



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

import numpy as np


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      np.array:
			A__       =    f'''{sampling_rate}'''
			A__       =    '1'
			A__       =    'f32le'
			A__       =    [
			    'ffmpeg',
			    '-i',
			    'pipe:0',
			    '-ac',
			    ac,
			    '-ar',
			    ar,
			    '-f',
			    format_for_conversion,
			    '-hide_banner',
			    '-loglevel',
			    'quiet',
			    'pipe:1',
			]

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = "f32le" ,	)						->      Tuple:
			A__       =    f'''{sampling_rate}'''
			A__       =    '1'
			if format_for_conversion == "s16le":
						A__       =    2
			elif format_for_conversion == "f32le":
						A__       =    4
			else:
						raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

			A__       =    platform.system()
			if system == "Linux":
						A__       =    'alsa'
						A__       =    'default'
			elif system == "Darwin":
						A__       =    'avfoundation'
						A__       =    ':0'
			elif system == "Windows":
						A__       =    'dshow'
						A__       =    'default'

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = None ,	__UpperCamelCase = None ,	__UpperCamelCase = "f32le" ,	)						->      List[str]:
			if stream_chunk_s is not None:
						A__       =    stream_chunk_s
			else:
						A__       =    chunk_length_s

			A__       =    ffmpeg_microphone(__UpperCamelCase ,	__UpperCamelCase ,	format_for_conversion=__UpperCamelCase					)
			if format_for_conversion == "s16le":
						A__       =    np.intaa
						A__       =    2
			elif format_for_conversion == "f32le":
						A__       =    np.floataa
						A__       =    4
			else:
						raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

			if stride_length_s is None:
						A__       =    chunk_length_s / 6
			A__       =    int(round(sampling_rate * chunk_length_s					)					) * size_of_sample
			if isinstance(__UpperCamelCase ,	(int, float)					):
						A__       =    [stride_length_s, stride_length_s]

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      Dict:
			A__       =    b''
			A__     , A__       =    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}'''					)
			A__       =    0
			for raw in iterator:
						acc += raw
						if stream and len(__UpperCamelCase					) < chunk_len:
									A__       =    (_stride_left, 0)
									yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
						else:
									while len(__UpperCamelCase					) >= chunk_len:
												# We are flushing the accumulator
												A__       =    (_stride_left, stride_right)
												A__       =    {'raw': acc[:chunk_len], 'stride': stride}
												if stream:
															A__       =    False
												yield item
												A__       =    stride_left
												A__       =    acc[chunk_len - stride_left - stride_right :]
    # Last chunk
			if len(__UpperCamelCase					) > stride_left:
						A__       =    {'raw': acc, 'stride': (_stride_left, 0)}
						if stream:
									A__       =    False
						yield item






def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Optional[int]:
			A__       =    2**24  # 16Mo
			try:
						with subprocess.Popen(__UpperCamelCase ,	stdout=subprocess.PIPE ,	bufsize=__UpperCamelCase					) as ffmpeg_process:
									while True:
												A__       =    ffmpeg_process.stdout.read(__UpperCamelCase					)
												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









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



					def __init__(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_snake_case					:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,					_snake_case					:       bool = True				,					_snake_case					:       Union[bool, str, PaddingStrategy] = False				,					_snake_case					:       Union[bool, str, TruncationStrategy] = None				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       int = 0				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = True				,					_snake_case					:       Optional[Union[str, TensorType]] = None				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    self.tokenizer(
								    text=_snake_case				,					add_special_tokens=_snake_case				,					padding=_snake_case				,					truncation=_snake_case				,					max_length=_snake_case				,					stride=_snake_case				,					pad_to_multiple_of=_snake_case				,					return_token_type_ids=_snake_case				,					return_attention_mask=_snake_case				,					return_overflowing_tokens=_snake_case				,					return_special_tokens_mask=_snake_case				,					return_offsets_mapping=_snake_case				,					return_length=_snake_case				,					verbose=_snake_case				,					return_tensors=_snake_case				,					**_snake_case				,					)
								# add pixel_values + pixel_mask
								A__       =    self.image_processor(
								    _snake_case				,					return_tensors=_snake_case				,					do_normalize=_snake_case				,					do_center_crop=_snake_case				,					**_snake_case					)
								encoding.update(_snake_case					)

								return encoding



					def     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


								return self.tokenizer.batch_decode(*_snake_case				,					**_snake_case					)



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return self.tokenizer.decode(*_snake_case				,					**_snake_case					)



					@property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.tokenizer.model_input_names
								A__       =    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)




