



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

lowercase_								=     logging.get_logger(__name__)

lowercase_								=     {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
lowercase_								=     [
    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
]
lowercase_								=     [
    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 				_snake_case			(					_UpperCamelCase):
				UpperCamelCase__				:					List[Any]  		="whisper"
				UpperCamelCase__				:					Dict  		=["past_key_values"]
				UpperCamelCase__				:					List[Any]  		={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




				def __init__(						self					:						Optional[Any],					__lowercase					:						Dict=5_1865,					__lowercase					:						Tuple=80,					__lowercase					:						Optional[Any]=6,					__lowercase					:						List[Any]=4,					__lowercase					:						Any=6,					__lowercase					:						Tuple=4,					__lowercase					:						List[str]=1536,					__lowercase					:						str=1536,					__lowercase					:						Any=0.0,					__lowercase					:						List[str]=0.0,					__lowercase					:						Dict=5_0257,					__lowercase					:						str=True,					__lowercase					:						Optional[Any]=True,					__lowercase					:						Optional[Any]="gelu",					__lowercase					:						Optional[int]=256,					__lowercase					:						List[Any]=0.0,					__lowercase					:						Dict=0.0,					__lowercase					:						List[Any]=0.0,					__lowercase					:						int=0.02,					__lowercase					:						Optional[Any]=False,					__lowercase					:						Optional[Any]=1500,					__lowercase					:						List[str]=448,					__lowercase					:						Union[str, Any]=5_0256,					__lowercase					:						List[str]=5_0256,					__lowercase					:						Any=5_0256,					__lowercase					:						str=None,					__lowercase					:						List[Any]=[220, 5_0256],					__lowercase					:						int=False,					__lowercase					:						str=256,					__lowercase					:						Any=False,					__lowercase					:						Optional[Any]=0.05,					__lowercase					:						Union[str, Any]=10,					__lowercase					:						int=2,					__lowercase					:						int=0.0,					__lowercase					:						Optional[int]=10,					__lowercase					:						str=0,					__lowercase					:						Any=7,					**__lowercase					:						Tuple,					):
							lowercase__					  =				vocab_size
							lowercase__					  =				num_mel_bins
							lowercase__					  =				d_model
							lowercase__					  =				encoder_layers
							lowercase__					  =				encoder_attention_heads
							lowercase__					  =				decoder_layers
							lowercase__					  =				decoder_attention_heads
							lowercase__					  =				decoder_ffn_dim
							lowercase__					  =				encoder_ffn_dim
							lowercase__					  =				dropout
							lowercase__					  =				attention_dropout
							lowercase__					  =				activation_dropout
							lowercase__					  =				activation_function
							lowercase__					  =				init_std
							lowercase__					  =				encoder_layerdrop
							lowercase__					  =				decoder_layerdrop
							lowercase__					  =				use_cache
							lowercase__					  =				encoder_layers
							lowercase__					  =				scale_embedding  # scale factor will be sqrt(d_model) if True
							lowercase__					  =				max_source_positions
							lowercase__					  =				max_target_positions

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

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

							lowercase__					  =				median_filter_width

							super().__init__(
							    pad_token_id=__a,					bos_token_id=__a,					eos_token_id=__a,					is_encoder_decoder=__a,					decoder_start_token_id=__a,					suppress_tokens=__a,					begin_suppress_tokens=__a,					**__a,					)
class 				_snake_case			(					_UpperCamelCase):




				@property
				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				OrderedDict(
							    [
							        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
							    ]						)
							if self.use_past:
										lowercase__					  =				{0: "batch"}
							else:
										lowercase__					  =				{0: "batch", 1: "decoder_sequence"}

							if self.use_past:
										self.fill_with_past_key_values_(__a,					direction="inputs"						)

							return common_inputs




				def 							A__      (						self					:						Dict,					__lowercase					:						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],					__lowercase					:						int = -1,					__lowercase					:						int = -1,					__lowercase					:						bool = False,					__lowercase					:						Optional["TensorType"] = None,					__lowercase					:						int = 2_2050,					__lowercase					:						float = 5.0,					__lowercase					:						int = 220,					):
							lowercase__					  =				OrderedDict()
							lowercase__					  =				OnnxConfig.generate_dummy_inputs(
							    self,					preprocessor=preprocessor.feature_extractor,					batch_size=__a,					framework=__a,					sampling_rate=__a,					time_duration=__a,					frequency=__a,					)
							lowercase__					  =				encoder_inputs["input_features"].shape[2]
							lowercase__					  =				encoder_sequence_length // 2 if self.use_past else seq_length

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

							lowercase__					  =				encoder_inputs.pop("input_features"						)
							lowercase__					  =				decoder_inputs.pop("decoder_input_ids"						)

							if "past_key_values" in decoder_inputs:
										lowercase__					  =				decoder_inputs.pop("past_key_values"						)

							return dummy_inputs




				@property
				def 							A__      (						self					:						str						):
							return 1e-3









import math



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			if 0 not in (x, y):
						# We use the relation x^y = y*log10(x), where 10 is the base.
						return y * math.logaa(SCREAMING_SNAKE_CASE_  )
			else:
						if x == 0:  # 0 raised to any number is 0
									return 0
						elif y == 0:
									return 1  # any number raised to 0 is 1
			raise AssertionError("This should never happen"  )


if __name__ == "__main__":  # Main function
							# Read two numbers from input and typecast them to int using map function.
							# Here x is the base and y is the power.
							lowercase_								=     """Enter the base and the power separated by a comma: """
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))

							# We find the log of each number, using the function res(), which takes two
							# arguments.
							lowercase_								=     res(xa, ya)
							lowercase_								=     res(xa, ya)

							# We check for the largest number
							if resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							elif resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							else:
														print("""Both are equal""")




