
'''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


UpperCAmelCase__				:	List[Any]					=						logging.get_logger(__name__)






@dataclass
class 				A       :
	snake_case__ :str
	snake_case__ :str
	snake_case__ :Optional[str]				       =       None
	snake_case__ :Optional[str]				       =       None





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

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






@dataclass(frozen=SCREAMING_SNAKE_CASE__      )
class 				A       :
	snake_case__ :List[int]
	snake_case__ :Optional[List[int]]				       =       None
	snake_case__ :Optional[List[int]]				       =       None
	snake_case__ :Optional[Union[int, float]]				       =       None





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

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






class 				A       :





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    Optional[Any]							):


				"""simple docstring"""

				raise NotImplementedError()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				raise NotImplementedError()





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


				"""simple docstring"""

				raise NotImplementedError()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    Tuple							):


				"""simple docstring"""

				raise NotImplementedError()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				raise NotImplementedError()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

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







	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    int ,							__magic_name__     :    Dict ,							__magic_name__     :    str=None							):


				"""simple docstring"""

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






class 				A       (		SCREAMING_SNAKE_CASE__      ):





	def __init__(   self     :    Union[str, Any] ,							__magic_name__     :    Tuple=None ,							__magic_name__     :    List[Any]=None ,							__magic_name__     :    Dict="classification" ,							__magic_name__     :    Union[str, Any]=False							):


				"""simple docstring"""

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





	def __len__(   self     :    Dict							):


				"""simple docstring"""

				return len(self.examples							)





	def __getitem__(   self     :    Any ,							__magic_name__     :    Dict							):


				"""simple docstring"""

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





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    Optional[int] ,							__magic_name__     :    Optional[int] ,							__magic_name__     :    Optional[Any]="" ,							__magic_name__     :    Optional[int]=0 ,							__magic_name__     :    List[Any]=1 ,							__magic_name__     :    List[str]=None ,							__magic_name__     :    str=False ,							**__magic_name__     :    List[str]							):


				"""simple docstring"""

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





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    List[Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]=None ,							**__magic_name__     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							cls(**__magic_name__							)
				processor.add_examples(__magic_name__ ,							labels=__magic_name__							)
				return processor





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    str="" ,							__magic_name__     :    Union[str, Any]=0 ,							__magic_name__     :    Optional[int]=1 ,							__magic_name__     :    Dict=None ,							__magic_name__     :    Optional[Any]=False ,							__magic_name__     :    Union[str, Any]=False ,							__magic_name__     :    Optional[int]=False ,							):


				"""simple docstring"""

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

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





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


				"""simple docstring"""

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

				# Update examples
				if overwrite_examples:
							lowerCAmelCase__									=							examples
				else:
							self.examples.extend(__magic_name__							)

				# Update labels
				if overwrite_labels:
							lowerCAmelCase__									=							list(__magic_name__							)
				else:
							lowerCAmelCase__									=							list(set(self.labels							).union(__magic_name__							)							)

				return self.examples







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


				"""simple docstring"""

				if max_length is None:
							lowerCAmelCase__									=							tokenizer.max_len

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

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

							lowerCAmelCase__									=							tokenizer.encode(
							    example.text_a ,							add_special_tokens=__magic_name__ ,							max_length=min(__magic_name__ ,							tokenizer.max_len							) ,							)
							all_input_ids.append(__magic_name__							)

				lowerCAmelCase__									=							max(len(__magic_name__							) for input_ids in all_input_ids							)

				lowerCAmelCase__									=							[]
				for ex_index, (input_ids, example) in enumerate(zip(__magic_name__ ,							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__									=							[1 if mask_padding_with_zero else 0] * len(__magic_name__							)

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

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

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

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

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

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







'''simple docstring'''

import math







def 					A       ( UpperCamelCase_   :       int )   ->    list:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive integers have prime factors" )
			lowerCAmelCase__									=							[]
			while n % 2 == 0:
						pf.append(2 )
						lowerCAmelCase__									=							int(n / 2 )
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						while n % i == 0:
									pf.append(UpperCamelCase_ )
									lowerCAmelCase__									=							int(n / i )
			if n > 2:
						pf.append(UpperCamelCase_ )
			return pf







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			div *= temp
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						div *= temp
			if n > 1:
						div *= 2
			return div







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			if temp > 1:
						s *= (2**temp - 1) / (2 - 1)
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						if temp > 1:
									s *= (i**temp - 1) / (i - 1)
			return int(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			lowerCAmelCase__									=							n
			for x in set(prime_factors(UpperCamelCase_ ) ):
						s *= (x - 1) / x
			return int(UpperCamelCase_ )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





