
'''simple docstring'''

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


lowerCAmelCase_			:			Union[str, Any]			   =     logging.get_logger(__name__)







class 	__lowerCAmelCase    (  __a				):
     snake_case   :    List[Any]							      =      ["""input_values""", """attention_mask"""]



     def __init__(self						,	lowerCAmelCase__=1						,	lowerCAmelCase__=1_6_0_0_0						,	lowerCAmelCase__=0.0						,	lowerCAmelCase__=False						,	lowerCAmelCase__=True						,	**lowerCAmelCase__						,	):
          super().__init__(feature_size=lowerCAmelCase__						,	sampling_rate=lowerCAmelCase__						,	padding_value=lowerCAmelCase__						,	**lowerCAmelCase__	)
          _UpperCAmelCase					:  List[Any]         =    return_attention_mask
          _UpperCAmelCase					:  List[Any]         =    do_normalize



     @staticmethod
     def     snake_case_ (lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = 0.0	):
          if attention_mask is not None:
               _UpperCAmelCase					:  int         =    np.array(lowerCAmelCase__						,	np.intaa	)
               _UpperCAmelCase					:  List[Any]         =    []

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

                    normed_input_values.append(lowerCAmelCase__	)
          else:
               _UpperCAmelCase					:  Optional[int]         =    [(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

          return normed_input_values



     def __call__(self						,	lowerCAmelCase__						,	lowerCAmelCase__ = False						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = False						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__						,	):

          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."""	)

          _UpperCAmelCase					:  List[str]         =    isinstance(lowerCAmelCase__						,	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					:  List[str]         =    is_batched_numpy or (
              isinstance(lowerCAmelCase__						,	(list, tuple)	) and (isinstance(raw_speech[0]						,	(np.ndarray, tuple, list)	))
          )

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

          # convert into correct format for padding
          _UpperCAmelCase					:  Optional[Any]         =    BatchFeature({"""input_values""": raw_speech}	)

          _UpperCAmelCase					:  Dict         =    self.pad(
              lowerCAmelCase__						,	padding=lowerCAmelCase__						,	max_length=lowerCAmelCase__						,	truncation=lowerCAmelCase__						,	pad_to_multiple_of=lowerCAmelCase__						,	return_attention_mask=lowerCAmelCase__						,	)

          # convert input values to correct format
          _UpperCAmelCase					:  Tuple         =    padded_inputs["""input_values"""]
          if not isinstance(input_values[0]						,	np.ndarray	):
               _UpperCAmelCase					:  Tuple         =    [np.asarray(lowerCAmelCase__						,	dtype=np.floataa	) for array in input_values]
          elif (
              not isinstance(lowerCAmelCase__						,	np.ndarray	)
              and isinstance(input_values[0]						,	np.ndarray	)
              and input_values[0].dtype is np.dtype(np.floataa	)
          ):
               _UpperCAmelCase					:  Tuple         =    [array.astype(np.floataa	) for array in input_values]
          elif isinstance(lowerCAmelCase__						,	np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
               _UpperCAmelCase					:  Any         =    input_values.astype(np.floataa	)

          # convert attention_mask to correct format
          _UpperCAmelCase					:  Any         =    padded_inputs.get("""attention_mask"""	)
          if attention_mask is not None:
               _UpperCAmelCase					:  int         =    [np.asarray(lowerCAmelCase__						,	dtype=np.intaa	) for array in attention_mask]

          # zero-mean and unit-variance normalization
          if self.do_normalize:
               _UpperCAmelCase					:  Any         =    (
                   attention_mask
                   if self._get_padding_strategies(lowerCAmelCase__						,	max_length=lowerCAmelCase__	) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               _UpperCAmelCase					:  int         =    self.zero_mean_unit_var_norm(
                   padded_inputs["""input_values"""]						,	attention_mask=lowerCAmelCase__						,	padding_value=self.padding_value	)

          if return_tensors is not None:
               _UpperCAmelCase					:  Optional[int]         =    padded_inputs.convert_to_tensors(lowerCAmelCase__	)

          return padded_inputs






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
   from .testing_utils import (
       floats_tensor,
       load_hf_numpy,
       load_image,
       load_numpy,
       load_pt,
       nightly,
       parse_flag_from_env,
       print_tensor_test,
       require_torch_a,
       require_torch_gpu,
       skip_mps,
       slow,
       torch_all_close,
       torch_device,
   )
   from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase			=    get_logger(__name__)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Tuple:




      """simple docstring"""

      if version.parse(SCREAMING_SNAKE_CASE					) < version.parse(SCREAMING_SNAKE_CASE					):
            if "dev" in min_version:
                  snake_case_       =      (
                      '''This example requires a source install from HuggingFace diffusers (see '''
                      '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                  )
            else:
                  snake_case_       =      f'''This example requires a minimum version of {min_version},'''
            error_message += f''' but the version found is {__version__}.\n'''
            raise ImportError(SCREAMING_SNAKE_CASE					)