




"""simple docstring"""





import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
					from transformers.models.mobilevitva.modeling_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
					    make_divisible,
					)


if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor



class   a__					(						__magic_name__ ):


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       self.config_class(**self.inputs_dict)
						self.parent.assertTrue(hasattr(UpperCamelCase_   ,	"width_multiplier"))






class   a__					:


			def __init__(   self   :     Any   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     str=13   ,	UpperCamelCase_   :     Optional[Any]=64   ,	UpperCamelCase_   :     Tuple=2   ,	UpperCamelCase_   :     Optional[int]=3   ,	UpperCamelCase_   :     Dict="swish"   ,	UpperCamelCase_   :     Tuple=3   ,	UpperCamelCase_   :     Optional[Any]=32   ,	UpperCamelCase_   :     Dict=0.1   ,	UpperCamelCase_   :     Any=0.02   ,	UpperCamelCase_   :     Union[str, Any]=True   ,	UpperCamelCase_   :     int=True   ,	UpperCamelCase_   :     Union[str, Any]=10   ,	UpperCamelCase_   :     Union[str, Any]=None   ,	UpperCamelCase_   :     Optional[int]=0.25   ,	UpperCamelCase_   :     Optional[Any]=0.0   ,	UpperCamelCase_   :     Optional[Any]=0.0   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       parent
						__UpperCAmelCase						:			Tuple												=       batch_size
						__UpperCAmelCase						:			Union[str, Any]												=       image_size
						__UpperCAmelCase						:			Any												=       patch_size
						__UpperCAmelCase						:			int												=       num_channels
						__UpperCAmelCase						:			Optional[Any]												=       make_divisible(512 * width_multiplier   ,	divisor=8)
						__UpperCAmelCase						:			Union[str, Any]												=       hidden_act
						__UpperCAmelCase						:			Union[str, Any]												=       conv_kernel_size
						__UpperCAmelCase						:			List[Any]												=       output_stride
						__UpperCAmelCase						:			int												=       classifier_dropout_prob
						__UpperCAmelCase						:			Any												=       use_labels
						__UpperCAmelCase						:			List[Any]												=       is_training
						__UpperCAmelCase						:			Any												=       num_labels
						__UpperCAmelCase						:			str												=       initializer_range
						__UpperCAmelCase						:			Union[str, Any]												=       scope
						__UpperCAmelCase						:			int												=       width_multiplier
						__UpperCAmelCase						:			Optional[int]												=       ffn_dropout
						__UpperCAmelCase						:			Optional[int]												=       attn_dropout


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

						__UpperCAmelCase						:			str												=       None
						__UpperCAmelCase						:			Optional[Any]												=       None
						if self.use_labels:
									__UpperCAmelCase						:			List[Any]												=       ids_tensor([self.batch_size]   ,	self.num_labels)
									__UpperCAmelCase						:			Dict												=       ids_tensor([self.batch_size, self.image_size, self.image_size]   ,	self.num_labels)

						__UpperCAmelCase						:			Any												=       self.get_config()

						return config, pixel_values, labels, pixel_labels


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						return MobileViTVaConfig(
						    image_size=self.image_size   ,	patch_size=self.patch_size   ,	num_channels=self.num_channels   ,	hidden_act=self.hidden_act   ,	conv_kernel_size=self.conv_kernel_size   ,	output_stride=self.output_stride   ,	classifier_dropout_prob=self.classifier_dropout_prob   ,	initializer_range=self.initializer_range   ,	width_multiplier=self.width_multiplier   ,	ffn_dropout=self.ffn_dropout_prob   ,	attn_dropout=self.attn_dropout_prob   ,	)


			def    a_  (   self   :     Optional[int]   ,	UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     Optional[int]   ,	UpperCamelCase_   :     Optional[int]   ,	UpperCamelCase_   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       MobileViTVaModel(config=UpperCamelCase_)
						model.to(UpperCamelCase_)
						model.eval()
						__UpperCAmelCase						:			Union[str, Any]												=       model(UpperCamelCase_)
						self.parent.assertEqual(
						    result.last_hidden_state.shape   ,	(
						        self.batch_size,
						        self.last_hidden_size,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )   ,	)


			def    a_  (   self   :     List[str]   ,	UpperCamelCase_   :     List[Any]   ,	UpperCamelCase_   :     List[Any]   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       self.num_labels
						__UpperCAmelCase						:			int												=       MobileViTVaForImageClassification(UpperCamelCase_)
						model.to(UpperCamelCase_)
						model.eval()
						__UpperCAmelCase						:			Any												=       model(UpperCamelCase_   ,	labels=UpperCamelCase_)
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels))


			def    a_  (   self   :     int   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       self.num_labels
						__UpperCAmelCase						:			List[str]												=       MobileViTVaForSemanticSegmentation(UpperCamelCase_)
						model.to(UpperCamelCase_)
						model.eval()
						__UpperCAmelCase						:			Tuple												=       model(UpperCamelCase_)
						self.parent.assertEqual(
						    result.logits.shape   ,	(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )   ,	)
						__UpperCAmelCase						:			Union[str, Any]												=       model(UpperCamelCase_   ,	labels=UpperCamelCase_)
						self.parent.assertEqual(
						    result.logits.shape   ,	(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )   ,	)


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       self.prepare_config_and_inputs()
						__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       config_and_inputs
						__UpperCAmelCase						:			Any												=       {"pixel_values": pixel_values}
						return config, inputs_dict






@require_torch
class   a__					(						__magic_name__				,     __magic_name__				,     unittest.TestCase ):
			lowercase_		=						(
			    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)

			lowercase_		=						(
			    {
			        "feature-extraction": MobileViTVaModel,
			        "image-classification": MobileViTVaForImageClassification,
			        "image-segmentation": MobileViTVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			lowercase_		=						False
			lowercase_		=						False
			lowercase_		=						False
			lowercase_		=						False


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       MobileViTVaModelTester(self)
						__UpperCAmelCase						:			List[Any]												=       MobileViTVaConfigTester(self   ,	config_class=UpperCamelCase_   ,	has_text_modality=UpperCamelCase_)


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						self.config_tester.run_common_tests()


			@unittest.skip(reason="MobileViTV2 does not use inputs_embeds")
			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						pass


			@unittest.skip(reason="MobileViTV2 does not support input and output embeddings")
			def    a_  (   self   :     List[Any]):


						"""simple docstring"""




						pass


			@unittest.skip(reason="MobileViTV2 does not output attentions")
			def    a_  (   self   :     List[Any]):


						"""simple docstring"""




						pass


			@require_torch_multi_gpu
			@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run.")
			def    a_  (   self   :     str):


						"""simple docstring"""




						pass


			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						pass


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									__UpperCAmelCase						:			List[Any]												=       model_class(UpperCamelCase_)
									__UpperCAmelCase						:			Dict												=       inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__UpperCAmelCase						:			List[Any]												=       [*signature.parameters.keys()]

									__UpperCAmelCase						:			int												=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]   ,	UpperCamelCase_)


			def    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase_)


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						def check_hidden_states_output(UpperCamelCase_   :     str   ,	UpperCamelCase_   :     List[Any]   ,	UpperCamelCase_   :     Optional[int]):
									__UpperCAmelCase						:			str												=       model_class(UpperCamelCase_)
									model.to(UpperCamelCase_)
									model.eval()

									with torch.no_grad():
												__UpperCAmelCase						:			Dict												=       model(**self._prepare_for_class(UpperCamelCase_   ,	UpperCamelCase_))

									__UpperCAmelCase						:			Tuple												=       outputs.hidden_states

									__UpperCAmelCase						:			Tuple												=       5
									self.assertEqual(len(UpperCamelCase_)   ,	UpperCamelCase_)

									# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
									# with the width and height being successively divided by 2.
									__UpperCAmelCase						:			Dict												=       2
									for i in range(len(UpperCamelCase_)):
												self.assertListEqual(
												    list(hidden_states[i].shape[-2:])   ,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]   ,	)
												divisor *= 2

									self.assertEqual(self.model_tester.output_stride   ,	divisor // 2)

						__UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									__UpperCAmelCase						:			Union[str, Any]												=       True
									check_hidden_states_output(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__UpperCAmelCase						:			Union[str, Any]												=       True

									check_hidden_states_output(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_)


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_)


			@slow
			def    a_  (   self   :     Dict):


						"""simple docstring"""




						for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__UpperCAmelCase						:			List[str]												=       MobileViTVaModel.from_pretrained(UpperCamelCase_)
									self.assertIsNotNone(UpperCamelCase_)






def        _UpperCamelCase     (      )      ->      Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase						:			Optional[int]												=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
			return image





@require_torch
@require_vision
class   a__					(						unittest.TestCase ):


			@cached_property
			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						return (
						    MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
						    if is_vision_available()
						    else None
						)


			@slow
			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").to(
						    UpperCamelCase_)

						__UpperCAmelCase						:			List[str]												=       self.default_image_processor
						__UpperCAmelCase						:			str												=       prepare_img()
						__UpperCAmelCase						:			Union[str, Any]												=       image_processor(images=UpperCamelCase_   ,	return_tensors="pt").to(UpperCamelCase_)

						# forward pass
						with torch.no_grad():
									__UpperCAmelCase						:			Union[str, Any]												=       model(**UpperCamelCase_)

						# verify the logits
						__UpperCAmelCase						:			str												=       torch.Size((1, 1000))
						self.assertEqual(outputs.logits.shape   ,	UpperCamelCase_)

						__UpperCAmelCase						:			List[str]												=       torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]).to(UpperCamelCase_)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,	UpperCamelCase_   ,	atol=1e-4))


			@slow
			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
						__UpperCAmelCase						:			int												=       model.to(UpperCamelCase_)

						__UpperCAmelCase						:			Dict												=       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

						__UpperCAmelCase						:			Dict												=       prepare_img()
						__UpperCAmelCase						:			Optional[Any]												=       image_processor(images=UpperCamelCase_   ,	return_tensors="pt").to(UpperCamelCase_)

						# forward pass
						with torch.no_grad():
									__UpperCAmelCase						:			List[Any]												=       model(**UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       outputs.logits

						# verify the logits
						__UpperCAmelCase						:			int												=       torch.Size((1, 21, 32, 32))
						self.assertEqual(logits.shape   ,	UpperCamelCase_)

						__UpperCAmelCase						:			List[str]												=       torch.tensor(
						    [
						        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
						        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
						        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
						    ]   ,	device=UpperCamelCase_   ,	)

						self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,	UpperCamelCase_   ,	atol=1e-4))


			@slow
			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
						__UpperCAmelCase						:			Union[str, Any]												=       model.to(UpperCamelCase_)

						__UpperCAmelCase						:			int												=       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

						__UpperCAmelCase						:			List[str]												=       prepare_img()
						__UpperCAmelCase						:			Dict												=       image_processor(images=UpperCamelCase_   ,	return_tensors="pt").to(UpperCamelCase_)

						# forward pass
						with torch.no_grad():
									__UpperCAmelCase						:			List[str]												=       model(**UpperCamelCase_)

						__UpperCAmelCase						:			int												=       outputs.logits.detach().cpu()

						__UpperCAmelCase						:			int												=       image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_   ,	target_sizes=[(50, 60)])
						__UpperCAmelCase						:			Tuple												=       torch.Size((50, 60))
						self.assertEqual(segmentation[0].shape   ,	UpperCamelCase_)

						__UpperCAmelCase						:			Any												=       image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       torch.Size((32, 32))
						self.assertEqual(segmentation[0].shape   ,	UpperCamelCase_)





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

import numpy as np



def   __UpperCAmelCase					(      __a :     bytes						,__a :     int      )     ->	np.array:

					"""simple docstring"""

					_a      :       int   =						F"""{sampling_rate}"""
					_a      :       str   =						'''1'''
					_a      :       Optional[int]   =						'''f32le'''
					_a      :       Optional[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:
															_a      :       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
					_a      :       Optional[Any]   =						output_stream[0]
					_a      :       Optional[int]   =						np.frombuffer(__a						,np.floataa      )
					if audio.shape[0] == 0:
										raise ValueError('''Malformed soundfile'''      )
					return audio



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     str = "f32le"						,)     ->	str:

					"""simple docstring"""

					_a      :       Dict   =						F"""{sampling_rate}"""
					_a      :       Optional[Any]   =						'''1'''
					if format_for_conversion == "s16le":
										_a      :       Dict   =						2
					elif format_for_conversion == "f32le":
										_a      :       Optional[Any]   =						4
					else:
										raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""      )

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

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



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     Optional[int] = None						,__a :     Optional[Union[Tuple[float, float], float]] = None						,__a :     str = "f32le"						,)     ->	Optional[int]:

					"""simple docstring"""

					if stream_chunk_s is not None:
										_a      :       Tuple   =						stream_chunk_s
					else:
										_a      :       Tuple   =						chunk_length_s

					_a      :       Tuple   =						ffmpeg_microphone(__a						,__a						,format_for_conversion=__a      )
					if format_for_conversion == "s16le":
										_a      :       Any   =						np.intaa
										_a      :       Optional[int]   =						2
					elif format_for_conversion == "f32le":
										_a      :       Dict   =						np.floataa
										_a      :       List[Any]   =						4
					else:
										raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""      )

					if stride_length_s is None:
										_a      :       List[Any]   =						chunk_length_s / 6
					_a      :       Optional[int]   =						int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
					if isinstance(__a						,(int, float)      ):
										_a      :       Optional[Any]   =						[stride_length_s, stride_length_s]

					_a      :       Optional[Any]   =						int(round(sampling_rate * stride_length_s[0]      )      ) * size_of_sample
					_a      :       str   =						int(round(sampling_rate * stride_length_s[1]      )      ) * size_of_sample
					_a      :       Optional[Any]   =						datetime.datetime.now()
					_a      :       Tuple   =						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
										_a      :       Dict   =						np.frombuffer(item['''raw''']						,dtype=__a      )
										_a      :       Dict   =						(
										    item['''stride'''][0] // size_of_sample,
										    item['''stride'''][1] // size_of_sample,
										)
										_a      :       str   =						sampling_rate
										audio_time += delta
										if datetime.datetime.now() > audio_time + 10 * delta:
															# We're late !! SKIP
															continue
										yield item



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     int						,__a :     Tuple[int, int]						,__a :     bool = False      )     ->	Optional[int]:

					"""simple docstring"""

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







def   __UpperCAmelCase					(      __a :     int						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Dict   =						2**24  # 16Mo
					try:
										with subprocess.Popen(__a						,stdout=subprocess.PIPE						,bufsize=__a      ) as ffmpeg_process:
															while True:
																				_a      :       int   =						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



