




"""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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)



class   lowerCAmelCase_    (			__A					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			feature_size
										snake_case								=			sampling_rate
										snake_case								=			padding_value

										snake_case								=			kwargs.pop('padding_side'		,    'right'    )
										snake_case								=			kwargs.pop('return_attention_mask'		,    lowerCAmelCase    )

										super().__init__(**lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    ):






										"""simple docstring"""

										if isinstance(lowerCAmelCase		,    (list, tuple)    ) and isinstance(processed_features[0]		,    (dict, BatchFeature)    ):
																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()    )}"""    )

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

										if len(lowerCAmelCase    ) == 0:
																if return_attention_mask:
																						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

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

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

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

        # Convert padding_strategy in PaddingStrategy
										snake_case								=			self._get_padding_strategies(padding=lowerCAmelCase		,    max_length=lowerCAmelCase    )

										snake_case								=			processed_features[self.model_input_names[0]]

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

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

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

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

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

										return BatchFeature(lowerCAmelCase		,    tensor_type=lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = PaddingStrategy.DO_NOT_PAD		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    ):






										"""simple docstring"""

										snake_case								=			processed_features[self.model_input_names[0]]

										if padding_strategy == PaddingStrategy.LONGEST:
																snake_case								=			len(lowerCAmelCase    )

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

										snake_case								=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase    ) < max_length

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

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

										return processed_features

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    ):






										"""simple docstring"""

										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.'    )

										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):
																snake_case								=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										snake_case								=			len(lowerCAmelCase    ) > max_length

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

										return processed_features

				def 		snake_case (				self		,    lowerCAmelCase=False		,    lowerCAmelCase=None    ):






										"""simple docstring"""

										if padding is not False:
																if padding is True:
																						snake_case								=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																elif not isinstance(lowerCAmelCase		,    lowerCAmelCase    ):
																						snake_case								=			PaddingStrategy(lowerCAmelCase    )
																elif isinstance(lowerCAmelCase		,    lowerCAmelCase    ):
																						snake_case								=			padding
										else:
																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"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						return math.sqrt(_UpperCamelCase					) * math.sqrt(_UpperCamelCase					) == num





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						snake_case								=			0
						snake_case								=			n
						while left <= right:
												snake_case								=			(left + right) // 2
												if mid**2 == n:
																		return True
												elif mid**2 > n:
																		snake_case								=			mid - 1
												else:
																		snake_case								=			mid + 1
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()


