

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


lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				A_							):


       """simple docstring"""





       def __init__( self					,						_A					,						_A					,						_A					,						**_A							):
            __lowerCAmelCase					=							feature_size
            __lowerCAmelCase					=							sampling_rate
            __lowerCAmelCase					=							padding_value

            __lowerCAmelCase					=							kwargs.pop('padding_side'					,						'right'							)
            __lowerCAmelCase					=							kwargs.pop('return_attention_mask'					,						_A							)

            super().__init__(**_A							)




       def        A__  ( self					,						_A					,						_A = True					,						_A = None					,						_A = False					,						_A = None					,						_A = None					,						_A = None					,						):
            # 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(_A					,						(list, tuple)							) and isinstance(processed_features[0]					,						(dict, BatchFeature)							):
                 __lowerCAmelCase					=							{
                     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()							)}"""							)

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

            if len(_A							) == 0:
                 if return_attention_mask:
                      __lowerCAmelCase					=							[]
                 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

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

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

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

        # Convert padding_strategy in PaddingStrategy
            __lowerCAmelCase					=							self._get_padding_strategies(padding=_A					,						max_length=_A							)

            __lowerCAmelCase					=							processed_features[self.model_input_names[0]]

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

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

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

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

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

            return BatchFeature(_A					,						tensor_type=_A							)




       def        A__  ( self					,						_A					,						_A = None					,						_A = PaddingStrategy.DO_NOT_PAD					,						_A = None					,						_A = None					,						):
            __lowerCAmelCase					=							processed_features[self.model_input_names[0]]

            if padding_strategy == PaddingStrategy.LONGEST:
                 __lowerCAmelCase					=							len(_A							)

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

            __lowerCAmelCase					=							padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_A							) < max_length

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

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

            return processed_features




       def        A__  ( self					,						_A					,						_A = None					,						_A = None					,						_A = None					,						):
            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.'							)

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

            __lowerCAmelCase					=							len(_A							) > max_length

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

            return processed_features




       def        A__  ( self					,						_A=False					,						_A=None							):

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









import random


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							num - 1
					__lowerCAmelCase					=							0

					while s % 2 == 0:
										__lowerCAmelCase					=							s // 2
										t += 1

					for _ in range(5      ):
										__lowerCAmelCase					=							random.randrange(2		,					num - 1      )
										__lowerCAmelCase					=							pow(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )
										if v != 1:
															__lowerCAmelCase					=							0
															while v != (num - 1):
																				if i == t - 1:
																									return False
																				else:
																									__lowerCAmelCase					=							i + 1
																									__lowerCAmelCase					=							(v**2) % num
					return True


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if num < 2:
										return False

					__lowerCAmelCase					=							[
					    2,
					    3,
					    5,
					    7,
					    11,
					    13,
					    17,
					    19,
					    23,
					    29,
					    31,
					    37,
					    41,
					    43,
					    47,
					    53,
					    59,
					    61,
					    67,
					    71,
					    73,
					    79,
					    83,
					    89,
					    97,
					    101,
					    103,
					    107,
					    109,
					    113,
					    127,
					    131,
					    137,
					    139,
					    149,
					    151,
					    157,
					    163,
					    167,
					    173,
					    179,
					    181,
					    191,
					    193,
					    197,
					    199,
					    211,
					    223,
					    227,
					    229,
					    233,
					    239,
					    241,
					    251,
					    257,
					    263,
					    269,
					    271,
					    277,
					    281,
					    283,
					    293,
					    307,
					    311,
					    313,
					    317,
					    331,
					    337,
					    347,
					    349,
					    353,
					    359,
					    367,
					    373,
					    379,
					    383,
					    389,
					    397,
					    401,
					    409,
					    419,
					    421,
					    431,
					    433,
					    439,
					    443,
					    449,
					    457,
					    461,
					    463,
					    467,
					    479,
					    487,
					    491,
					    499,
					    503,
					    509,
					    521,
					    523,
					    541,
					    547,
					    557,
					    563,
					    569,
					    571,
					    577,
					    587,
					    593,
					    599,
					    601,
					    607,
					    613,
					    617,
					    619,
					    631,
					    641,
					    643,
					    647,
					    653,
					    659,
					    661,
					    673,
					    677,
					    683,
					    691,
					    701,
					    709,
					    719,
					    727,
					    733,
					    739,
					    743,
					    751,
					    757,
					    761,
					    769,
					    773,
					    787,
					    797,
					    809,
					    811,
					    821,
					    823,
					    827,
					    829,
					    839,
					    853,
					    857,
					    859,
					    863,
					    877,
					    881,
					    883,
					    887,
					    907,
					    911,
					    919,
					    929,
					    937,
					    941,
					    947,
					    953,
					    967,
					    971,
					    977,
					    983,
					    991,
					    997,
					]

					if num in low_primes:
										return True

					for prime in low_primes:
										if (num % prime) == 0:
															return False

					return rabin_miller(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__ = 1_024      ):

					"""simple docstring"""






					while True:
										__lowerCAmelCase					=							random.randrange(2 ** (keysize - 1)		,					2 ** (keysize)      )
										if is_prime_low_num(UpperCAmelCase__      ):
															return num


if __name__ == "__main__":
		lowerCamelCase      				=     generate_large_prime()
		print(('''Prime number:''', num))
		print(('''is_prime_low_num:''', is_prime_low_num(num)))






