"""simple docstring"""



import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


_UpperCamelCase								=		get_logger(__name__)


_UpperCamelCase								=		Path(__file__).parent / "model_card_template.md"
_UpperCamelCase								=		uuida().hex
_UpperCamelCase								=		os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
_UpperCamelCase								=		os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
_UpperCamelCase								=		HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"

def  _A(		lowerCAmelCase = None							):
							A__       :     List[Any]								=			F'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
							if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
														return ua + "; telemetry/off"
							if is_torch_available():
														ua += F'''; torch/{_torch_version}'''
							if is_flax_available():
														ua += F'''; jax/{_jax_version}'''
														ua += F'''; flax/{_flax_version}'''
							if is_onnx_available():
														ua += F'''; onnxruntime/{_onnxruntime_version}'''
							# CI will set this value to True
							if os.environ.get("""DIFFUSERS_IS_CI"""			,  """"""							).upper() in ENV_VARS_TRUE_VALUES:
														ua += "; is_ci/true"
							if isinstance(lowerCAmelCase			,  lowerCAmelCase							):
														ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items()							)
							elif isinstance(lowerCAmelCase			,  lowerCAmelCase							):
														ua += "; " + user_agent
							return ua

def  _A(		lowerCAmelCase			,  lowerCAmelCase = None			,  lowerCAmelCase = None							):
							if token is None:
														A__       :     Optional[Any]								=			HfFolder.get_token()
							if organization is None:
														A__       :     str								=			whoami(lowerCAmelCase							)["""name"""]
														return F'''{username}/{model_id}'''
							else:
														return F'''{organization}/{model_id}'''

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							if not is_jinja_available():
														raise ValueError(
														    """Modelcard rendering is based on Jinja templates."""
														    """ Please make sure to have `jinja` installed before using `create_model_card`."""
														    """ To install it, please run `pip install Jinja2`."""							)

							if hasattr(lowerCAmelCase			,  """local_rank"""							) and args.local_rank not in [-1, 0]:
														return

							A__       :     int								=			args.hub_token if hasattr(lowerCAmelCase			,  """hub_token"""							) else None
							A__       :     Any								=			get_full_repo_name(lowerCAmelCase			,  token=lowerCAmelCase							)

							A__       :     Tuple								=			ModelCard.from_template(
							    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
							        language="""en"""			,  license="""apache-2.0"""			,  library_name="""diffusers"""			,  tags=[]			,  datasets=args.dataset_name			,  metrics=[]			,  )			,  template_path=lowerCAmelCase			,  model_name=lowerCAmelCase			,  repo_name=lowerCAmelCase			,  dataset_name=args.dataset_name if hasattr(lowerCAmelCase			,  """dataset_name"""							) else None			,  learning_rate=args.learning_rate			,  train_batch_size=args.train_batch_size			,  eval_batch_size=args.eval_batch_size			,  gradient_accumulation_steps=(
							        args.gradient_accumulation_steps if hasattr(lowerCAmelCase			,  """gradient_accumulation_steps"""							) else None
							    )			,  adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase			,  """adam_beta1"""							) else None			,  adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase			,  """adam_beta2"""							) else None			,  adam_weight_decay=args.adam_weight_decay if hasattr(lowerCAmelCase			,  """adam_weight_decay"""							) else None			,  adam_epsilon=args.adam_epsilon if hasattr(lowerCAmelCase			,  """adam_epsilon"""							) else None			,  lr_scheduler=args.lr_scheduler if hasattr(lowerCAmelCase			,  """lr_scheduler"""							) else None			,  lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCAmelCase			,  """lr_warmup_steps"""							) else None			,  ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCAmelCase			,  """ema_inv_gamma"""							) else None			,  ema_power=args.ema_power if hasattr(lowerCAmelCase			,  """ema_power"""							) else None			,  ema_max_decay=args.ema_max_decay if hasattr(lowerCAmelCase			,  """ema_max_decay"""							) else None			,  mixed_precision=args.mixed_precision			,  )

							A__       :     Optional[int]								=			os.path.join(args.output_dir			,  """README.md"""							)
							model_card.save(lowerCAmelCase							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase = None							):
							if resolved_file is None or commit_hash is not None:
														return commit_hash
							A__       :     Dict								=			str(Path(lowerCAmelCase							).as_posix()							)
							A__       :     str								=			re.search(r"""snapshots/([^/]+)/"""			,  lowerCAmelCase							)
							if search is None:
														return None
							A__       :     Tuple								=			search.groups()[0]
							return commit_hash if REGEX_COMMIT_HASH.match(lowerCAmelCase							) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_UpperCamelCase								=		os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_UpperCamelCase								=		os.path.join(hf_cache_home, "diffusers")

def  _A(		lowerCAmelCase = None			,  lowerCAmelCase = None							):
							if new_cache_dir is None:
														A__       :     Optional[int]								=			DIFFUSERS_CACHE
							if old_cache_dir is None:
														A__       :     Optional[int]								=			old_diffusers_cache

							A__       :     Optional[int]								=			Path(lowerCAmelCase							).expanduser()
							A__       :     Any								=			Path(lowerCAmelCase							).expanduser()
							for old_blob_path in old_cache_dir.glob("""**/blobs/*"""							):
														if old_blob_path.is_file() and not old_blob_path.is_symlink():
																					A__       :     Optional[Any]								=			new_cache_dir / old_blob_path.relative_to(lowerCAmelCase							)
																					new_blob_path.parent.mkdir(parents=lowerCAmelCase			,  exist_ok=lowerCAmelCase							)
																					os.replace(lowerCAmelCase			,  lowerCAmelCase							)
																					try:
																												os.symlink(lowerCAmelCase			,  lowerCAmelCase							)
																					except OSError:
																												logger.warning(
																												    """Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."""							)
							# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


_UpperCamelCase								=		os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
				_UpperCamelCase								=		0
else:
				with open(cache_version_file) as f:
								try:
												_UpperCamelCase								=		int(f.read())
								except ValueError:
												_UpperCamelCase								=		0

if cache_version < 1:
				_UpperCamelCase								=		os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
				if old_cache_is_not_empty:
								logger.warning(
								    "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
								    "existing cached models. This is a one-time operation, you can interrupt it or run it "
								    "later by calling `diffusers.utils.hub_utils.move_cache()`."
								)
								try:
												move_cache()
								except Exception as e:
												_UpperCamelCase								=		"\n".join(traceback.format_tb(e.__traceback__))
												logger.error(
												    F'There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '
												    "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
												    "message and we will do our best to help."
												)

if cache_version < 1:
				try:
								os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
								with open(cache_version_file, "w") as f:
												f.write("1")
				except Exception:
								logger.warning(
								    F'There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '
								    "the directory exists and can be written to."
								)

def  _A(		lowerCAmelCase			,  lowerCAmelCase = None							):
							if variant is not None:
														A__       :     Optional[int]								=			weights_name.split("""."""							)
														A__       :     List[str]								=			splits[:-1] + [variant] + splits[-1:]
														A__       :     Union[str, Any]								=			""".""".join(lowerCAmelCase							)

							return weights_name



def  _A(		lowerCAmelCase			,  *,
    lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=None			,  ):
							A__       :     int								=			str(lowerCAmelCase							)
							if os.path.isfile(lowerCAmelCase							):
														return pretrained_model_name_or_path
							elif os.path.isdir(lowerCAmelCase							):
														if os.path.isfile(os.path.join(lowerCAmelCase			,  lowerCAmelCase							)							):
																					# Load from a PyTorch checkpoint
																					A__       :     int								=			os.path.join(lowerCAmelCase			,  lowerCAmelCase							)
																					return model_file
														elif subfolder is not None and os.path.isfile(
														    os.path.join(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)							):
																					A__       :     List[Any]								=			os.path.join(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
																					return model_file
														else:
																					raise EnvironmentError(
																					    F'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'''							)
							else:
														# 1. First check if deprecated way of loading from branches is used
														if (
														    revision in DEPRECATED_REVISION_ARGS
														    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
														    and version.parse(version.parse(lowerCAmelCase							).base_version							) >= version.parse("""0.20.0"""							)
														):
																					try:
																												A__       :     Any								=			hf_hub_download(
																												    lowerCAmelCase			,  filename=_add_variant(lowerCAmelCase			,  lowerCAmelCase							)			,  cache_dir=lowerCAmelCase			,  force_download=lowerCAmelCase			,  proxies=lowerCAmelCase			,  resume_download=lowerCAmelCase			,  local_files_only=lowerCAmelCase			,  use_auth_token=lowerCAmelCase			,  user_agent=lowerCAmelCase			,  subfolder=lowerCAmelCase			,  revision=revision or commit_hash			,  )
																												warnings.warn(
																												    F'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'''			,  lowerCAmelCase			,  )
																												return model_file
																					except:  # noqa: E722
																												warnings.warn(
																												    F'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowerCAmelCase			,  lowerCAmelCase							)} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(lowerCAmelCase			,  lowerCAmelCase							)}\' so that the correct variant file can be added.'''			,  lowerCAmelCase			,  )
														try:
																					# 2. Load model file as usual
																					A__       :     int								=			hf_hub_download(
																					    lowerCAmelCase			,  filename=lowerCAmelCase			,  cache_dir=lowerCAmelCase			,  force_download=lowerCAmelCase			,  proxies=lowerCAmelCase			,  resume_download=lowerCAmelCase			,  local_files_only=lowerCAmelCase			,  use_auth_token=lowerCAmelCase			,  user_agent=lowerCAmelCase			,  subfolder=lowerCAmelCase			,  revision=revision or commit_hash			,  )
																					return model_file

														except RepositoryNotFoundError:
																					raise EnvironmentError(
																					    F'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
																					    """listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a """
																					    """token having permission to this repo with `use_auth_token` or log in with `huggingface-cli """
																					    """login`."""							)
														except RevisionNotFoundError:
																					raise EnvironmentError(
																					    F'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
																					    """this model name. Check the model page at """
																					    F'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'''							)
														except EntryNotFoundError:
																					raise EnvironmentError(
																					    F'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'''							)
														except HTTPError as err:
																					raise EnvironmentError(
																					    F'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'''							)
														except ValueError:
																					raise EnvironmentError(
																					    F'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
																					    F''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
																					    F''' directory containing a file named {weights_name} or'''
																					    """ \nCheckout your internet connection or see how to run the library in"""
																					    """ offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."""							)
														except EnvironmentError:
																					raise EnvironmentError(
																					    F'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
																					    """'https://huggingface.co/models', make sure you don't have a local directory with the same name. """
																					    F'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
																					    F'''containing a file named {weights_name}'''							)






"""simple docstring"""



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_UpperCamelCase								=		logging.get_logger(__name__)
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[int]					     =				['input_features', 'is_longer']





			def __init__(			self   ,      snake_case_=64   ,      snake_case_=48_000   ,      snake_case_=480   ,      snake_case_=10   ,      snake_case_=1_024   ,      snake_case_=0.0   ,      snake_case_=False   ,      snake_case_ = 0   ,      snake_case_ = 14_000   ,      snake_case_ = None   ,      snake_case_ = "fusion"   ,      snake_case_ = "repeatpad"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    feature_size=snake_case_   ,      sampling_rate=snake_case_   ,      padding_value=snake_case_   ,      return_attention_mask=snake_case_   ,      **snake_case_   ,      )
										A__       :     List[str]								=			top_db
										A__       :     Optional[int]								=			truncation
										A__       :     str								=			padding
										A__       :     str								=			fft_window_size
										A__       :     Any								=			(fft_window_size >> 1) + 1
										A__       :     Optional[Any]								=			hop_length
										A__       :     Any								=			max_length_s
										A__       :     int								=			max_length_s * sampling_rate
										A__       :     Any								=			sampling_rate
										A__       :     List[str]								=			frequency_min
										A__       :     int								=			frequency_max
										A__       :     Tuple								=			mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,      num_mel_filters=snake_case_   ,      min_frequency=snake_case_   ,      max_frequency=snake_case_   ,      sampling_rate=snake_case_   ,      norm=snake_case_   ,      mel_scale="""htk"""   ,      )
										A__       :     List[Any]								=			mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,      num_mel_filters=snake_case_   ,      min_frequency=snake_case_   ,      max_frequency=snake_case_   ,      sampling_rate=snake_case_   ,      norm="""slaney"""   ,      mel_scale="""slaney"""   ,      )





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			copy.deepcopy(self.__dict__							)
										A__       :     int								=			self.__class__.__name__
										if "mel_filters" in output:
																	del output["mel_filters"]
										if "mel_filters_slaney" in output:
																	del output["mel_filters_slaney"]
										return output





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     Any								=			spectrogram(
										    snake_case_   ,      window_function(self.fft_window_size   ,      """hann"""							)   ,      frame_length=self.fft_window_size   ,      hop_length=self.hop_length   ,      power=2.0   ,      mel_filters=snake_case_   ,      log_mel="""dB"""   ,      )
										return log_mel_spectrogram.T





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			np.array_split(list(range(0   ,      total_frames - chunk_frames + 1							)							)   ,      3							)
										if len(ranges[1]							) == 0:
																	# if the audio is too short, we just use the first chunk
																	A__       :     Optional[Any]								=			[0]
										if len(ranges[2]							) == 0:
																	# if the audio is too short, we just use the first chunk
																	A__       :     str								=			[0]
										# randomly choose index for each part
										A__       :     int								=			np.random.choice(ranges[0]							)
										A__       :     Dict								=			np.random.choice(ranges[1]							)
										A__       :     Dict								=			np.random.choice(ranges[2]							)

										A__       :     Any								=			mel[idx_front : idx_front + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_middle : idx_middle + chunk_frames, :]
										A__       :     Optional[int]								=			mel[idx_back : idx_back + chunk_frames, :]

										A__       :     Dict								=			torch.tensor(mel[None, None, :]							)
										A__       :     Dict								=			torch.nn.functional.interpolate(
										    snake_case_   ,      size=[chunk_frames, 64]   ,      mode="""bilinear"""   ,      align_corners=snake_case_							)
										A__       :     List[Any]								=			mel_shrink[0][0].numpy()
										A__       :     Dict								=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,      axis=0							)
										return mel_fusion





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if waveform.shape[0] > max_length:
																	if truncation == "rand_trunc":
																								A__       :     int								=			True
																								# random crop to max_length (for compatibility) -> this should be handled by self.pad
																								A__       :     Tuple								=			len(snake_case_							) - max_length
																								A__       :     Any								=			np.random.randint(0   ,      overflow + 1							)
																								A__       :     int								=			waveform[idx : idx + max_length]
																								A__       :     List[str]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters_slaney							)[None, :]
																	elif truncation == "fusion":
																								A__       :     Optional[int]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters							)
																								A__       :     Optional[int]								=			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																								A__       :     int								=			mel.shape[0]
																								if chunk_frames == total_frames:
																															# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																															# In this case, we just use the whole audio.
																															A__       :     Union[str, Any]								=			np.stack([mel, mel, mel, mel]   ,      axis=0							)
																															A__       :     Dict								=			False
																								else:
																															A__       :     List[Any]								=			self._random_mel_fusion(snake_case_   ,      snake_case_   ,      snake_case_							)
																															A__       :     Optional[Any]								=			True
																	else:
																								raise NotImplementedError(F'''data_truncating {truncation} not implemented'''							)

										else:
																	A__       :     Tuple								=			False
																	# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																	if waveform.shape[0] < max_length:
																								if padding == "repeat":
																															A__       :     Union[str, Any]								=			int(max_length / len(snake_case_							)							)
																															A__       :     List[Any]								=			np.stack(np.tile(snake_case_   ,      n_repeat + 1							)							)[:max_length]
																								if padding == "repeatpad":
																															A__       :     List[Any]								=			int(max_length / len(snake_case_							)							)
																															A__       :     Tuple								=			np.stack(np.tile(snake_case_   ,      snake_case_							)							)
																								A__       :     Tuple								=			np.pad(snake_case_   ,      (0, max_length - waveform.shape[0])   ,      mode="""constant"""   ,      constant_values=0							)

																	if truncation == "fusion":
																								A__       :     str								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters							)
																								A__       :     List[Any]								=			np.stack([input_mel, input_mel, input_mel, input_mel]   ,      axis=0							)
																	else:
																								A__       :     List[Any]								=			self._np_extract_fbank_features(snake_case_   ,      self.mel_filters_slaney							)[None, :]

										return input_mel, longer





			def __call__(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     Optional[Any]								=			truncation if truncation is not None else self.truncation
										A__       :     Union[str, Any]								=			padding if padding else self.padding

										if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																								    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																								    F''' was sampled with {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__       :     List[Any]								=			isinstance(snake_case_   ,      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__       :     int								=			is_batched_numpy or (
										    isinstance(snake_case_   ,      (list, tuple)							) and (isinstance(raw_speech[0]   ,      (np.ndarray, tuple, list)							))
										)

										if is_batched:
																	A__       :     Any								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for speech in raw_speech]
										elif not is_batched and not isinstance(snake_case_   ,      np.ndarray							):
																	A__       :     Any								=			np.asarray(snake_case_   ,      dtype=np.floataa							)
										elif isinstance(snake_case_   ,      np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
																	A__       :     Optional[int]								=			raw_speech.astype(np.floataa							)

										# always return batch
										if not is_batched:
																	A__       :     Union[str, Any]								=			[np.asarray(snake_case_							)]

										# convert to mel spectrogram, truncate and pad if needed.
										A__       :     Optional[Any]								=			[
										    self._get_input_mel(snake_case_   ,      max_length if max_length else self.nb_max_samples   ,      snake_case_   ,      snake_case_							)
										    for waveform in raw_speech
										]

										A__       :     List[str]								=			[]
										A__       :     Optional[Any]								=			[]
										for mel, longer in padded_inputs:
																	input_mel.append(snake_case_							)
																	is_longer.append(snake_case_							)

										if truncation == "fusion" and sum(snake_case_							) == 0:
																	# if no audio is longer than 10s, then randomly select one audio to be longer
																	A__       :     Optional[Any]								=			np.random.randint(0   ,      len(snake_case_							)							)
																	A__       :     str								=			True

										if isinstance(input_mel[0]   ,      snake_case_							):
																	A__       :     Optional[Any]								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for feature in input_mel]

										# is_longer is a list of bool
										A__       :     Union[str, Any]								=			[[longer] for longer in is_longer]

										A__       :     str								=			{"""input_features""": input_mel, """is_longer""": is_longer}
										A__       :     Optional[int]								=			BatchFeature(snake_case_							)

										if return_tensors is not None:
																	A__       :     Dict								=			input_features.convert_to_tensors(snake_case_							)

										return input_features





