
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase						:      Dict									= logging.get_logger(__name__)

lowercase						:      Optional[Any]									= {
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'speech_to_text_2'
	_A			=  ['past_key_values']
	_A			=  {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







	def __init__(      self	:Dict  ,     a	:Tuple=1_0_0_0_0  ,     a	:Optional[int]=6  ,     a	:List[str]=2_0_4_8  ,     a	:Tuple=4  ,     a	:List[Any]=0.0  ,     a	:str=True  ,     a	:Any="relu"  ,     a	:Any=2_5_6  ,     a	:Optional[int]=0.1  ,     a	:Any=0.0  ,     a	:int=0.0  ,     a	:int=0.02  ,     a	:List[Any]=2  ,     a	:Tuple=True  ,     a	:str=1  ,     a	:Optional[int]=0  ,     a	:List[Any]=2  ,     a	:Any=1_0_2_4  ,     **a	:str  ,     )	->				int:
								__UpperCamelCase			:     List[str]         =      vocab_size
								__UpperCamelCase			:     int         =      d_model
								__UpperCamelCase			:     Optional[int]         =      decoder_ffn_dim
								__UpperCamelCase			:     Any         =      decoder_layers
								__UpperCamelCase			:     Any         =      decoder_attention_heads
								__UpperCamelCase			:     Tuple         =      dropout
								__UpperCamelCase			:     Any         =      attention_dropout
								__UpperCamelCase			:     Any         =      activation_dropout
								__UpperCamelCase			:     Dict         =      activation_function
								__UpperCamelCase			:     int         =      init_std
								__UpperCamelCase			:     List[str]         =      decoder_layerdrop
								__UpperCamelCase			:     Optional[Any]         =      use_cache
								__UpperCamelCase			:     int         =      decoder_layers
								__UpperCamelCase			:     Union[str, Any]         =      scale_embedding  # scale factor will be sqrt(d_model) if True
								__UpperCamelCase			:     Dict         =      max_target_positions

								super().__init__(
								    pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     decoder_start_token_id=a  ,     **a  ,     )

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int | None, int | None, float]:






							'''simple docstring'''





							if not arr:
														return None, None, 0
							if low == high:
														return low, high, arr[low]

							__UpperCamelCase			:     Tuple         =      (low + high) // 2
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_subarray(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      max_subarray(_lowerCamelCase		,						mid + 1		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_cross_sum(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							if left_sum >= right_sum and left_sum >= cross_sum:
														return left_low, left_high, left_sum
							elif right_sum >= left_sum and right_sum >= cross_sum:
														return right_low, right_high, right_sum
							return cross_left, cross_right, cross_sum




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int, int, float]:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      float("-inf"), -1
							__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      float("-inf"), -1

							__UpperCamelCase			:     int | float         =      0
							for i in range(_lowerCamelCase		,						low - 1		,						-1):
														summ += arr[i]
														if summ > left_sum:
																					__UpperCamelCase			:     Tuple         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							__UpperCamelCase			:     Union[str, Any]         =      0
							for i in range(mid + 1		,						high + 1):
														summ += arr[i]
														if summ > right_sum:
																					__UpperCamelCase			:     Union[str, Any]         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							return max_left, max_right, (left_sum + right_sum)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					float:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      [randint(1		,						_lowerCamelCase) for _ in range(_lowerCamelCase)]
							__UpperCamelCase			:     Optional[Any]         =      time.time()
							max_subarray(_lowerCamelCase		,						0		,						input_size - 1)
							__UpperCamelCase			:     Any         =      time.time()
							return end - start




def 			_SCREAMING_SNAKE_CASE      (	)      ->					None:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      [10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
							__UpperCamelCase			:     Union[str, Any]         =      [time_max_subarray(_lowerCamelCase) for input_size in input_sizes]
							print("No of Inputs\t\tTime Taken")
							for input_size, runtime in zip(_lowerCamelCase		,						_lowerCamelCase):
														print(_lowerCamelCase		,						"\t\t"		,						_lowerCamelCase)
							plt.plot(_lowerCamelCase		,						_lowerCamelCase)
							plt.xlabel("Number of Inputs")
							plt.ylabel("Time taken in seconds")
							plt.show()


if __name__ == "__main__":
		from doctest import testmod

		testmod()