

'''simple docstring'''





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

import numpy as np






def 							_lowerCAmelCase (      __magic_name__	:    Tuple      ,   __magic_name__	:    List[str]	)							->     Optional[int]:
						lowercase   :       Optional[int]								=f'''{sampling_rate}'''
						lowercase   :       Optional[Any]								='''1'''
						lowercase   :       Optional[Any]								='''f32le'''
						lowercase   :       List[Any]								=[
						    '''ffmpeg''',
						    '''-i''',
						    '''pipe:0''',
						    '''-ac''',
						    ac,
						    '''-ar''',
						    ar,
						    '''-f''',
						    format_for_conversion,
						    '''-hide_banner''',
						    '''-loglevel''',
						    '''quiet''',
						    '''pipe:1''',
						]

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






def 							_lowerCAmelCase (      __magic_name__	:    str      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    List[str] = "f32le"      ,   )							->     List[Any]:
						lowercase   :       Tuple								=f'''{sampling_rate}'''
						lowercase   :       Optional[int]								='''1'''
						if format_for_conversion == "s16le":
												lowercase   :       List[Any]								=2
						elif format_for_conversion == "f32le":
												lowercase   :       str								=4
						else:
												raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''	)

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

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






def 							_lowerCAmelCase (      __magic_name__	:    List[str]      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    int = None      ,   __magic_name__	:    Any = None      ,   __magic_name__	:    Any = "f32le"      ,   )							->     Optional[int]:
						if stream_chunk_s is not None:
												lowercase   :       Dict								=stream_chunk_s
						else:
												lowercase   :       str								=chunk_length_s

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

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

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






def 							_lowerCAmelCase (      __magic_name__	:    Tuple      ,   __magic_name__	:    int      ,   __magic_name__	:    Optional[Any]      ,   __magic_name__	:    Union[str, Any] = False	)							->     int:
						lowercase   :       Any								=B''''''
						lowercase		,  lowercase   :       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}'''	)
						lowercase   :       Any								=0
						for raw in iterator:
												acc += raw
												if stream and len(lowerCAmelCase__	) < chunk_len:
																		lowercase   :       str								=(_stride_left, 0)
																		yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
												else:
																		while len(lowerCAmelCase__	) >= chunk_len:
																								# We are flushing the accumulator
																								lowercase   :       Optional[int]								=(_stride_left, stride_right)
																								lowercase   :       Optional[Any]								={'''raw''': acc[:chunk_len], '''stride''': stride}
																								if stream:
																														lowercase   :       Dict								=False
																								yield item
																								lowercase   :       Union[str, Any]								=stride_left
																								lowercase   :       str								=acc[chunk_len - stride_left - stride_right :]
    # Last chunk
						if len(lowerCAmelCase__	) > stride_left:
												lowercase   :       Union[str, Any]								={'''raw''': acc, '''stride''': (_stride_left, 0)}
												if stream:
																		lowercase   :       Dict								=False
												yield item







def 							_lowerCAmelCase (      __magic_name__	:    List[Any]      ,   __magic_name__	:    Any	)							->     Any:
						lowercase   :       Optional[Any]								=2**24  # 16Mo
						try:
												with subprocess.Popen(lowerCAmelCase__      ,   stdout=subprocess.PIPE      ,   bufsize=lowerCAmelCase__	) as ffmpeg_process:
																		while True:
																								lowercase   :       Dict								=ffmpeg_process.stdout.read(lowerCAmelCase__	)
																								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 unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.roberta.modeling_flax_roberta import (
							    FlaxRobertaForCausalLM,
							    FlaxRobertaForMaskedLM,
							    FlaxRobertaForMultipleChoice,
							    FlaxRobertaForQuestionAnswering,
							    FlaxRobertaForSequenceClassification,
							    FlaxRobertaForTokenClassification,
							    FlaxRobertaModel,
							)





class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		str=13		,				UpperCAmelCase__    :		Optional[Any]=7		,				UpperCAmelCase__    :		Dict=True		,				UpperCAmelCase__    :		int=True		,				UpperCAmelCase__    :		int=True		,				UpperCAmelCase__    :		Dict=True		,				UpperCAmelCase__    :		Dict=99		,				UpperCAmelCase__    :		str=32		,				UpperCAmelCase__    :		Optional[Any]=5		,				UpperCAmelCase__    :		Any=4		,				UpperCAmelCase__    :		List[Any]=37		,				UpperCAmelCase__    :		str="gelu"		,				UpperCAmelCase__    :		Dict=0.1		,				UpperCAmelCase__    :		List[Any]=0.1		,				UpperCAmelCase__    :		Optional[Any]=512		,				UpperCAmelCase__    :		Any=16		,				UpperCAmelCase__    :		str=2		,				UpperCAmelCase__    :		Tuple=0.02		,				UpperCAmelCase__    :		int=4		,				):
										'''simple docstring'''
										lowercase   :       int								=parent
										lowercase   :       List[str]								=batch_size
										lowercase   :       str								=seq_length
										lowercase   :       Optional[Any]								=is_training
										lowercase   :       Union[str, Any]								=use_attention_mask
										lowercase   :       Optional[Any]								=use_token_type_ids
										lowercase   :       Tuple								=use_labels
										lowercase   :       List[str]								=vocab_size
										lowercase   :       List[str]								=hidden_size
										lowercase   :       Tuple								=num_hidden_layers
										lowercase   :       Any								=num_attention_heads
										lowercase   :       List[str]								=intermediate_size
										lowercase   :       Optional[Any]								=hidden_act
										lowercase   :       Dict								=hidden_dropout_prob
										lowercase   :       List[Any]								=attention_probs_dropout_prob
										lowercase   :       Optional[Any]								=max_position_embeddings
										lowercase   :       Tuple								=type_vocab_size
										lowercase   :       Optional[int]								=type_sequence_label_size
										lowercase   :       Optional[Any]								=initializer_range
										lowercase   :       Optional[int]								=num_choices







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size      )

										lowercase   :       Union[str, Any]								=None
										if self.use_attention_mask:
																lowercase   :       List[str]								=random_attention_mask([self.batch_size, self.seq_length]      )

										lowercase   :       Tuple								=None
										if self.use_token_type_ids:
																lowercase   :       str								=ids_tensor([self.batch_size, self.seq_length]		,				self.type_vocab_size      )

										lowercase   :       int								=RobertaConfig(
										    vocab_size=self.vocab_size		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				hidden_act=self.hidden_act		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				max_position_embeddings=self.max_position_embeddings		,				type_vocab_size=self.type_vocab_size		,				is_decoder=UpperCAmelCase__		,				initializer_range=self.initializer_range		,				)

										return config, input_ids, token_type_ids, attention_mask







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=self.prepare_config_and_inputs()
										lowercase		,  lowercase		,  lowercase		,  lowercase   :       str								=config_and_inputs
										lowercase   :       Optional[Any]								={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.prepare_config_and_inputs()
										lowercase		,  lowercase		,  lowercase		,  lowercase   :       Any								=config_and_inputs

										lowercase   :       List[str]								=True
										lowercase   :       Optional[Any]								=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
										lowercase   :       str								=ids_tensor([self.batch_size, self.seq_length]		,				vocab_size=2      )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)





@require_flax
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						True

				lowerCamelCase_							=						(
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       str								=FlaxRobertaModelTester(self      )







				@slow
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										for model_class_name in self.all_model_classes:
																lowercase   :       Optional[int]								=model_class_name.from_pretrained('''roberta-base'''		,				from_pt=UpperCAmelCase__      )
																lowercase   :       List[Any]								=model(np.ones((1, 1)      )      )
																self.assertIsNotNone(UpperCAmelCase__      )





