



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
 from ...feature_extraction_utils import FeatureExtractionMixin
 from ...tokenization_utils_base import PreTrainedTokenizerBase
 from ...utils import TensorType

__snake_case						:Any      		=     logging.get_logger(__name__)

__snake_case						:Union[str, Any]      		=     {
    "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}

# fmt: off
__snake_case						:Optional[Any]      		=     [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786,
    1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
    1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
    3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
__snake_case						:List[str]      		=     [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
    1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
    2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
    4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]

class     _A       (	A__    ):
       UpperCamelCase__  :    Tuple											=						'''whisper'''
       UpperCamelCase__  :    Optional[int]											=						['''past_key_values''']
       UpperCamelCase__  :    List[str]											=						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



       def __init__(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Tuple=51_865     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=80     ,		__SCREAMING_SNAKE_CASE							:     str=6     ,		__SCREAMING_SNAKE_CASE							:     int=4     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=6     ,		__SCREAMING_SNAKE_CASE							:     Tuple=4     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=1_536     ,		__SCREAMING_SNAKE_CASE							:     Tuple=1_536     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.0     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=50_257     ,		__SCREAMING_SNAKE_CASE							:     Dict=True     ,		__SCREAMING_SNAKE_CASE							:     int=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     Tuple=256     ,		__SCREAMING_SNAKE_CASE							:     Any=0.0     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.0     ,		__SCREAMING_SNAKE_CASE							:     str=0.0     ,		__SCREAMING_SNAKE_CASE							:     str=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[str]=False     ,		__SCREAMING_SNAKE_CASE							:     Any=1_500     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=448     ,		__SCREAMING_SNAKE_CASE							:     Any=50_256     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=50_256     ,		__SCREAMING_SNAKE_CASE							:     Tuple=50_256     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     str=[220, 50_256]     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     Dict=256     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.05     ,		__SCREAMING_SNAKE_CASE							:     int=10     ,		__SCREAMING_SNAKE_CASE							:     str=2     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=0.0     ,		__SCREAMING_SNAKE_CASE							:     str=10     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=7     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		):


           '''simple docstring'''

           __a						       =				vocab_size
           __a						       =				num_mel_bins
           __a						       =				d_model
           __a						       =				encoder_layers
           __a						       =				encoder_attention_heads
           __a						       =				decoder_layers
           __a						       =				decoder_attention_heads
           __a						       =				decoder_ffn_dim
           __a						       =				encoder_ffn_dim
           __a						       =				dropout
           __a						       =				attention_dropout
           __a						       =				activation_dropout
           __a						       =				activation_function
           __a						       =				init_std
           __a						       =				encoder_layerdrop
           __a						       =				decoder_layerdrop
           __a						       =				use_cache
           __a						       =				encoder_layers
           __a						       =				scale_embedding  # scale factor will be sqrt(d_model) if True
           __a						       =				max_source_positions
           __a						       =				max_target_positions

           # Audio Classification-specific parameters. Feel free to ignore for other classes.
           __a						       =				classifier_proj_size
           __a						       =				use_weighted_layer_sum

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           __a						       =				apply_spec_augment
           __a						       =				mask_time_prob
           __a						       =				mask_time_length
           __a						       =				mask_time_min_masks
           __a						       =				mask_feature_prob
           __a						       =				mask_feature_length
           __a						       =				mask_feature_min_masks

           __a						       =				median_filter_width

           super().__init__(
               pad_token_id=__snake_case     ,		bos_token_id=__snake_case     ,		eos_token_id=__snake_case     ,		is_encoder_decoder=__snake_case     ,		decoder_start_token_id=__snake_case     ,		suppress_tokens=__snake_case     ,		begin_suppress_tokens=__snake_case     ,		**__snake_case     ,		)


class     _A       (	A__    ):



       @property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				OrderedDict(
               [
                   ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
               ])
           if self.use_past:
               __a						       =				{0: '''batch'''}
           else:
               __a						       =				{0: '''batch''', 1: '''decoder_sequence'''}

           if self.use_past:
               self.fill_with_past_key_values_(__snake_case     ,		direction='''inputs''')

           return common_inputs



       def 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]     ,		__SCREAMING_SNAKE_CASE							:     int = -1     ,		__SCREAMING_SNAKE_CASE							:     int = -1     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     Optional["TensorType"] = None     ,		__SCREAMING_SNAKE_CASE							:     int = 22_050     ,		__SCREAMING_SNAKE_CASE							:     float = 5.0     ,		__SCREAMING_SNAKE_CASE							:     int = 220     ,		):


           '''simple docstring'''

           __a						       =				OrderedDict()
           __a						       =				OnnxConfig.generate_dummy_inputs(
               self     ,		preprocessor=preprocessor.feature_extractor     ,		batch_size=__snake_case     ,		framework=__snake_case     ,		sampling_rate=__snake_case     ,		time_duration=__snake_case     ,		frequency=__snake_case     ,		)
           __a						       =				encoder_inputs['''input_features'''].shape[2]
           __a						       =				encoder_sequence_length // 2 if self.use_past else seq_length

           __a						       =				super().generate_dummy_inputs(
               preprocessor.tokenizer     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case)

           __a						       =				encoder_inputs.pop('''input_features''')
           __a						       =				decoder_inputs.pop('''decoder_input_ids''')

           if "past_key_values" in decoder_inputs:
               __a						       =				decoder_inputs.pop('''past_key_values''')

           return dummy_inputs





       @property
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           return 1E-3










'''simple docstring'''






import random

from .binary_exp_mod import bin_exp_mod


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Optional[Any]=1000  )      ->   int:
							if n < 2:
														return False

							if n % 2 == 0:
														return n == 2

							# this means n is odd
							UpperCAmelCase :       str 							=		n - 1
							UpperCAmelCase :       List[Any] 							=		0
							while d % 2 == 0:
														d /= 2
														exp += 1

							# n - 1=d*(2**exp)
							UpperCAmelCase :       List[str] 							=		0
							while count < prec:
														UpperCAmelCase :       int 							=		random.randint(2   ,      n - 1  )
														UpperCAmelCase :       List[str] 							=		bin_exp_mod(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														if b != 1:
																					UpperCAmelCase :       int 							=		True
																					for _ in range(_lowerCAmelCase  ):
																												if b == n - 1:
																																			UpperCAmelCase :       Dict 							=		False
																																			break
																												UpperCAmelCase :       str 							=		b * b
																												b %= n
																					if flag:
																												return False
																					count += 1
							return True


if __name__ == "__main__":
						UpperCamelCase__:   Optional[int]								=			abs(int(input("Enter bound : ").strip()))
						print("Here's the list of primes:")
						print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))





