_UpperCamelCase   ,  max_length=min(_UpperCamelCase   ,  tokenizer.max_len			)   ,  )
    all_input_ids.append(_UpperCamelCase			)

   __lowerCAmelCase     :       Dict						=   max(len(_UpperCamelCase			) for input_ids in all_input_ids			)

   __lowerCAmelCase     :       Tuple						=   []
   for ex_index, (input_ids, example) in enumerate(zip(_UpperCamelCase   ,  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.
    __lowerCAmelCase     :       Union[str, Any]						=   [1 if mask_padding_with_zero else 0] * len(_UpperCamelCase			)

    # Zero-pad up to the sequence length.
    __lowerCAmelCase     :       Dict						=   batch_length - len(_UpperCamelCase			)
    if pad_on_left:
     __lowerCAmelCase     :       int						=   ([pad_token] * padding_length) + input_ids
     __lowerCAmelCase     :       Union[str, Any]						=   ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
    else:
     __lowerCAmelCase     :       List[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     :       str						=   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     :       Any						=   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     :       Optional[int]						=   torch.tensor([f.label for f in features]   ,  dtype=torch.float			)

    __lowerCAmelCase     :       Optional[Any]						=   TensorDataset(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase			)
    return dataset
   else:
    raise ValueError("""return_tensors should be one of \'tf\' or \'pt\'"""			)







import numpy as np
import requests







def 		_UpperCamelCase      (			)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  requests.get(
						    'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
						    'master/Week1/ADRvsRating.csv'						)
						lowerCAmelCase__			  =  response.text.splitlines()
						lowerCAmelCase__			  =  []
						for item in lines:
												lowerCAmelCase__			  =  item.split(','						)
												data.append(UpperCamelCase_						)
						data.pop(0						)  # This is for removing the labels from the list
						lowerCAmelCase__			  =  np.matrix(UpperCamelCase_						)
						return dataset







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len_data

						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta - (alpha / n) * sum_grad
						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str			,				UpperCamelCase_				:    List[Any]						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.sum(np.square(UpperCamelCase_						)						)
						lowerCAmelCase__			  =  sum_elem / (2 * len_data)
						return error







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    List[Any]						)     ->      str:

						"""simple docstring"""



						lowerCAmelCase__			  =  10_0000
						lowerCAmelCase__			  =  0.000_1550

						lowerCAmelCase__			  =  data_x.shape[1]
						lowerCAmelCase__			  =  data_x.shape[0] - 1

						lowerCAmelCase__			  =  np.zeros((1, no_features)						)

						for i in range(0			,				UpperCamelCase_						):
												lowerCAmelCase__			  =  run_steep_gradient_descent(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												lowerCAmelCase__			  =  sum_of_square_error(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												print(F"At Iteration {i + 1} - Error is {error:.5f}"						)

						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Any						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  sum(abs(y - predicted_y[i]						) for i, y in enumerate(UpperCamelCase_						)						)
						return total / len(UpperCamelCase_						)







def 		_UpperCamelCase      (			)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  collect_dataset()

						lowerCAmelCase__			  =  data.shape[0]
						lowerCAmelCase__			  =  np.c_[np.ones(UpperCamelCase_						), data[:, :-1]].astype(UpperCamelCase_						)
						lowerCAmelCase__			  =  data[:, -1].astype(UpperCamelCase_						)

						lowerCAmelCase__			  =  run_linear_regression(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta.shape[1]
						print('Resultant Feature vector : '						)
						for i in range(0			,				UpperCamelCase_						):
												print(F"{theta[0, i]:.5f}"						)


if __name__ == "__main__":
					main()





