



import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_lowerCamelCase         =	get_logger(__name__)

# Datasets
_lowerCamelCase         =	"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
_lowerCamelCase         =	"""https://cdn-datasets.huggingface.co/datasets/datasets"""
_lowerCamelCase         =	"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"""

# Metrics
_lowerCamelCase         =	"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"""
_lowerCamelCase         =	"""https://cdn-datasets.huggingface.co/datasets/metric"""
_lowerCamelCase         =	"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"""

# Hub
_lowerCamelCase         =	os.environ.get("""HF_ENDPOINT""", """https://huggingface.co""")
_lowerCamelCase         =	HF_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
_lowerCamelCase         =	"""main"""

_lowerCamelCase         =	version.parse(platform.python_version())

if PY_VERSION < version.parse("""3.8"""):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
_lowerCamelCase         =	{"""1""", """ON""", """YES""", """TRUE"""}
_lowerCamelCase         =	ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


# Imports
_lowerCamelCase         =	version.parse(importlib_metadata.version("""dill"""))
_lowerCamelCase         =	version.parse(importlib_metadata.version("""pandas"""))
_lowerCamelCase         =	version.parse(importlib_metadata.version("""pyarrow"""))

_lowerCamelCase         =	os.environ.get("""USE_TF""", """AUTO""").upper()
_lowerCamelCase         =	os.environ.get("""USE_TORCH""", """AUTO""").upper()
_lowerCamelCase         =	os.environ.get("""USE_JAX""", """AUTO""").upper()

_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
      _lowerCamelCase         =	importlib.util.find_spec("""torch""") is not None
      if TORCH_AVAILABLE:
            try:
                  _lowerCamelCase         =	version.parse(importlib_metadata.version("""torch"""))
                  logger.info(f'''PyTorch version {TORCH_VERSION} available.''')
            except importlib_metadata.PackageNotFoundError:
                  pass
else:
      logger.info("""Disabling PyTorch because USE_TF is set""")

_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
      _lowerCamelCase         =	importlib.util.find_spec("""tensorflow""") is not None
      if TF_AVAILABLE:
            # For the metadata, we have to look for both tensorflow and tensorflow-cpu
            for package in [
                "tensorflow",
                "tensorflow-cpu",
                "tensorflow-gpu",
                "tf-nightly",
                "tf-nightly-cpu",
                "tf-nightly-gpu",
                "intel-tensorflow",
                "tensorflow-rocm",
                "tensorflow-macos",
            ]:
                  try:
                        _lowerCamelCase         =	version.parse(importlib_metadata.version(package))
                  except importlib_metadata.PackageNotFoundError:
                        continue
                  else:
                        break
            else:
                  _lowerCamelCase         =	False
      if TF_AVAILABLE:
            if TF_VERSION.major < 2:
                  logger.info(f'''TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.''')
                  _lowerCamelCase         =	False
            else:
                  logger.info(f'''TensorFlow version {TF_VERSION} available.''')
else:
      logger.info("""Disabling Tensorflow because USE_TORCH is set""")


_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
      _lowerCamelCase         =	importlib.util.find_spec("""jax""") is not None and importlib.util.find_spec("""jaxlib""") is not None
      if JAX_AVAILABLE:
            try:
                  _lowerCamelCase         =	version.parse(importlib_metadata.version("""jax"""))
                  logger.info(f'''JAX version {JAX_VERSION} available.''')
            except importlib_metadata.PackageNotFoundError:
                  pass
else:
      logger.info("""Disabling JAX because USE_JAX is set to False""")


_lowerCamelCase         =	os.environ.get("""USE_BEAM""", """AUTO""").upper()
_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
      try:
            _lowerCamelCase         =	version.parse(importlib_metadata.version("""apache_beam"""))
            _lowerCamelCase         =	True
            logger.info(f'''Apache Beam version {BEAM_VERSION} available.''')
      except importlib_metadata.PackageNotFoundError:
            pass
else:
      logger.info("""Disabling Apache Beam because USE_BEAM is set to False""")


# Optional tools for data loading
_lowerCamelCase         =	importlib.util.find_spec("""sqlalchemy""") is not None

# Optional tools for feature decoding
_lowerCamelCase         =	importlib.util.find_spec("""PIL""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.0.31""")
_lowerCamelCase         =	importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.1.0""")

# Optional compression tools
_lowerCamelCase         =	importlib.util.find_spec("""rarfile""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""zstandard""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""lz4""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""py7zr""") is not None


# Cache location
_lowerCamelCase         =	"""~/.cache"""
_lowerCamelCase         =	os.getenv("""XDG_CACHE_HOME""", DEFAULT_XDG_CACHE_HOME)
_lowerCamelCase         =	os.path.join(XDG_CACHE_HOME, """huggingface""")
_lowerCamelCase         =	os.path.expanduser(os.getenv("""HF_HOME""", DEFAULT_HF_CACHE_HOME))

_lowerCamelCase         =	os.path.join(HF_CACHE_HOME, """datasets""")
_lowerCamelCase         =	Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

_lowerCamelCase         =	os.path.join(HF_CACHE_HOME, """metrics""")
_lowerCamelCase         =	Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

_lowerCamelCase         =	os.path.join(HF_CACHE_HOME, """modules""")
_lowerCamelCase         =	Path(os.getenv("""HF_MODULES_CACHE""", DEFAULT_HF_MODULES_CACHE))

_lowerCamelCase         =	"""downloads"""
_lowerCamelCase         =	os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_lowerCamelCase         =	Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

_lowerCamelCase         =	"""extracted"""
_lowerCamelCase         =	os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
_lowerCamelCase         =	Path(os.getenv("""HF_DATASETS_EXTRACTED_DATASETS_PATH""", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
_lowerCamelCase         =	(
    os.environ.get("""HF_UPDATE_DOWNLOAD_COUNTS""", """AUTO""").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
_lowerCamelCase         =	1000

# Size of the preloaded record batch in `Dataset.__iter__`
_lowerCamelCase         =	10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
_lowerCamelCase         =	4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
_lowerCamelCase         =	"""500MB"""

# Parquet configuration
_lowerCamelCase         =	100
_lowerCamelCase         =	100
_lowerCamelCase         =	100

# Offline mode
_lowerCamelCase         =	os.environ.get("""HF_DATASETS_OFFLINE""", """AUTO""").upper() in ENV_VARS_TRUE_VALUES

# In-memory
_lowerCamelCase         =	0  # Disabled
_lowerCamelCase         =	float(os.environ.get("""HF_DATASETS_IN_MEMORY_MAX_SIZE""", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
_lowerCamelCase         =	"""dataset.arrow"""
_lowerCamelCase         =	"""indices.arrow"""
_lowerCamelCase         =	"""state.json"""
_lowerCamelCase         =	"""dataset_info.json"""
_lowerCamelCase         =	"""dataset_infos.json"""
_lowerCamelCase         =	"""LICENSE"""
_lowerCamelCase         =	"""metric_info.json"""
_lowerCamelCase         =	"""dataset_dict.json"""

_lowerCamelCase         =	"""datasets_modules"""

_lowerCamelCase         =	255

# Streaming
_lowerCamelCase         =	20
_lowerCamelCase         =	5

# Datasets without script
_lowerCamelCase         =	200
_lowerCamelCase         =	10
_lowerCamelCase         =	200

# Progress bars
_lowerCamelCase         =	0.0_5  # 20 progress updates per sec














'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


__A   :							int     	=						logging.get_logger(__name__)






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	['input_values', 'attention_mask']






     def __init__(self     :						Optional[int]					,					lowercase__     :						Tuple=1					,					lowercase__     :						Optional[int]=16000					,					lowercase__     :						Any=128					,					lowercase__     :						Optional[Any]=1024					,					lowercase__     :						int=0.0					,					lowercase__     :						List[str]=True					,					lowercase__     :						Dict=-4.2_67_73_93					,					lowercase__     :						Tuple=4.5_68_99_74					,					lowercase__     :						Optional[int]=False					,					**lowercase__     :						Optional[int]					,					)	->							Tuple:
           super().__init__(feature_size=lowercase__					,					sampling_rate=lowercase__					,					padding_value=lowercase__					,					**lowercase__       )
           __A		 =   num_mel_bins
           __A		 =   max_length
           __A		 =   do_normalize
           __A		 =   mean
           __A		 =   std
           __A		 =   return_attention_mask






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						np.ndarray					,					lowercase__     :						int					,					)	->							np.ndarray:
           __A		 =   torch.from_numpy(lowercase__       ).unsqueeze(0       )
           __A		 =   ta_kaldi.fbank(
               lowercase__					,					htk_compat=lowercase__					,					sample_frequency=self.sampling_rate					,					use_energy=lowercase__					,					window_type="hanning"					,					num_mel_bins=self.num_mel_bins					,					dither=0.0					,					frame_shift=10					,					)

           __A		 =   fbank.shape[0]
           __A		 =   max_length - n_frames

           # pad or truncate, depending on difference
           if difference > 0:
                 __A		 =   torch.nn.ZeroPadad((0, 0, 0, difference)       )
                 __A		 =   pad_module(lowercase__       )
           elif difference < 0:
                 __A		 =   fbank[0:max_length, :]

           __A		 =   fbank.numpy()

           return fbank






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						np.ndarray       )	->							np.ndarray:
           return (input_values - (self.mean)) / (self.std * 2)






     def __call__(self     :						str					,					lowercase__     :						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,					lowercase__     :						Optional[int] = None					,					lowercase__     :						Optional[Union[str, TensorType]] = None					,					**lowercase__     :						List[Any]					,					)	->							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 `raw_speech` 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."       )

           __A		 =   isinstance(lowercase__					,					np.ndarray       ) and len(raw_speech.shape       ) > 1
           if is_batched_numpy and len(raw_speech.shape       ) > 2:
                 raise ValueError(f"Only mono-channel audio is supported for input to {self}"       )
           __A		 =   is_batched_numpy or (
               isinstance(lowercase__					,					(list, tuple)       ) and (isinstance(raw_speech[0]					,					(np.ndarray, tuple, list)       ))
           )

           if is_batched:
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for speech in raw_speech]
           elif not is_batched and not isinstance(lowercase__					,					np.ndarray       ):
                 __A		 =   np.asarray(lowercase__					,					dtype=np.floataa       )
           elif isinstance(lowercase__					,					np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
                 __A		 =   raw_speech.astype(np.floataa       )

           # always return batch
           if not is_batched:
                 __A		 =   [raw_speech]

           # extract fbank features and pad/truncate to max_length
           __A		 =   [self._extract_fbank_features(lowercase__					,					max_length=self.max_length       ) for waveform in raw_speech]

           # convert into BatchFeature
           __A		 =   BatchFeature({"input_values": features}       )

           # make sure list is in array format
           __A		 =   padded_inputs.get("input_values"       )
           if isinstance(input_values[0]					,					lowercase__       ):
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for feature in input_values]

           # normalization
           if self.do_normalize:
                 __A		 =   [self.normalize(lowercase__       ) for feature in input_values]

           if return_tensors is not None:
                 __A		 =   padded_inputs.convert_to_tensors(lowercase__       )

           return padded_inputs


