




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__A						:							Optional[Any]							=  logging.get_logger(__name__)




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''input_values''', '''padding_mask''']





							def __init__(					self       :	str       ,     a       :	int = 1       ,     a       :	int = 24_000       ,     a       :	float = 0.0       ,     a       :	float = None       ,     a       :	float = None       ,     **a       :	List[Any]       ,     )			->					Tuple:
										super().__init__(feature_size=a       ,     sampling_rate=a       ,     padding_value=a       ,     **a			)
										SCREAMING_SNAKE_CASE           =					chunk_length_s
										SCREAMING_SNAKE_CASE           =					overlap





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[int]:
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate			)





							@property
							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Optional[int]:
										if self.chunk_length_s is None or self.overlap is None:
													return None
										else:
													return max(1       ,     int((1.0 - self.overlap) * self.chunk_length			)			)







							def __call__(					self       :	Dict       ,     a       :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,     a       :	Optional[Union[bool, str, PaddingStrategy]] = None       ,     a       :	Optional[bool] = False       ,     a       :	Optional[int] = None       ,     a       :	Optional[Union[str, TensorType]] = None       ,     a       :	Optional[int] = None       ,     )			->					BatchFeature:
										if sampling_rate is not None:
													if sampling_rate != self.sampling_rate:
																raise ValueError(
																    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																    f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
																    f""" {self.sampling_rate} and not {sampling_rate}."""			)
										else:
													logger.warning(
													    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
													    """Failing to do so can result in silent errors that might be hard to debug."""			)

										if padding and truncation:
													raise ValueError("""Both padding and truncation were set. Make sure you only set one."""			)
										elif padding is None:
													# by default let's pad the inputs
													SCREAMING_SNAKE_CASE           =					True

										SCREAMING_SNAKE_CASE           =					bool(
										    isinstance(a       ,     (list, tuple)			) and (isinstance(raw_audio[0]       ,     (np.ndarray, tuple, list)			))			)

										if is_batched:
													SCREAMING_SNAKE_CASE           =					[np.asarray(a       ,     dtype=np.floataa			).T for audio in raw_audio]
										elif not is_batched and not isinstance(a       ,     np.ndarray			):
													SCREAMING_SNAKE_CASE           =					np.asarray(a       ,     dtype=np.floataa			)
										elif isinstance(a       ,     np.ndarray			) and raw_audio.dtype is np.dtype(np.floataa			):
													SCREAMING_SNAKE_CASE           =					raw_audio.astype(np.floataa			)

										# always return batch
										if not is_batched:
													SCREAMING_SNAKE_CASE           =					[np.asarray(a			).T]

										# verify inputs are valid
										for idx, example in enumerate(a			):
													if example.ndim > 2:
																raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""			)
													if self.feature_size == 1 and example.ndim != 1:
																raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""			)
													if self.feature_size == 2 and example.shape[-1] != 2:
																raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""			)

										SCREAMING_SNAKE_CASE           =					None
										SCREAMING_SNAKE_CASE           =					BatchFeature({"""input_values""": raw_audio}			)
										if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
													if truncation:
																SCREAMING_SNAKE_CASE           =					min(array.shape[0] for array in raw_audio			)
																SCREAMING_SNAKE_CASE           =					int(np.floor(max_length / self.chunk_stride			)			)
																SCREAMING_SNAKE_CASE           =					(nb_step - 1) * self.chunk_stride + self.chunk_length
													elif padding:
																SCREAMING_SNAKE_CASE           =					max(array.shape[0] for array in raw_audio			)
																SCREAMING_SNAKE_CASE           =					int(np.ceil(max_length / self.chunk_stride			)			)
																SCREAMING_SNAKE_CASE           =					(nb_step - 1) * self.chunk_stride + self.chunk_length
																SCREAMING_SNAKE_CASE           =					"""max_length"""
													else:
																SCREAMING_SNAKE_CASE           =					input_values

        # normal padding on batch
										if padded_inputs is None:
													SCREAMING_SNAKE_CASE           =					self.pad(
													    a       ,     max_length=a       ,     truncation=a       ,     padding=a       ,     return_attention_mask=a       ,     )
													if padding:
																SCREAMING_SNAKE_CASE           =					padded_inputs.pop("""attention_mask"""			)

										SCREAMING_SNAKE_CASE           =					[]
										for example in padded_inputs.pop("""input_values"""			):
													if self.feature_size == 1:
																SCREAMING_SNAKE_CASE           =					example[..., None]
													input_values.append(example.T			)

										SCREAMING_SNAKE_CASE           =					input_values
										if return_tensors is not None:
													SCREAMING_SNAKE_CASE           =					padded_inputs.convert_to_tensors(a			)

										return padded_inputs












import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


__A						:							Tuple							=  importlib.util.find_spec("""s3fs""") is not None

if _has_safs:
		from .safilesystem import SaFileSystem  # noqa: F401

__A						:							List[compression.BaseCompressedFileFileSystem]							=  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
		if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
				warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
		fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if "://" in dataset_path:
						SCREAMING_SNAKE_CASE           =					dataset_path.split("""://"""   )[1]
			return dataset_path
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if fs is not None and fs.protocol != "file":
						return True
			else:
						return False
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					not is_remote_filesystem(SCREAMING_SNAKE_CASE   )
			if is_local:
						# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
						shutil.move(fs._strip_protocol(SCREAMING_SNAKE_CASE   )					,	fs._strip_protocol(SCREAMING_SNAKE_CASE   )   )
			else:
						fs.mv(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	recursive=SCREAMING_SNAKE_CASE   )




def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			if hasattr(fsspec.asyn					,	"""reset_lock"""   ):
						# for future fsspec>2022.05.0
						fsspec.asyn.reset_lock()
			else:
						SCREAMING_SNAKE_CASE           =					None
						SCREAMING_SNAKE_CASE           =					None
						SCREAMING_SNAKE_CASE           =					threading.Lock()






