

"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Any  				=	f'{sampling_rate}'
	lowerCAmelCase__	:						Optional[Any]  				=	'''1'''
	lowerCAmelCase__	:						List[str]  				=	'''f32le'''
	lowerCAmelCase__	:						List[Any]  				=	[
	    '''ffmpeg''',
	    '''-i''',
	    '''pipe:0''',
	    '''-ac''',
	    ac,
	    '''-ar''',
	    ar,
	    '''-f''',
	    format_for_conversion,
	    '''-hide_banner''',
	    '''-loglevel''',
	    '''quiet''',
	    '''pipe:1''',
	]

	try:
		with subprocess.Popen(A_						,     stdin=subprocess.PIPE						,     stdout=subprocess.PIPE ) as ffmpeg_process:
			lowerCAmelCase__	:						Any  				=	ffmpeg_process.communicate(A_ )
	except FileNotFoundError as error:
		raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
	lowerCAmelCase__	:						Any  				=	output_stream[0]
	lowerCAmelCase__	:						Union[str, Any]  				=	np.frombuffer(A_						,     np.floataa )
	if audio.shape[0] == 0:
		raise ValueError('''Malformed soundfile''' )
	return audio

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ = "f32le"						,     ):
	lowerCAmelCase__	:						List[str]  				=	f'{sampling_rate}'
	lowerCAmelCase__	:						str  				=	'''1'''
	if format_for_conversion == "s16le":
		lowerCAmelCase__	:						Tuple  				=	2
	elif format_for_conversion == "f32le":
		lowerCAmelCase__	:						List[str]  				=	4
	else:
		raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

	lowerCAmelCase__	:						Tuple  				=	platform.system()
	if system == "Linux":
		lowerCAmelCase__	:						Optional[int]  				=	'''alsa'''
		lowerCAmelCase__	:						str  				=	'''default'''
	elif system == "Darwin":
		lowerCAmelCase__	:						Optional[int]  				=	'''avfoundation'''
		lowerCAmelCase__	:						Any  				=	''':0'''
	elif system == "Windows":
		lowerCAmelCase__	:						Dict  				=	'''dshow'''
		lowerCAmelCase__	:						List[Any]  				=	'''default'''

	lowerCAmelCase__	:						Optional[int]  				=	[
	    '''ffmpeg''',
	    '''-f''',
	    format_,
	    '''-i''',
	    input_,
	    '''-ac''',
	    ac,
	    '''-ar''',
	    ar,
	    '''-f''',
	    format_for_conversion,
	    '''-fflags''',
	    '''nobuffer''',
	    '''-hide_banner''',
	    '''-loglevel''',
	    '''quiet''',
	    '''pipe:1''',
	]
	lowerCAmelCase__	:						Any  				=	int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
	lowerCAmelCase__	:						Optional[Any]  				=	_ffmpeg_stream(A_						,     A_ )
	for item in iterator:
		yield item

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ = None						,     A_ = None						,     A_ = "f32le"						,     ):
	if stream_chunk_s is not None:
		lowerCAmelCase__	:						Any  				=	stream_chunk_s
	else:
		lowerCAmelCase__	:						str  				=	chunk_length_s

	lowerCAmelCase__	:						List[str]  				=	ffmpeg_microphone(A_						,     A_						,     format_for_conversion=A_ )
	if format_for_conversion == "s16le":
		lowerCAmelCase__	:						str  				=	np.intaa
		lowerCAmelCase__	:						Any  				=	2
	elif format_for_conversion == "f32le":
		lowerCAmelCase__	:						Optional[Any]  				=	np.floataa
		lowerCAmelCase__	:						Dict  				=	4
	else:
		raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

	if stride_length_s is None:
		lowerCAmelCase__	:						str  				=	chunk_length_s / 6
	lowerCAmelCase__	:						Any  				=	int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
	if isinstance(A_						,     (int, float) ):
		lowerCAmelCase__	:						Optional[Any]  				=	[stride_length_s, stride_length_s]

	lowerCAmelCase__	:						str  				=	int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
	lowerCAmelCase__	:						str  				=	int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
	lowerCAmelCase__	:						Optional[Any]  				=	datetime.datetime.now()
	lowerCAmelCase__	:						Optional[int]  				=	datetime.timedelta(seconds=A_ )
	for item in chunk_bytes_iter(A_						,     A_						,     stride=(stride_left, stride_right)						,     stream=A_ ):
		# Put everything back in numpy scale
		lowerCAmelCase__	:						Optional[int]  				=	np.frombuffer(item['''raw''']						,     dtype=A_ )
		lowerCAmelCase__	:						str  				=	(
		    item['''stride'''][0] // size_of_sample,
		    item['''stride'''][1] // size_of_sample,
		)
		lowerCAmelCase__	:						Tuple  				=	sampling_rate
		audio_time += delta
		if datetime.datetime.now() > audio_time + 10 * delta:
			# We're late !! SKIP
			continue
		yield item

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ = False ):
	lowerCAmelCase__	:						str  				=	b''''''
	lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	stride
	if stride_left + stride_right >= chunk_len:
		raise ValueError(
		    f'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
	lowerCAmelCase__	:						Union[str, Any]  				=	0
	for raw in iterator:
		acc += raw
		if stream and len(A_ ) < chunk_len:
			lowerCAmelCase__	:						Tuple  				=	(_stride_left, 0)
			yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
		else:
			while len(A_ ) >= chunk_len:
				# We are flushing the accumulator
				lowerCAmelCase__	:						Dict  				=	(_stride_left, stride_right)
				lowerCAmelCase__	:						Optional[int]  				=	{'''raw''': acc[:chunk_len], '''stride''': stride}
				if stream:
					lowerCAmelCase__	:						Optional[Any]  				=	False
				yield item
				lowerCAmelCase__	:						int  				=	stride_left
				lowerCAmelCase__	:						List[str]  				=	acc[chunk_len - stride_left - stride_right :]
    # Last chunk
	if len(A_ ) > stride_left:
		lowerCAmelCase__	:						Optional[Any]  				=	{'''raw''': acc, '''stride''': (_stride_left, 0)}
		if stream:
			lowerCAmelCase__	:						Tuple  				=	False
		yield item





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						str  				=	2**24  # 16Mo
	try:
		with subprocess.Popen(A_						,     stdout=subprocess.PIPE						,     bufsize=A_ ) as ffmpeg_process:
			while True:
				lowerCAmelCase__	:						List[str]  				=	ffmpeg_process.stdout.read(A_ )
				if raw == b"":
					break
				yield raw
	except FileNotFoundError as error:
		raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error







"""simple docstring"""





import torch

from transformers import AutoModel







class 				SCREAMING_SNAKE_CASE					(		torch.nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				Tuple   ,lowercase_			:				Dict="sayef/fsner-bert-base-uncased"		):
						super(lowercase_   ,self		).__init__()

						lowerCAmelCase__	:						int  				=	AutoModel.from_pretrained(lowercase_   ,return_dict=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.nn.CosineSimilarity(3   ,1E-08		)
						lowerCAmelCase__	:						List[str]  				=	torch.nn.Softmax(dim=1		)







					def        __lowerCAmelCase (       self			:				str   ,**lowercase_			:				int		):
						return self.bert(**lowercase_		).last_hidden_state







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				Optional[int]		):
						return token_embeddings.sum(2   ,keepdim=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				int   ,lowercase_			:				str   ,lowercase_			:				Tuple=1		):
						return self.softmax(T * self.cos(lowercase_   ,lowercase_		)		)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]		):
						lowerCAmelCase__	:						List[Any]  				=	W_supports['''sizes'''].tolist()
						lowerCAmelCase__	:						Dict  				=	W_supports['''start_token_id'''].item()
						lowerCAmelCase__	:						Union[str, Any]  				=	W_supports['''end_token_id'''].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						lowerCAmelCase__	:						Optional[Any]  				=	self.BERT(**lowercase_		)
						lowerCAmelCase__	:						int  				=	self.BERT(**lowercase_		)

						lowerCAmelCase__	:						List[str]  				=	None
						lowerCAmelCase__	:						Union[str, Any]  				=	None

						lowerCAmelCase__	:						int  				=	W_supports['''input_ids'''] == start_token_id
						lowerCAmelCase__	:						Optional[Any]  				=	W_supports['''input_ids'''] == end_token_id

						for i, size in enumerate(lowercase_		):
							if i == 0:
								lowerCAmelCase__	:						str  				=	0
							else:
								lowerCAmelCase__	:						List[Any]  				=	support_sizes[i - 1]

							lowerCAmelCase__	:						Optional[Any]  				=	S[s : s + size][start_token_masks[s : s + size]]
							lowerCAmelCase__	:						List[Any]  				=	S[s : s + size][end_token_masks[s : s + size]]

							lowerCAmelCase__	:						Union[str, Any]  				=	torch.matmul(q[i]   ,s_start.T		).sum(1		).softmax(0		)
							lowerCAmelCase__	:						Any  				=	torch.matmul(q[i]   ,s_end.T		).sum(1		).softmax(0		)

							if p_starts is not None:
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_starts, p_start)		)
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_ends, p_end)		)
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	p_start
								lowerCAmelCase__	:						str  				=	p_end

						return p_starts, p_ends




