


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,
)


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

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

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


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]       ):






       """simple docstring"""





       if version.parse(SCREAMING_SNAKE_CASE_       ) < version.parse(SCREAMING_SNAKE_CASE_       ):
              if "dev" in min_version:
                     UpperCamelCase    :	Union[str, Any]   	=		(
                         '''This example requires a source install from HuggingFace Transformers (see '''
                         '''`https://huggingface.co/docs/transformers/installation#install-from-source`),'''
                     )
              else:
                     UpperCamelCase    :	str   	=		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.'''       )





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

import numpy as np

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 : Dict 			=				logging.get_logger(__name__)



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Any							=					["input_features"]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=80 ,					__SCREAMING_SNAKE_CASE=16_000 ,					__SCREAMING_SNAKE_CASE=160 ,					__SCREAMING_SNAKE_CASE=30 ,					__SCREAMING_SNAKE_CASE=400 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=False ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(
                  feature_size=__SCREAMING_SNAKE_CASE ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					padding_value=__SCREAMING_SNAKE_CASE ,					return_attention_mask=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	List[str]   	=		n_fft
              UpperCamelCase    :	Dict   	=		hop_length
              UpperCamelCase    :	Dict   	=		chunk_length
              UpperCamelCase    :	List[str]   	=		chunk_length * sampling_rate
              UpperCamelCase    :	Dict   	=		self.n_samples // hop_length
              UpperCamelCase    :	str   	=		sampling_rate
              UpperCamelCase    :	Union[str, Any]   	=		mel_filter_bank(
                  num_frequency_bins=1 + n_fft // 2 ,					num_mel_filters=__SCREAMING_SNAKE_CASE ,					min_frequency=0.0 ,					max_frequency=8_000.0 ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					norm='''slaney''' ,					mel_scale='''slaney''' ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		spectrogram(
                  __SCREAMING_SNAKE_CASE ,					window_function(self.n_fft ,					'''hann'''						) ,					frame_length=self.n_fft ,					hop_length=self.hop_length ,					power=2.0 ,					mel_filters=self.mel_filters ,					log_mel='''log10''' ,					)
              UpperCamelCase    :	int   	=		log_spec[:, :-1]
              UpperCamelCase    :	int   	=		np.maximum(__SCREAMING_SNAKE_CASE ,					log_spec.max() - 8.0						)
              UpperCamelCase    :	Any   	=		(log_spec + 4.0) / 4.0
              return log_spec






       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def  _lowercase   (     __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 0.0						):




              """simple docstring"""




              if attention_mask is not None:
                     UpperCamelCase    :	List[Any]   	=		np.array(__SCREAMING_SNAKE_CASE ,					np.intaa						)
                     UpperCamelCase    :	Optional[Any]   	=		[]

                     for vector, length in zip(__SCREAMING_SNAKE_CASE ,					attention_mask.sum(-1						)						):
                            UpperCamelCase    :	Optional[Any]   	=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
                            if length < normed_slice.shape[0]:
                                   UpperCamelCase    :	Optional[int]   	=		padding_value

                            normed_input_values.append(__SCREAMING_SNAKE_CASE						)
              else:
                     UpperCamelCase    :	Union[str, Any]   	=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

              return normed_input_values






       def __call__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = "max_length" ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              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    :	Tuple   	=		isinstance(__SCREAMING_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}"""						)
              UpperCamelCase    :	Union[str, Any]   	=		is_batched_numpy or (
                  isinstance(__SCREAMING_SNAKE_CASE ,					(list, tuple)						) and (isinstance(raw_speech[0] ,					(np.ndarray, tuple, list)						))
              )

              if is_batched:
                     UpperCamelCase    :	List[Any]   	=		[np.asarray([speech] ,					dtype=np.floataa						).T for speech in raw_speech]
              elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						):
                     UpperCamelCase    :	int   	=		np.asarray(__SCREAMING_SNAKE_CASE ,					dtype=np.floataa						)
              elif isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
                     UpperCamelCase    :	Union[str, Any]   	=		raw_speech.astype(np.floataa						)

              # always return batch
              if not is_batched:
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray([raw_speech]						).T]

              UpperCamelCase    :	Optional[int]   	=		BatchFeature({'''input_features''': raw_speech}						)

              # convert into correct format for padding

              UpperCamelCase    :	Optional[Any]   	=		self.pad(
                  __SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					max_length=max_length if max_length else self.n_samples ,					truncation=__SCREAMING_SNAKE_CASE ,					pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,					return_attention_mask=return_attention_mask or do_normalize ,					)

              # zero-mean and unit-variance normalization
              if do_normalize:
                     UpperCamelCase    :	Optional[Any]   	=		self.zero_mean_unit_var_norm(
                         padded_inputs['''input_features'''] ,					attention_mask=padded_inputs['''attention_mask'''] ,					padding_value=self.padding_value ,					)
                     UpperCamelCase    :	List[str]   	=		np.stack(padded_inputs['''input_features'''] ,					axis=0						)

              # make sure list is in array format
              UpperCamelCase    :	Dict   	=		padded_inputs.get('''input_features'''						).transpose(2 ,					0 ,					1						)

              UpperCamelCase    :	Tuple   	=		[self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE						) for waveform in input_features[0]]

              if isinstance(input_features[0] ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray(__SCREAMING_SNAKE_CASE ,					dtype=np.floataa						) for feature in input_features]
              else:
                     UpperCamelCase    :	Dict   	=		input_features

              if return_attention_mask:
                     # rescale from sample (48000) to feature (3000)
                     UpperCamelCase    :	Union[str, Any]   	=		padded_inputs['''attention_mask'''][:, :: self.hop_length]

              if return_tensors is not None:
                     UpperCamelCase    :	Dict   	=		padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE						)

              return padded_inputs






       def  _lowercase   (     self						):




              """simple docstring"""




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

