





'''simple docstring'''






from random import randint
from tempfile import TemporaryFile

import numpy as np



def        UpperCamelCase__		(						__magic_name__  :   List[str]		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   Union[str, Any]      )				->			Any:
 '''simple docstring'''






 snake_case__     :					str    =  0
 if start < end:
  snake_case__     :					str    =  randint(__magic_name__		,       __magic_name__      )
  snake_case__     :					Optional[Any]    =  a[end]
  snake_case__     :					Dict    =  a[pivot]
  snake_case__     :					Union[str, Any]    =  temp

  snake_case__  ,							snake_case__     :					List[Any]    =  _in_place_partition(__magic_name__		,       __magic_name__		,       __magic_name__      )
  count += _in_place_quick_sort(__magic_name__		,       __magic_name__		,       p - 1      )
  count += _in_place_quick_sort(__magic_name__		,       p + 1		,       __magic_name__      )
 return count



def        UpperCamelCase__		(						__magic_name__  :   Union[str, Any]		,       __magic_name__  :   str		,       __magic_name__  :   int      )				->			Union[str, Any]:
 '''simple docstring'''






 snake_case__     :					Optional[int]    =  0
 snake_case__     :					int    =  randint(__magic_name__		,       __magic_name__      )
 snake_case__     :					Any    =  a[end]
 snake_case__     :					Dict    =  a[pivot]
 snake_case__     :					List[Any]    =  temp
 snake_case__     :					List[str]    =  start - 1
 for index in range(__magic_name__		,       __magic_name__      ):
  count += 1
  if a[index] < a[end]:  # check if current val is less than pivot value
   snake_case__     :					Dict    =  new_pivot_index + 1
   snake_case__     :					Union[str, Any]    =  a[new_pivot_index]
   snake_case__     :					int    =  a[index]
   snake_case__     :					str    =  temp

 snake_case__     :					List[Any]    =  a[new_pivot_index + 1]
 snake_case__     :					Optional[int]    =  a[end]
 snake_case__     :					List[Any]    =  temp
 return new_pivot_index + 1, count


A_		:  Dict		 =						TemporaryFile()
A_		:  str		 =						100  # 1000 elements are to be sorted


A_ ,		A_		:  Union[str, Any]		 =						0, 1  # mean and standard deviation
A_		:  Optional[Any]		 =						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
A_		:  List[str]		 =						np.load(outfile)
A_		:  List[Any]		 =						len(M) - 1
A_		:  List[Any]		 =						_in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)





print(z)






'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


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

lowerCAmelCase					:    Dict           = {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

											if len(self.conv_kernel     ) != self.num_conv_layers:
																		raise ValueError(
																		    'Configuration for convolutional module is incorrect. '
																		    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
																		    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel     )}`, '''
																		    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''     )




