
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


lowercase						:      int									= logging.get_logger(__name__)




class 				lowerCamelCase__							(       _a):




	'''simple docstring'''




	_A			=  ['input_features', 'is_longer']







	def __init__(      self	:Tuple  ,     a	:List[Any]=6_4  ,     a	:Any=4_8_0_0_0  ,     a	:Optional[Any]=4_8_0  ,     a	:List[Any]=1_0  ,     a	:Optional[int]=1_0_2_4  ,     a	:Any=0.0  ,     a	:Union[str, Any]=False  ,     a	:int = 0  ,     a	:Optional[Any] = 1_4_0_0_0  ,     a	:Dict = None  ,     a	:List[Any] = "fusion"  ,     a	:str = "repeatpad"  ,     **a	:int  ,     )	->				Dict:
								super().__init__(
								    feature_size=_A  ,     sampling_rate=_A  ,     padding_value=_A  ,     return_attention_mask=_A  ,     **_A  ,     )
								__UpperCamelCase			:     int         =      top_db
								__UpperCamelCase			:     str         =      truncation
								__UpperCamelCase			:     List[str]         =      padding
								__UpperCamelCase			:     int         =      fft_window_size
								__UpperCamelCase			:     str         =      (fft_window_size >> 1) + 1
								__UpperCamelCase			:     List[Any]         =      hop_length
								__UpperCamelCase			:     Optional[Any]         =      max_length_s
								__UpperCamelCase			:     Union[str, Any]         =      max_length_s * sampling_rate
								__UpperCamelCase			:     Optional[int]         =      sampling_rate
								__UpperCamelCase			:     Optional[int]         =      frequency_min
								__UpperCamelCase			:     Union[str, Any]         =      frequency_max
								__UpperCamelCase			:     Dict         =      mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins  ,     num_mel_filters=_A  ,     min_frequency=_A  ,     max_frequency=_A  ,     sampling_rate=_A  ,     norm=_A  ,     mel_scale="htk"  ,     )
								__UpperCamelCase			:     str         =      mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins  ,     num_mel_filters=_A  ,     min_frequency=_A  ,     max_frequency=_A  ,     sampling_rate=_A  ,     norm="slaney"  ,     mel_scale="slaney"  ,     )







	def      _lowerCamelCase       (      self	:str		)	->				int:
								__UpperCamelCase			:     str         =      copy.deepcopy(self.__dict__		)
								__UpperCamelCase			:     Union[str, Any]         =      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	:Union[str, Any]  ,     a	:str  ,     a	:Union[str, Any] = None		)	->				str:
								__UpperCamelCase			:     List[str]         =      spectrogram(
								    _A  ,     window_function(self.fft_window_size  ,     "hann"		)  ,     frame_length=self.fft_window_size  ,     hop_length=self.hop_length  ,     power=2.0  ,     mel_filters=_A  ,     log_mel="dB"  ,     )
								return log_mel_spectrogram.T







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:List[Any]  ,     a	:Tuple  ,     a	:Dict		)	->				str:
								__UpperCamelCase			:     Tuple         =      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
															__UpperCamelCase			:     Dict         =      [0]
								if len(ranges[2]		) == 0:
															# if the audio is too short, we just use the first chunk
															__UpperCamelCase			:     Union[str, Any]         =      [0]
								# randomly choose index for each part
								__UpperCamelCase			:     List[Any]         =      np.random.choice(ranges[0]		)
								__UpperCamelCase			:     List[Any]         =      np.random.choice(ranges[1]		)
								__UpperCamelCase			:     List[Any]         =      np.random.choice(ranges[2]		)

								__UpperCamelCase			:     List[Any]         =      mel[idx_front : idx_front + chunk_frames, :]
								__UpperCamelCase			:     List[str]         =      mel[idx_middle : idx_middle + chunk_frames, :]
								__UpperCamelCase			:     Any         =      mel[idx_back : idx_back + chunk_frames, :]

								__UpperCamelCase			:     List[str]         =      torch.tensor(mel[None, None, :]		)
								__UpperCamelCase			:     Optional[int]         =      torch.nn.functional.interpolate(
								    _A  ,     size=[chunk_frames, 6_4]  ,     mode="bilinear"  ,     align_corners=_A		)
								__UpperCamelCase			:     Tuple         =      mel_shrink[0][0].numpy()
								__UpperCamelCase			:     str         =      np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]  ,     axis=0		)
								return mel_fusion







	def      _lowerCamelCase       (      self	:Tuple  ,     a	:str  ,     a	:Tuple  ,     a	:List[str]  ,     a	:int		)	->				List[str]:
								if waveform.shape[0] > max_length:
															if truncation == "rand_trunc":
																						__UpperCamelCase			:     Tuple         =      True
																						# random crop to max_length (for compatibility) -> this should be handled by self.pad
																						__UpperCamelCase			:     Tuple         =      len(_A		) - max_length
																						__UpperCamelCase			:     Any         =      np.random.randint(0  ,     overflow + 1		)
																						__UpperCamelCase			:     Dict         =      waveform[idx : idx + max_length]
																						__UpperCamelCase			:     List[str]         =      self._np_extract_fbank_features(_A  ,     self.mel_filters_slaney		)[None, :]
															elif truncation == "fusion":
																						__UpperCamelCase			:     Any         =      self._np_extract_fbank_features(_A  ,     self.mel_filters		)
																						__UpperCamelCase			:     List[str]         =      max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																						__UpperCamelCase			:     Union[str, Any]         =      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.
																													__UpperCamelCase			:     Tuple         =      np.stack([mel, mel, mel, mel]  ,     axis=0		)
																													__UpperCamelCase			:     Union[str, Any]         =      False
																						else:
																													__UpperCamelCase			:     Union[str, Any]         =      self._random_mel_fusion(_A  ,     _A  ,     _A		)
																													__UpperCamelCase			:     List[Any]         =      True
															else:
																						raise NotImplementedError(f'data_truncating {truncation} not implemented'		)

								else:
															__UpperCamelCase			:     Optional[int]         =      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":
																													__UpperCamelCase			:     Union[str, Any]         =      int(max_length / len(_A		)		)
																													__UpperCamelCase			:     str         =      np.stack(np.tile(_A  ,     n_repeat + 1		)		)[:max_length]
																						if padding == "repeatpad":
																													__UpperCamelCase			:     Tuple         =      int(max_length / len(_A		)		)
																													__UpperCamelCase			:     List[str]         =      np.stack(np.tile(_A  ,     _A		)		)
																						__UpperCamelCase			:     List[Any]         =      np.pad(_A  ,     (0, max_length - waveform.shape[0])  ,     mode="constant"  ,     constant_values=0		)

															if truncation == "fusion":
																						__UpperCamelCase			:     Optional[int]         =      self._np_extract_fbank_features(_A  ,     self.mel_filters		)
																						__UpperCamelCase			:     Tuple         =      np.stack([input_mel, input_mel, input_mel, input_mel]  ,     axis=0		)
															else:
																						__UpperCamelCase			:     Any         =      self._np_extract_fbank_features(_A  ,     self.mel_filters_slaney		)[None, :]

								return input_mel, longer







	def __call__(      self	:List[Any]  ,     a	:str  ,     a	:List[Any] = None  ,     a	:Tuple = None  ,     a	:Dict = None  ,     a	:Optional[Any] = None  ,     a	:Any = None  ,     **a	:Union[str, Any]  ,     )	->				Union[str, Any]:
								__UpperCamelCase			:     str         =      truncation if truncation is not None else self.truncation
								__UpperCamelCase			:     Optional[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."		)

								__UpperCamelCase			:     Any         =      isinstance(_A  ,     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}'		)
								__UpperCamelCase			:     Dict         =      is_batched_numpy or (
								    isinstance(_A  ,     (list, tuple)		) and (isinstance(raw_speech[0]  ,     (np.ndarray, tuple, list)		))
								)

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

								# always return batch
								if not is_batched:
															__UpperCamelCase			:     Optional[Any]         =      [np.asarray(_A		)]

								# convert to mel spectrogram, truncate and pad if needed.
								__UpperCamelCase			:     Tuple         =      [
								    self._get_input_mel(_A  ,     max_length if max_length else self.nb_max_samples  ,     _A  ,     _A		)
								    for waveform in raw_speech
								]

								__UpperCamelCase			:     Tuple         =      []
								__UpperCamelCase			:     str         =      []
								for mel, longer in padded_inputs:
															input_mel.append(_A		)
															is_longer.append(_A		)

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

								if isinstance(input_mel[0]  ,     _A		):
															__UpperCamelCase			:     Dict         =      [np.asarray(_A  ,     dtype=np.floataa		) for feature in input_mel]

								# is_longer is a list of bool
								__UpperCamelCase			:     List[Any]         =      [[longer] for longer in is_longer]

								__UpperCamelCase			:     Tuple         =      {"input_features": input_mel, "is_longer": is_longer}
								__UpperCamelCase			:     Optional[int]         =      BatchFeature(_A		)

								if return_tensors is not None:
															__UpperCamelCase			:     List[str]         =      input_features.convert_to_tensors(_A		)

								return input_features

# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowercase						:      Any									= 'pytorch_model.bin'
lowercase						:      List[str]									= 'pytorch_model.bin.index.json'
lowercase						:      List[Any]									= 'adapter_config.json'
lowercase						:      str									= 'adapter_model.bin'
lowercase						:      List[str]									= 'adapter_model.safetensors'
lowercase						:      Any									= 'tf_model.h5'
lowercase						:      str									= 'tf_model.h5.index.json'
lowercase						:      Any									= 'model.ckpt'
lowercase						:      Optional[int]									= 'flax_model.msgpack'
lowercase						:      Dict									= 'flax_model.msgpack.index.json'
lowercase						:      Dict									= 'model.safetensors'
lowercase						:      Dict									= 'model.safetensors.index.json'
lowercase						:      Union[str, Any]									= 'config.json'
lowercase						:      Tuple									= 'preprocessor_config.json'
lowercase						:      Tuple									= FEATURE_EXTRACTOR_NAME
lowercase						:      Dict									= 'generation_config.json'
lowercase						:      Dict									= 'modelcard.json'

lowercase						:      Optional[int]									= '▁'
lowercase						:      Any									= SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowercase						:      List[Any]									= [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowercase						:      Tuple									= [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowercase						:      Any									= [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					List[str]:






							'''simple docstring'''





							if version.parse(_lowerCamelCase) < version.parse(_lowerCamelCase):
														if "dev" in min_version:
																					__UpperCamelCase			:     List[str]         =      (
																					    "This example requires a source install from HuggingFace Transformers (see "
																					    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
																					)
														else:
																					__UpperCamelCase			:     Optional[Any]         =      F'This example requires a minimum version of {min_version},'
														error_message += F' but the version found is {__version__}.\n'
														raise ImportError(
														    error_message
														    + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
														    "versions of HuggingFace Transformers.")