




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					:       List[Any]    					=      logging.get_logger(__name__)

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

# fmt: off
__UpperCamelCase					:       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
]
__UpperCamelCase					:       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, 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 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  '''whisper'''
							UpperCamelCase__					     =  ['''past_key_values''']
							UpperCamelCase__					     =  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
							def __init__(						self						:Union[str, Any]		,      __magic_name__						:List[str]=5_1865		,      __magic_name__						:Any=80		,      __magic_name__						:List[str]=6		,      __magic_name__						:Dict=4		,      __magic_name__						:Union[str, Any]=6		,      __magic_name__						:Dict=4		,      __magic_name__						:str=1536		,      __magic_name__						:Tuple=1536		,      __magic_name__						:int=0.0		,      __magic_name__						:Any=0.0		,      __magic_name__						:List[str]=5_0257		,      __magic_name__						:Union[str, Any]=True		,      __magic_name__						:int=True		,      __magic_name__						:Optional[int]="gelu"		,      __magic_name__						:Dict=256		,      __magic_name__						:Dict=0.0		,      __magic_name__						:int=0.0		,      __magic_name__						:List[str]=0.0		,      __magic_name__						:Union[str, Any]=0.02		,      __magic_name__						:List[str]=False		,      __magic_name__						:str=1500		,      __magic_name__						:int=448		,      __magic_name__						:List[str]=5_0256		,      __magic_name__						:int=5_0256		,      __magic_name__						:str=5_0256		,      __magic_name__						:Union[str, Any]=None		,      __magic_name__						:Optional[Any]=[220, 5_0256]		,      __magic_name__						:List[str]=False		,      __magic_name__						:List[str]=256		,      __magic_name__						:Optional[Any]=False		,      __magic_name__						:List[str]=0.05		,      __magic_name__						:Any=10		,      __magic_name__						:Optional[Any]=2		,      __magic_name__						:Union[str, Any]=0.0		,      __magic_name__						:Dict=10		,      __magic_name__						:Optional[int]=0		,      __magic_name__						:Optional[Any]=7		,      **__magic_name__						:Dict		,      ):







								'''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=__magic_name__		,      bos_token_id=__magic_name__		,      eos_token_id=__magic_name__		,      is_encoder_decoder=__magic_name__		,      decoder_start_token_id=__magic_name__		,      suppress_tokens=__magic_name__		,      begin_suppress_tokens=__magic_name__		,      **__magic_name__		,      )







class 		__lowerCAmelCase	(	__magic_name__ ):
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''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_(__magic_name__		,      direction="""inputs"""				)

								return common_inputs
							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]		,      __magic_name__						:int = -1		,      __magic_name__						:int = -1		,      __magic_name__						:bool = False		,      __magic_name__						:Optional["TensorType"] = None		,      __magic_name__						:int = 2_2050		,      __magic_name__						:float = 5.0		,      __magic_name__						:int = 220		,      ):







								'''simple docstring'''







								a										=							OrderedDict()
								a										=							OnnxConfig.generate_dummy_inputs(
								    self		,      preprocessor=preprocessor.feature_extractor		,      batch_size=__magic_name__		,      framework=__magic_name__		,      sampling_rate=__magic_name__		,      time_duration=__magic_name__		,      frequency=__magic_name__		,      )
								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		,      __magic_name__		,      __magic_name__		,      __magic_name__		,      __magic_name__				)

								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						:Union[str, Any]				):







								'''simple docstring'''







								return 1E-3







import math
from datetime import datetime, timedelta





def 		__A		(	__lowerCamelCase    )      ->       datetime:
	a										=							year % 19
	a										=							year % 4
	a										=							year % 7
	a										=							math.floor(year / 100    )
	a										=							math.floor((13 + 8 * leap_day_inhibits) / 25    )
	a										=							leap_day_inhibits / 4
	a										=							(
	    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
	) % 30
	a										=							(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

	# days to be added to March 21
	a										=							(19 * metonic_cycle + secular_moon_shift) % 30

	# PHM -> Paschal Full Moon
	a										=							(
	    2 * julian_leap_year
	    + 4 * non_leap_year
	    + 6 * days_to_add
	    + century_starting_point
	) % 7

	if days_to_add == 29 and days_from_phm_to_sunday == 6:
		return datetime(__lowerCamelCase							,			4							,			19    )
	elif days_to_add == 28 and days_from_phm_to_sunday == 6:
		return datetime(__lowerCamelCase							,			4							,			18    )
	else:
		return datetime(__lowerCamelCase							,			3							,			22    ) + timedelta(
		    days=int(days_to_add + days_from_phm_to_sunday    )    )


if __name__ == "__main__":
		for year in (1_994, 2_000, 2_010, 2_021, 2_023):
				__UpperCamelCase					:       Tuple    					=      "will be" if year > datetime.now().year else "was"
				print(F'Easter in {year} {tense} {gauss_easter(year)}')

