
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

_lowerCamelCase	:    Optional[Any]          =      logging.get_logger(__name__)

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

# fmt: off
_lowerCamelCase	:    List[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, 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
]
_lowerCamelCase	:    Union[str, Any]          =      [
    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        SCREAMING_SNAKE_CASE__					(     __UpperCamelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Optional[Any]          = """whisper"""
	_UpperCAmelCase	:		str          = ["""past_key_values"""]
	_UpperCAmelCase	:		List[str]          = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



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


				'''simple docstring'''


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

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

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

				_snake_case          =      median_filter_width

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




class        SCREAMING_SNAKE_CASE__					(     __UpperCamelCase ):





	'''simple docstring'''



	@property
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


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

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

				return common_inputs



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


				'''simple docstring'''


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

				_snake_case          =      super().generate_dummy_inputs(
				    preprocessor.tokenizer      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )

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

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

				return dummy_inputs





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


				'''simple docstring'''


				return 1E-3

from functools import lru_cache






@lru_cache
def 	a_		(	__lowercase       :     int			)			->  int:
			if num < 0:
						raise ValueError('Number should not be negative.'			)

			return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()