

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__ :     Any				    =    logging.get_logger(__name__)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[str]			       =["""input_features"""]


  def __init__(			self    : str				,				UpperCAmelCase__    : Tuple=8_0				,				UpperCAmelCase__    : Tuple=1_6_0_0_0				,				UpperCAmelCase__    : List[str]=1_6_0				,				UpperCAmelCase__    : List[Any]=3_0				,				UpperCAmelCase__    : str=4_0_0				,				UpperCAmelCase__    : Any=0.0				,				UpperCAmelCase__    : Optional[int]=False				,				**UpperCAmelCase__    : List[Any]				,				)					->Any:
        """simple docstring"""







        super().__init__(
            feature_size=UpperCAmelCase__				,				sampling_rate=UpperCAmelCase__				,				padding_value=UpperCAmelCase__				,				return_attention_mask=UpperCAmelCase__				,				**UpperCAmelCase__				,				)
        SCREAMING_SNAKE_CASE :      List[str]       =   n_fft
        SCREAMING_SNAKE_CASE :      Optional[int]       =   hop_length
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   chunk_length
        SCREAMING_SNAKE_CASE :      str       =   chunk_length * sampling_rate
        SCREAMING_SNAKE_CASE :      List[str]       =   self.n_samples // hop_length
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   sampling_rate
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2				,				num_mel_filters=UpperCAmelCase__				,				min_frequency=0.0				,				max_frequency=80_00.0				,				sampling_rate=UpperCAmelCase__				,				norm="""slaney"""				,				mel_scale="""slaney"""				,				)


  def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : np.array		)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   spectrogram(
            UpperCAmelCase__				,				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"""				,				)
        SCREAMING_SNAKE_CASE :      str       =   log_spec[:, :-1]
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.maximum(UpperCAmelCase__				,				log_spec.max() - 8.0		)
        SCREAMING_SNAKE_CASE :      List[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      (			UpperCAmelCase__    : List[np.ndarray]				,				UpperCAmelCase__    : List[np.ndarray]				,				UpperCAmelCase__    : float = 0.0		)					->List[np.ndarray]:
        """simple docstring"""







        if attention_mask is not None:
              SCREAMING_SNAKE_CASE :      List[str]       =   np.array(UpperCAmelCase__				,				np.intaa		)
              SCREAMING_SNAKE_CASE :      Dict       =   []

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

                    normed_input_values.append(UpperCAmelCase__		)
        else:
              SCREAMING_SNAKE_CASE :      str       =   [(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

        return normed_input_values


  def __call__(			self    : Optional[int]				,				UpperCAmelCase__    : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[Union[str, TensorType]] = None				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Optional[str] = "max_length"				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[bool] = None				,				**UpperCAmelCase__    : Dict				,				)					->BatchFeature:
        """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."""		)

        SCREAMING_SNAKE_CASE :      List[Any]       =   isinstance(UpperCAmelCase__				,				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}"		)
        SCREAMING_SNAKE_CASE :      Tuple       =   is_batched_numpy or (
            isinstance(UpperCAmelCase__				,				(list, tuple)		) and (isinstance(raw_speech[0]				,				(np.ndarray, tuple, list)		))
        )

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

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

        SCREAMING_SNAKE_CASE :      List[str]       =   BatchFeature({"""input_features""": raw_speech}		)

        # convert into correct format for padding

        SCREAMING_SNAKE_CASE :      Tuple       =   self.pad(
            UpperCAmelCase__				,				padding=UpperCAmelCase__				,				max_length=max_length if max_length else self.n_samples				,				truncation=UpperCAmelCase__				,				pad_to_multiple_of=UpperCAmelCase__				,				return_attention_mask=return_attention_mask or do_normalize				,				)

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

        # make sure list is in array format
        SCREAMING_SNAKE_CASE :      str       =   padded_inputs.get("""input_features"""		).transpose(2				,				0				,				1		)

        SCREAMING_SNAKE_CASE :      str       =   [self._np_extract_fbank_features(UpperCAmelCase__		) for waveform in input_features[0]]

        if isinstance(input_features[0]				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      int       =   [np.asarray(UpperCAmelCase__				,				dtype=np.floataa		) for feature in input_features]
        else:
              SCREAMING_SNAKE_CASE :      List[Any]       =   input_features

        if return_attention_mask:
              # rescale from sample (48000) to feature (3000)
              SCREAMING_SNAKE_CASE :      List[str]       =   padded_inputs["""attention_mask"""][:, :: self.hop_length]

        if return_tensors is not None:
              SCREAMING_SNAKE_CASE :      Tuple       =   padded_inputs.convert_to_tensors(UpperCAmelCase__		)

        return padded_inputs




  def  _lowercase      (			self    : List[str]		)					->Dict[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   copy.deepcopy(self.__dict__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.__class__.__name__
        if "mel_filters" in output:
              del output["mel_filters"]
        return output







import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


UpperCAmelCase__ :     List[str]				    =    (
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


UpperCAmelCase__ :     Union[str, Any]				    =    logging.get_logger(__name__)  # pylint: disable=invalid-name






def 					__lowercase      (     )     ->						List[Any]:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   """https://pypi.org/pypi/diffusers/json"""
      SCREAMING_SNAKE_CASE :      Dict       =   json.loads(request.urlopen(_A			).read()			)["""releases"""].keys()
      return sorted(_A						,    key=lambda _A			: version.Version(_A			)			)






def 					__lowercase      (     )     ->						Any:
      # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
      if HF_MODULES_CACHE in sys.path:
            return

      sys.path.append(_A			)
      os.makedirs(_A						,    exist_ok=_A			)
      SCREAMING_SNAKE_CASE :      Dict       =   Path(_A			) / """__init__.py"""
      if not init_path.exists():
            init_path.touch()






def 					__lowercase      (     _A			)     ->						Any:
      init_hf_modules()
      SCREAMING_SNAKE_CASE :      Any       =   Path(_A			) / name
      # If the parent module does not exist yet, recursively create it.
      if not dynamic_module_path.parent.exists():
            create_dynamic_module(dynamic_module_path.parent			)
      os.makedirs(_A						,    exist_ok=_A			)
      SCREAMING_SNAKE_CASE :      str       =   dynamic_module_path / """__init__.py"""
      if not init_path.exists():
            init_path.touch()






def 					__lowercase      (     _A			)     ->						Optional[int]:
      with open(_A						,    """r"""						,    encoding="""utf-8"""			) as f:
            SCREAMING_SNAKE_CASE :      Any       =   f.read()

      # Imports of the form `import .xxx`
      SCREAMING_SNAKE_CASE :      List[str]       =   re.findall("""^\s*import\s+\.(\S+)\s*$"""						,    _A						,    flags=re.MULTILINE			)
      # Imports of the form `from .xxx import yyy`
      relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import"""						,    _A						,    flags=re.MULTILINE			)
      # Unique-ify
      return list(set(_A			)			)






def 					__lowercase      (     _A			)     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   False
      SCREAMING_SNAKE_CASE :      List[Any]       =   [module_file]
      SCREAMING_SNAKE_CASE :      Any       =   []

      # Let's recurse through all relative imports
      while not no_change:
            SCREAMING_SNAKE_CASE :      List[str]       =   []
            for f in files_to_check:
                  new_imports.extend(get_relative_imports(_A			)			)

            SCREAMING_SNAKE_CASE :      List[str]       =   Path(_A			).parent
            SCREAMING_SNAKE_CASE :      int       =   [str(module_path / m			) for m in new_imports]
            SCREAMING_SNAKE_CASE :      int       =   [f for f in new_import_files if f not in all_relative_imports]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   [F"{f}.py" for f in new_import_files]

            SCREAMING_SNAKE_CASE :      List[Any]       =   len(_A			) == 0
            all_relative_imports.extend(_A			)

      return all_relative_imports






def 					__lowercase      (     _A			)     ->						Union[str, Any]:
      with open(_A						,    """r"""						,    encoding="""utf-8"""			) as f:
            SCREAMING_SNAKE_CASE :      List[Any]       =   f.read()

      # Imports of the form `import xxx`
      SCREAMING_SNAKE_CASE :      Any       =   re.findall("""^\s*import\s+(\S+)\s*$"""						,    _A						,    flags=re.MULTILINE			)
      # Imports of the form `from xxx import yyy`
      imports += re.findall("""^\s*from\s+(\S+)\s+import"""						,    _A						,    flags=re.MULTILINE			)
      # Only keep the top-level module
      SCREAMING_SNAKE_CASE :      List[Any]       =   [imp.split("""."""			)[0] for imp in imports if not imp.startswith("""."""			)]

      # Unique-ify and test we got them all
      SCREAMING_SNAKE_CASE :      Dict       =   list(set(_A			)			)
      SCREAMING_SNAKE_CASE :      Dict       =   []
      for imp in imports:
            try:
                  importlib.import_module(_A			)
            except ImportError:
                  missing_packages.append(_A			)

      if len(_A			) > 0:
            raise ImportError(
                """This modeling file requires the following packages that were not found in your environment: """
                F"{', '.join(_A			)}. Run `pip install {' '.join(_A			)}`"			)

      return get_relative_imports(_A			)






def 					__lowercase      (     _A						,    _A			)     ->						List[str]:
      SCREAMING_SNAKE_CASE :      int       =   module_path.replace(os.path.sep						,    """."""			)
      SCREAMING_SNAKE_CASE :      List[str]       =   importlib.import_module(_A			)

      if class_name is None:
            return find_pipeline_class(_A			)
      return getattr(_A						,    _A			)






def 					__lowercase      (     _A			)     ->						Optional[int]:
      from ..pipelines import DiffusionPipeline

      SCREAMING_SNAKE_CASE :      Tuple       =   dict(inspect.getmembers(_A						,    inspect.isclass			)			)

      SCREAMING_SNAKE_CASE :      Dict       =   None
      for cls_name, cls in cls_members.items():
            if (
                cls_name != DiffusionPipeline.__name__
                and issubclass(cls						,    _A			)
                and cls.__module__.split("""."""			)[0] != "diffusers"
            ):
                  if pipeline_class is not None:
                        raise ValueError(
                            F"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"
                            F" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"
                            F" {loaded_module}."			)
                  SCREAMING_SNAKE_CASE :      List[str]       =   cls

      return pipeline_class






def 					__lowercase      (     _A						,    _A						,    _A = None						,    _A = False						,    _A = False						,    _A = None						,    _A = None						,    _A = None						,    _A = False						,    )     ->						List[str]:
      SCREAMING_SNAKE_CASE :      List[str]       =   str(_A			)

      SCREAMING_SNAKE_CASE :      List[Any]       =   os.path.join(_A						,    _A			)

      if os.path.isfile(_A			):
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   module_file_or_url
            SCREAMING_SNAKE_CASE :      Dict       =   """local"""
      elif pretrained_model_name_or_path.count("""/"""			) == 0:
            SCREAMING_SNAKE_CASE :      Tuple       =   get_diffusers_versions()
            # cut ".dev0"
            SCREAMING_SNAKE_CASE :      str       =   """v""" + """.""".join(__version__.split("""."""			)[:3]			)

            # retrieve github version that matches
            if revision is None:
                  SCREAMING_SNAKE_CASE :      Any       =   latest_version if latest_version[1:] in available_versions else """main"""
                  logger.info(F"Defaulting to latest_version: {revision}."			)
            elif revision in available_versions:
                  SCREAMING_SNAKE_CASE :      List[Any]       =   F"v{revision}"
            elif revision == "main":
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   revision
            else:
                  raise ValueError(
                      F"`custom_revision`: {revision} does not exist. Please make sure to choose one of"
                      F" {', '.join(available_versions + ['main']			)}."			)

            # community pipeline on GitHub
            SCREAMING_SNAKE_CASE :      Tuple       =   COMMUNITY_PIPELINES_URL.format(revision=_A						,    pipeline=_A			)
            try:
                  SCREAMING_SNAKE_CASE :      str       =   cached_download(
                      _A						,    cache_dir=_A						,    force_download=_A						,    proxies=_A						,    resume_download=_A						,    local_files_only=_A						,    use_auth_token=_A						,    )
                  SCREAMING_SNAKE_CASE :      Tuple       =   """git"""
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   pretrained_model_name_or_path + """.py"""
            except EnvironmentError:
                  logger.error(F"Could not locate the {module_file} inside {pretrained_model_name_or_path}."			)
                  raise
      else:
            try:
                  # Load from URL or cache if already cached
                  SCREAMING_SNAKE_CASE :      int       =   hf_hub_download(
                      _A						,    _A						,    cache_dir=_A						,    force_download=_A						,    proxies=_A						,    resume_download=_A						,    local_files_only=_A						,    use_auth_token=_A						,    )
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   os.path.join("""local"""						,    """--""".join(pretrained_model_name_or_path.split("""/"""			)			)			)
            except EnvironmentError:
                  logger.error(F"Could not locate the {module_file} inside {pretrained_model_name_or_path}."			)
                  raise

    # Check we have all the requirements in our environment
      SCREAMING_SNAKE_CASE :      int       =   check_imports(_A			)

      # Now we move the module inside our cached dynamic modules.
      SCREAMING_SNAKE_CASE :      List[Any]       =   DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
      create_dynamic_module(_A			)
      SCREAMING_SNAKE_CASE :      Tuple       =   Path(_A			) / full_submodule
      if submodule == "local" or submodule == "git":
            # We always copy local files (we could hash the file to see if there was a change, and give them the name of
            # that hash, to only copy when there is a modification but it seems overkill for now).
            # The only reason we do the copy is to avoid putting too many folders in sys.path.
            shutil.copy(_A						,    submodule_path / module_file			)
            for module_needed in modules_needed:
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   F"{module_needed}.py"
                  shutil.copy(os.path.join(_A						,    _A			)						,    submodule_path / module_needed			)
      else:
            # Get the commit hash
            # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
            if isinstance(_A						,    _A			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   use_auth_token
            elif use_auth_token is True:
                  SCREAMING_SNAKE_CASE :      str       =   HfFolder.get_token()
            else:
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   None

            SCREAMING_SNAKE_CASE :      List[Any]       =   model_info(_A						,    revision=_A						,    token=_A			).sha

            # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
            # benefit of versioning.
            SCREAMING_SNAKE_CASE :      int       =   submodule_path / commit_hash
            SCREAMING_SNAKE_CASE :      Tuple       =   full_submodule + os.path.sep + commit_hash
            create_dynamic_module(_A			)

            if not (submodule_path / module_file).exists():
                  shutil.copy(_A						,    submodule_path / module_file			)
            # Make sure we also have every file with relative
            for module_needed in modules_needed:
                  if not (submodule_path / module_needed).exists():
                        get_cached_module_file(
                            _A						,    F"{module_needed}.py"						,    cache_dir=_A						,    force_download=_A						,    resume_download=_A						,    proxies=_A						,    use_auth_token=_A						,    revision=_A						,    local_files_only=_A						,    )
      return os.path.join(_A						,    _A			)







def 					__lowercase      (     _A						,    _A						,    _A = None						,    _A = None						,    _A = False						,    _A = False						,    _A = None						,    _A = None						,    _A = None						,    _A = False						,    **_A						,    )     ->						Tuple:
      SCREAMING_SNAKE_CASE :      Dict       =   get_cached_module_file(
          _A						,    _A						,    cache_dir=_A						,    force_download=_A						,    resume_download=_A						,    proxies=_A						,    use_auth_token=_A						,    revision=_A						,    local_files_only=_A						,    )
      return get_class_in_module(_A						,    final_module.replace(""".py"""						,    """"""			)			)




