


'''simple docstring'''






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

__UpperCAmelCase       =							logging.get_logger(__name__)

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

# fmt: off
__UpperCAmelCase       =							[
    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, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
    1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
    4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
    11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
    17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
    34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
__UpperCAmelCase       =							[
    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, 1_350, 1_853, 1_982, 2_460, 2_627,
    3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
    7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
    14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
    22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
    42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Tuple     						=				"whisper"
							lowercase__      :       int     						=				["past_key_values"]
							lowercase__      :       List[str]     						=				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







							def __init__(		self ,   lowerCamelCase_=5_18_65 ,   lowerCamelCase_=80 ,   lowerCamelCase_=6 ,   lowerCamelCase_=4 ,   lowerCamelCase_=6 ,   lowerCamelCase_=4 ,   lowerCamelCase_=15_36 ,   lowerCamelCase_=15_36 ,   lowerCamelCase_=0.0 ,   lowerCamelCase_=0.0 ,   lowerCamelCase_=5_02_57 ,   lowerCamelCase_=True ,   lowerCamelCase_=True ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=2_56 ,   lowerCamelCase_=0.0 ,   lowerCamelCase_=0.0 ,   lowerCamelCase_=0.0 ,   lowerCamelCase_=0.02 ,   lowerCamelCase_=False ,   lowerCamelCase_=15_00 ,   lowerCamelCase_=4_48 ,   lowerCamelCase_=5_02_56 ,   lowerCamelCase_=5_02_56 ,   lowerCamelCase_=5_02_56 ,   lowerCamelCase_=None ,   lowerCamelCase_=[2_20, 5_02_56] ,   lowerCamelCase_=False ,   lowerCamelCase_=2_56 ,   lowerCamelCase_=False ,   lowerCamelCase_=0.05 ,   lowerCamelCase_=10 ,   lowerCamelCase_=2 ,   lowerCamelCase_=0.0 ,   lowerCamelCase_=10 ,   lowerCamelCase_=0 ,   lowerCamelCase_=7 ,   **lowerCamelCase_ ,   )	->							str:
														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     num_mel_bins
														lowerCAmelCase__	       =     d_model
														lowerCAmelCase__	       =     encoder_layers
														lowerCAmelCase__	       =     encoder_attention_heads
														lowerCAmelCase__	       =     decoder_layers
														lowerCAmelCase__	       =     decoder_attention_heads
														lowerCAmelCase__	       =     decoder_ffn_dim
														lowerCAmelCase__	       =     encoder_ffn_dim
														lowerCAmelCase__	       =     dropout
														lowerCAmelCase__	       =     attention_dropout
														lowerCAmelCase__	       =     activation_dropout
														lowerCAmelCase__	       =     activation_function
														lowerCAmelCase__	       =     init_std
														lowerCAmelCase__	       =     encoder_layerdrop
														lowerCAmelCase__	       =     decoder_layerdrop
														lowerCAmelCase__	       =     use_cache
														lowerCAmelCase__	       =     encoder_layers
														lowerCAmelCase__	       =     scale_embedding  # scale factor will be sqrt(d_model) if True
														lowerCAmelCase__	       =     max_source_positions
														lowerCAmelCase__	       =     max_target_positions

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

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

														lowerCAmelCase__	       =     median_filter_width

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




class 		a__    (							a__  ):





							'''simple docstring'''






							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Mapping[str, Mapping[int, str]]:
														lowerCAmelCase__	       =     OrderedDict(
														    [
														        ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
														    ]       )
														if self.use_past:
																					lowerCAmelCase__	       =     {0: '''batch'''}
														else:
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''decoder_sequence'''}

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

														return common_inputs







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = -1 ,   lowerCamelCase_ = -1 ,   lowerCamelCase_ = False ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 2_20_50 ,   lowerCamelCase_ = 5.0 ,   lowerCamelCase_ = 2_20 ,   )	->							Mapping[str, Any]:
														lowerCAmelCase__	       =     OrderedDict()
														lowerCAmelCase__	       =     OnnxConfig.generate_dummy_inputs(
														    self ,   preprocessor=preprocessor.feature_extractor ,   batch_size=lowerCamelCase_ ,   framework=lowerCamelCase_ ,   sampling_rate=lowerCamelCase_ ,   time_duration=lowerCamelCase_ ,   frequency=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     encoder_inputs['''input_features'''].shape[2]
														lowerCAmelCase__	       =     encoder_sequence_length // 2 if self.use_past else seq_length

														lowerCAmelCase__	       =     super().generate_dummy_inputs(
														    preprocessor.tokenizer ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )

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

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

														return dummy_inputs







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							float:
														return 1e-3



'''simple docstring'''






from __future__ import annotations


def  _snake_case       (					A			,   A	)						->   float:
							lowerCAmelCase__	       =     sorted(numsa + numsa	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     divmod(len(A	)			,   2	)
							if mod == 1:
														return all_numbers[div]
							else:
														return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__UpperCAmelCase       =							[float(x) for x in input('''Enter the elements of first array: ''').split()]
		__UpperCAmelCase       =							[float(x) for x in input('''Enter the elements of second array: ''').split()]
		print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")