

"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


A_				:      Optional[Any]           =			logging.get_logger(__name__)





class  lowerCamelCase    (A__      ):

			def __init__(       self					:    List[str]     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    float     ,					**__UpperCAmelCase					:    int							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   feature_size
						SCREAMING_SNAKE_CASE__										=   sampling_rate
						SCREAMING_SNAKE_CASE__										=   padding_value

						SCREAMING_SNAKE_CASE__										=   kwargs.pop("""padding_side"""     ,					"""right"""							)
						SCREAMING_SNAKE_CASE__										=   kwargs.pop("""return_attention_mask"""     ,					__UpperCAmelCase							)

						super().__init__(**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    Union[
			        BatchFeature,
			        List[BatchFeature],
			        Dict[str, BatchFeature],
			        Dict[str, List[BatchFeature]],
			        List[Dict[str, BatchFeature]],
			    ]     ,					__UpperCAmelCase					:    Union[bool, str, PaddingStrategy] = True     ,					__UpperCAmelCase					:    Optional[int] = None     ,					__UpperCAmelCase					:    bool = False     ,					__UpperCAmelCase					:    Optional[int] = None     ,					__UpperCAmelCase					:    Optional[bool] = None     ,					__UpperCAmelCase					:    Optional[Union[str, TensorType]] = None     ,					) ->     BatchFeature:
						# If we have a list of dicts, let's convert it in a dict of lists
						# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
						if isinstance(__UpperCAmelCase     ,					(list, tuple)							) and isinstance(processed_features[0]     ,					(dict, BatchFeature)							):
									SCREAMING_SNAKE_CASE__										=   {
									    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
									}

						# The model's main input name, usually `input_values`, has be passed for padding
						if self.model_input_names[0] not in processed_features:
									raise ValueError(
									    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
									    F""" to this method that includes {self.model_input_names[0]}, but you provided"""
									    F""" {list(processed_features.keys()							)}"""							)

						SCREAMING_SNAKE_CASE__										=   processed_features[self.model_input_names[0]]
						SCREAMING_SNAKE_CASE__										=   (
						    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
						)

						if len(__UpperCAmelCase							) == 0:
									if return_attention_mask:
												SCREAMING_SNAKE_CASE__										=   []
									return processed_features

						# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
						# and rebuild them afterwards if no return_tensors is specified
						# Note that we lose the specific device the tensor may be on for PyTorch

						SCREAMING_SNAKE_CASE__										=   required_input[0]
						if isinstance(__UpperCAmelCase     ,					(list, tuple)							):
									# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
									SCREAMING_SNAKE_CASE__										=   0
									while len(required_input[index]							) == 0:
												index += 1
									if index < len(__UpperCAmelCase							):
												SCREAMING_SNAKE_CASE__										=   required_input[index][0]

						if return_tensors is None:
									if is_tf_tensor(__UpperCAmelCase							):
												SCREAMING_SNAKE_CASE__										=   """tf"""
									elif is_torch_tensor(__UpperCAmelCase							):
												SCREAMING_SNAKE_CASE__										=   """pt"""
									elif isinstance(__UpperCAmelCase     ,					(int, float, list, tuple, np.ndarray)							):
												SCREAMING_SNAKE_CASE__										=   """np"""
									else:
												raise ValueError(
												    F"""type of {first_element} unknown: {type(__UpperCAmelCase							)}. """
												    """Should be one of a python, numpy, pytorch or tensorflow object."""							)

						for key, value in processed_features.items():
									if isinstance(value[0]     ,					(int, float)							):
												SCREAMING_SNAKE_CASE__										=   to_numpy(__UpperCAmelCase							)
									else:
												SCREAMING_SNAKE_CASE__										=   [to_numpy(__UpperCAmelCase							) for v in value]

        # Convert padding_strategy in PaddingStrategy
						SCREAMING_SNAKE_CASE__										=   self._get_padding_strategies(padding=__UpperCAmelCase     ,					max_length=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   processed_features[self.model_input_names[0]]

						SCREAMING_SNAKE_CASE__										=   len(__UpperCAmelCase							)
						if not all(len(__UpperCAmelCase							) == batch_size for v in processed_features.values()							):
									raise ValueError("""Some items in the output dictionary have a different batch size than others."""							)

						SCREAMING_SNAKE_CASE__										=   []
						for i in range(__UpperCAmelCase							):
									SCREAMING_SNAKE_CASE__										=   {k: v[i] for k, v in processed_features.items()}
									# truncation
									SCREAMING_SNAKE_CASE__										=   self._truncate(
									    __UpperCAmelCase     ,					max_length=__UpperCAmelCase     ,					pad_to_multiple_of=__UpperCAmelCase     ,					truncation=__UpperCAmelCase     ,					)
									truncated_inputs.append(__UpperCAmelCase							)

						if padding_strategy == PaddingStrategy.LONGEST:
									# make sure that `max_length` cannot be longer than the longest truncated length
									SCREAMING_SNAKE_CASE__										=   max(len(input_slice[self.model_input_names[0]]							) for input_slice in truncated_inputs							)
									SCREAMING_SNAKE_CASE__										=   PaddingStrategy.MAX_LENGTH

						SCREAMING_SNAKE_CASE__										=   {}
						for i in range(__UpperCAmelCase							):
									# padding
									SCREAMING_SNAKE_CASE__										=   self._pad(
									    truncated_inputs[i]     ,					max_length=__UpperCAmelCase     ,					padding_strategy=__UpperCAmelCase     ,					pad_to_multiple_of=__UpperCAmelCase     ,					return_attention_mask=__UpperCAmelCase     ,					)

									for key, value in outputs.items():
												if key not in batch_outputs:
															SCREAMING_SNAKE_CASE__										=   []
												if value.dtype is np.dtype(np.floataa							):
															SCREAMING_SNAKE_CASE__										=   value.astype(np.floataa							)
												batch_outputs[key].append(__UpperCAmelCase							)

						return BatchFeature(__UpperCAmelCase     ,					tensor_type=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Union[Dict[str, np.ndarray], BatchFeature]     ,					__UpperCAmelCase					:    Optional[int] = None     ,					__UpperCAmelCase					:    PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,					__UpperCAmelCase					:    Optional[int] = None     ,					__UpperCAmelCase					:    Optional[bool] = None     ,					) ->     dict:
						SCREAMING_SNAKE_CASE__										=   processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
									SCREAMING_SNAKE_CASE__										=   len(__UpperCAmelCase							)

						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									SCREAMING_SNAKE_CASE__										=   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						SCREAMING_SNAKE_CASE__										=   padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCAmelCase							) < max_length

						if return_attention_mask and "attention_mask" not in processed_features:
									SCREAMING_SNAKE_CASE__										=   np.ones(len(__UpperCAmelCase							)     ,					dtype=np.intaa							)

						if needs_to_be_padded:
									SCREAMING_SNAKE_CASE__										=   max_length - len(__UpperCAmelCase							)
									if self.padding_side == "right":
												if return_attention_mask:
															SCREAMING_SNAKE_CASE__										=   np.pad(
															    processed_features["""attention_mask"""]     ,					(0, difference)							)
												SCREAMING_SNAKE_CASE__										=   ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
												SCREAMING_SNAKE_CASE__										=   np.pad(
												    __UpperCAmelCase     ,					__UpperCAmelCase     ,					"""constant"""     ,					constant_values=self.padding_value							)
									elif self.padding_side == "left":
												if return_attention_mask:
															SCREAMING_SNAKE_CASE__										=   np.pad(
															    processed_features["""attention_mask"""]     ,					(difference, 0)							)
												SCREAMING_SNAKE_CASE__										=   ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
												SCREAMING_SNAKE_CASE__										=   np.pad(
												    __UpperCAmelCase     ,					__UpperCAmelCase     ,					"""constant"""     ,					constant_values=self.padding_value							)
									else:
												raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

						return processed_features

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]     ,					__UpperCAmelCase					:    Union[Dict[str, np.ndarray], BatchFeature]     ,					__UpperCAmelCase					:    Optional[int] = None     ,					__UpperCAmelCase					:    Optional[int] = None     ,					__UpperCAmelCase					:    Optional[bool] = None     ,					) ->     Any:
						if not truncation:
									return processed_features
						elif truncation and max_length is None:
									raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""							)

						SCREAMING_SNAKE_CASE__										=   processed_features[self.model_input_names[0]]

						# find `max_length` that fits `pad_to_multiple_of`
						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									SCREAMING_SNAKE_CASE__										=   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						SCREAMING_SNAKE_CASE__										=   len(__UpperCAmelCase							) > max_length

						if needs_to_be_truncated:
									SCREAMING_SNAKE_CASE__										=   processed_features[self.model_input_names[0]][:max_length]
									if "attention_mask" in processed_features:
												SCREAMING_SNAKE_CASE__										=   processed_features["""attention_mask"""][:max_length]

						return processed_features





			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    Optional[Any]=False     ,					__UpperCAmelCase					:    Any=None							) ->     Dict:

						# Get padding strategy
						if padding is not False:
									if padding is True:
												SCREAMING_SNAKE_CASE__										=   PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
									elif not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase							):
												SCREAMING_SNAKE_CASE__										=   PaddingStrategy(__UpperCAmelCase							)
									elif isinstance(__UpperCAmelCase     ,					__UpperCAmelCase							):
												SCREAMING_SNAKE_CASE__										=   padding
						else:
									SCREAMING_SNAKE_CASE__										=   PaddingStrategy.DO_NOT_PAD

						# Set max length if needed
						if max_length is None:
									if padding_strategy == PaddingStrategy.MAX_LENGTH:
												raise ValueError(
												    F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""							)

        # Test if we have a padding value
						if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
									raise ValueError(
									    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
									    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""							)

						return padding_strategy





"""simple docstring"""




def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   2
			while i * i <= n:
						SCREAMING_SNAKE_CASE__										=   0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   1

			while True:
						i += 1
						t_num += i

						if count_divisors(snake_case__     ) > 5_00:
									break

			return t_num


if __name__ == "__main__":
						print(solution())


