

"""simple docstring"""




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


__A  :					Tuple												=	logging.get_logger(__name__)





@dataclass
class     __UpperCamelCase				:
		SCREAMING_SNAKE_CASE              =					42
		SCREAMING_SNAKE_CASE              =					42
		SCREAMING_SNAKE_CASE              =					None
		SCREAMING_SNAKE_CASE              =					None


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





@dataclass(frozen=_A		)
class     __UpperCamelCase				:
		SCREAMING_SNAKE_CASE              =					42
		SCREAMING_SNAKE_CASE              =					None
		SCREAMING_SNAKE_CASE              =					None
		SCREAMING_SNAKE_CASE              =					None


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							return json.dumps(dataclasses.asdict(self)) + "\n"





class     __UpperCamelCase				:


		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							raise NotImplementedError()


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
							raise NotImplementedError()


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]):
							raise NotImplementedError()


		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]):
							raise NotImplementedError()


		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
							raise NotImplementedError()


		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	Tuple):







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


		@classmethod
		def       SCREAMING_SNAKE_CASE__     (cls				:	Any  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None):







							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				(     _A		):


		def __init__(self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	List[str]=None  ,  __SCREAMING_SNAKE_CASE				:	Dict="classification"  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=False):
							A    =      [] if labels is None else labels
							A    =      [] if examples is None else examples
							A    =      mode
							A    =      verbose


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


		def __getitem__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]):







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


		@classmethod
		def       SCREAMING_SNAKE_CASE__     (cls				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=""  ,  __SCREAMING_SNAKE_CASE				:	str=0  ,  __SCREAMING_SNAKE_CASE				:	Dict=1  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=False  ,  **__SCREAMING_SNAKE_CASE				:	Dict):
							A    =      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       SCREAMING_SNAKE_CASE__     (cls				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	List[str]=None  ,  **__SCREAMING_SNAKE_CASE				:	Tuple):
							A    =      cls(**__SCREAMING_SNAKE_CASE)
							processor.add_examples(__SCREAMING_SNAKE_CASE  ,  labels=__SCREAMING_SNAKE_CASE)
							return processor


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Any=""  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1  ,  __SCREAMING_SNAKE_CASE				:	List[str]=None  ,  __SCREAMING_SNAKE_CASE				:	List[str]=False  ,  __SCREAMING_SNAKE_CASE				:	int=False  ,  __SCREAMING_SNAKE_CASE				:	Any=False  ,  ):
							A    =      self._read_tsv(__SCREAMING_SNAKE_CASE)
							if skip_first_row:
												A    =      lines[1:]
							A    =      []
							A    =      []
							A    =      []
							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:
																	A    =      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       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Tuple=None  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None  ,  __SCREAMING_SNAKE_CASE				:	int=False  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=False):







							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:
												A    =      [None] * len(__SCREAMING_SNAKE_CASE)
							if labels is None:
												A    =      [None] * len(__SCREAMING_SNAKE_CASE)
							A    =      []
							A    =      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:
																	A    ,					A    =      text_or_text_and_label
												else:
																	A    =      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:
												A    =      examples
							else:
												self.examples.extend(__SCREAMING_SNAKE_CASE)

							# Update labels
							if overwrite_labels:
												A    =      list(__SCREAMING_SNAKE_CASE)
							else:
												A    =      list(set(self.labels).union(__SCREAMING_SNAKE_CASE))

							return self.examples


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	str=None  ,  __SCREAMING_SNAKE_CASE				:	str=False  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=True  ,  __SCREAMING_SNAKE_CASE				:	List[str]=None  ,  ):







							if max_length is None:
												A    =      tokenizer.max_len

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

							A    =      []
							for ex_index, example in enumerate(self.examples):
												if ex_index % 1_0_0_0_0 == 0:
																	logger.info(F"""Tokenizing example {ex_index}""")

												A    =      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)

							A    =      max(len(__SCREAMING_SNAKE_CASE) for input_ids in all_input_ids)

							A    =      []
							for ex_index, (input_ids, example) in enumerate(zip(__SCREAMING_SNAKE_CASE  ,  self.examples)):
												if ex_index % 1_0_0_0_0 == 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.
												A    =      [1 if mask_padding_with_zero else 0] * len(__SCREAMING_SNAKE_CASE)

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

												A    =      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

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

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








"""simple docstring"""




def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if not isinstance(lowercase__    ,							lowercase__   ):
										raise ValueError("double_factorial() only accepts integral values"   )
					if n < 0:
										raise ValueError("double_factorial() not defined for negative values"   )
					return 1 if n <= 1 else n * double_factorial(n - 2   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





