





"""simple docstring"""





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_UpperCamelCase					      =   logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

_UpperCamelCase					      =   list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_UpperCamelCase					      =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							with open(SCREAMING_SNAKE_CASE     ,    '''rb''' ) as f:
														__lowerCamelCase   :   List[Any]       =Image.open(SCREAMING_SNAKE_CASE )
														return im.convert('''RGB''' )
@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={
				        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
				    }							,	)
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
				__snake_case :							Optional[str]									=		field(default=snake_case__							,	metadata={"""help""": """A folder containing the training data."""} )
				__snake_case :							Optional[str]									=		field(default=snake_case__							,	metadata={"""help""": """A folder containing the validation data."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.15							,	metadata={"""help""": """Percent to split off of train for validation."""} )
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }							,	)
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }							,	)




				def        __lowercase	(  self	:Optional[int]      ):
											if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
																		raise ValueError(
																		    '''You must specify either a dataset name from the hub or a train and/or validation directory.'''      )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							str									=		field(
				    default="""google/vit-base-patch16-224-in21k"""							,	metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							,	)
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(snake_case__ )}							,	)
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
				__snake_case :							str									=		field(
				    default="""main"""							,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,	)
				__snake_case :							str									=		field(default=snake_case__							,	metadata={"""help""": """Name or path of preprocessor config."""} )
				__snake_case :							bool									=		field(
				    default=snake_case__							,	metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }							,	)
				__snake_case :							bool									=		field(
				    default=snake_case__							,	metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}							,	)



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				List[Any] ):


							'''simple docstring'''







							__lowerCamelCase   :   Tuple       =torch.stack([example['''pixel_values'''] for example in examples] )
							__lowerCamelCase   :   Dict       =torch.tensor([example['''labels'''] for example in examples] )
							return {"pixel_values": pixel_values, "labels": labels}



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   Union[str, Any]       =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
							if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
							else:
														__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   str       =parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry('''run_image_classification'''     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,    datefmt='''%m/%d/%Y %H:%M:%S'''     ,    handlers=[logging.StreamHandler(sys.stdout )]     ,    )

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							__lowerCamelCase   :   str       =training_args.get_process_log_level()
							logger.setLevel(SCREAMING_SNAKE_CASE )
							transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE )
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
							    + F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
							logger.info(F'Training/evaluation parameters {training_args}' )

							# Detecting last checkpoint.
							__lowerCamelCase   :   Any       =None
							if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
														__lowerCamelCase   :   List[str]       =get_last_checkpoint(training_args.output_dir )
														if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
																					raise ValueError(
																					    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
																					    '''Use --overwrite_output_dir to overcome.''' )
														elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																					logger.info(
																					    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
																					    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )

    # Set seed before initializing model.
							set_seed(training_args.seed )

							# Initialize our dataset and prepare it for the 'image-classification' task.
							if data_args.dataset_name is not None:
														__lowerCamelCase   :   Any       =load_dataset(
														    data_args.dataset_name     ,    data_args.dataset_config_name     ,    cache_dir=model_args.cache_dir     ,    task='''image-classification'''     ,    use_auth_token=True if model_args.use_auth_token else None     ,    )
							else:
														__lowerCamelCase   :   Dict       ={}
														if data_args.train_dir is not None:
																					__lowerCamelCase   :   Dict       =os.path.join(data_args.train_dir     ,    '''**''' )
														if data_args.validation_dir is not None:
																					__lowerCamelCase   :   Optional[Any]       =os.path.join(data_args.validation_dir     ,    '''**''' )
														__lowerCamelCase   :   Tuple       =load_dataset(
														    '''imagefolder'''     ,    data_files=SCREAMING_SNAKE_CASE     ,    cache_dir=model_args.cache_dir     ,    task='''image-classification'''     ,    )

							# If we don't have a validation split, split off a percentage of train as validation.
							__lowerCamelCase   :   Any       =None if '''validation''' in dataset.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split     ,    SCREAMING_SNAKE_CASE ) and data_args.train_val_split > 0.0:
														__lowerCamelCase   :   Optional[Any]       =dataset['''train'''].train_test_split(data_args.train_val_split )
														__lowerCamelCase   :   List[Any]       =split['''train''']
														__lowerCamelCase   :   Any       =split['''test''']

							# Prepare label mappings.
							# We'll include these in the model's config to get human readable labels in the Inference API.
							__lowerCamelCase   :   List[str]       =dataset['''train'''].features['''labels'''].names
							__lowerCamelCase     ,    __lowerCamelCase   :   int       ={}, {}
							for i, label in enumerate(SCREAMING_SNAKE_CASE ):
														__lowerCamelCase   :   Union[str, Any]       =str(SCREAMING_SNAKE_CASE )
														__lowerCamelCase   :   str       =label

							# Load the accuracy metric from the datasets package
							__lowerCamelCase   :   List[Any]       =evaluate.load('''accuracy''' )

							# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
							# predictions and label_ids field) and has to return a dictionary string to float.
							def compute_metrics(SCREAMING_SNAKE_CASE							:				Dict ):
														return metric.compute(predictions=np.argmax(p.predictions     ,    axis=1 )     ,    references=p.label_ids )

							__lowerCamelCase   :   List[Any]       =AutoConfig.from_pretrained(
							    model_args.config_name or model_args.model_name_or_path     ,    num_labels=len(SCREAMING_SNAKE_CASE )     ,    labelaid=SCREAMING_SNAKE_CASE     ,    idalabel=SCREAMING_SNAKE_CASE     ,    finetuning_task='''image-classification'''     ,    cache_dir=model_args.cache_dir     ,    revision=model_args.model_revision     ,    use_auth_token=True if model_args.use_auth_token else None     ,    )
							__lowerCamelCase   :   Tuple       =AutoModelForImageClassification.from_pretrained(
							    model_args.model_name_or_path     ,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path )     ,    config=SCREAMING_SNAKE_CASE     ,    cache_dir=model_args.cache_dir     ,    revision=model_args.model_revision     ,    use_auth_token=True if model_args.use_auth_token else None     ,    ignore_mismatched_sizes=model_args.ignore_mismatched_sizes     ,    )
							__lowerCamelCase   :   Any       =AutoImageProcessor.from_pretrained(
							    model_args.image_processor_name or model_args.model_name_or_path     ,    cache_dir=model_args.cache_dir     ,    revision=model_args.model_revision     ,    use_auth_token=True if model_args.use_auth_token else None     ,    )

							# Define torchvision transforms to be applied to each image.
							if "shortest_edge" in image_processor.size:
														__lowerCamelCase   :   List[str]       =image_processor.size['''shortest_edge''']
							else:
														__lowerCamelCase   :   Any       =(image_processor.size['''height'''], image_processor.size['''width'''])
							__lowerCamelCase   :   Any       =Normalize(mean=image_processor.image_mean     ,    std=image_processor.image_std )
							__lowerCamelCase   :   Optional[int]       =Compose(
							    [
							        RandomResizedCrop(SCREAMING_SNAKE_CASE ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ] )
							__lowerCamelCase   :   Union[str, Any]       =Compose(
							    [
							        Resize(SCREAMING_SNAKE_CASE ),
							        CenterCrop(SCREAMING_SNAKE_CASE ),
							        ToTensor(),
							        normalize,
							    ] )

							def train_transforms(SCREAMING_SNAKE_CASE							:				int ):
														__lowerCamelCase   :   int       =[
														    _train_transforms(pil_img.convert('''RGB''' ) ) for pil_img in example_batch['''image''']
														]
														return example_batch

							def val_transforms(SCREAMING_SNAKE_CASE							:				Optional[int] ):
														__lowerCamelCase   :   int       =[_val_transforms(pil_img.convert('''RGB''' ) ) for pil_img in example_batch['''image''']]
														return example_batch

							if training_args.do_train:
														if "train" not in dataset:
																					raise ValueError('''--do_train requires a train dataset''' )
														if data_args.max_train_samples is not None:
																					__lowerCamelCase   :   Optional[Any]       =(
																					    dataset['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
																					)
														# Set the training transforms
														dataset["train"].set_transform(SCREAMING_SNAKE_CASE )

							if training_args.do_eval:
														if "validation" not in dataset:
																					raise ValueError('''--do_eval requires a validation dataset''' )
														if data_args.max_eval_samples is not None:
																					__lowerCamelCase   :   str       =(
																					    dataset['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
																					)
														# Set the validation transforms
														dataset["validation"].set_transform(SCREAMING_SNAKE_CASE )

							# Initalize our trainer
							__lowerCamelCase   :   str       =Trainer(
							    model=SCREAMING_SNAKE_CASE     ,    args=SCREAMING_SNAKE_CASE     ,    train_dataset=dataset['''train'''] if training_args.do_train else None     ,    eval_dataset=dataset['''validation'''] if training_args.do_eval else None     ,    compute_metrics=SCREAMING_SNAKE_CASE     ,    tokenizer=SCREAMING_SNAKE_CASE     ,    data_collator=SCREAMING_SNAKE_CASE     ,    )

							# Training
							if training_args.do_train:
														__lowerCamelCase   :   int       =None
														if training_args.resume_from_checkpoint is not None:
																					__lowerCamelCase   :   Any       =training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					__lowerCamelCase   :   Dict       =last_checkpoint
														__lowerCamelCase   :   Tuple       =trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE )
														trainer.save_model()
														trainer.log_metrics('''train'''     ,    train_result.metrics )
														trainer.save_metrics('''train'''     ,    train_result.metrics )
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														__lowerCamelCase   :   int       =trainer.evaluate()
														trainer.log_metrics('''eval'''     ,    SCREAMING_SNAKE_CASE )
														trainer.save_metrics('''eval'''     ,    SCREAMING_SNAKE_CASE )

							# Write model card and (optionally) push to hub
							__lowerCamelCase   :   Optional[int]       ={
							    '''finetuned_from''': model_args.model_name_or_path,
							    '''tasks''': '''image-classification''',
							    '''dataset''': data_args.dataset_name,
							    '''tags''': ['''image-classification''', '''vision'''],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**SCREAMING_SNAKE_CASE )
							else:
														trainer.create_model_card(**SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							main()











"""simple docstring"""





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

import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				bytes     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							__lowerCamelCase   :   List[Any]       ='''f32le'''
							__lowerCamelCase   :   Optional[int]       =[
							    '''ffmpeg''',
							    '''-i''',
							    '''pipe:0''',
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]

							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdin=subprocess.PIPE     ,    stdout=subprocess.PIPE ) as ffmpeg_process:
																					__lowerCamelCase   :   Union[str, Any]       =ffmpeg_process.communicate(SCREAMING_SNAKE_CASE )
							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   :   Tuple       =np.frombuffer(SCREAMING_SNAKE_CASE     ,    np.floataa )
							if audio.shape[0] == 0:
														raise ValueError('''Malformed soundfile''' )
							return audio



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   List[Any]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   List[Any]       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

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

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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[Union[Tuple[float, float], float]] = None     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							if stream_chunk_s is not None:
														__lowerCamelCase   :   Optional[int]       =stream_chunk_s
							else:
														__lowerCamelCase   :   Tuple       =chunk_length_s

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

							if stride_length_s is None:
														__lowerCamelCase   :   Tuple       =chunk_length_s / 6
							__lowerCamelCase   :   str       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,    (int, float) ):
														__lowerCamelCase   :   List[str]       =[stride_length_s, stride_length_s]

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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Tuple[int, int]     ,    SCREAMING_SNAKE_CASE							:				bool = False ):


							'''simple docstring'''







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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







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




