


"""simple docstring"""





import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


__UpperCAmelCase    				=      logging.get_logger(__name__)

if is_vision_available():
   import PIL.Image
   from PIL import Image
   from PIL.Image import Image as ImageType
else:
   __UpperCAmelCase    				=      object

if is_torch_available():
   import torch

if is_soundfile_availble():
   import soundfile as sf

class  lowercase_  :







      def __init__(      self :							Union[str, Any]		,  _lowercase :							List[Any]    ):
        lowerCAmelCase__    :    int								=					value







      def __str__(      self :							Any    ):
        return self.to_string()







      def 				_lowerCAmelCase       (      self :							str    ):
        logger.error(
            "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"    )
        return self._value







      def 				_lowerCAmelCase       (      self :							Any    ):
        logger.error(
            "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"    )
        return str(self._value    )

class  lowercase_  ( a_							,							a_						):







      def 				_lowerCAmelCase       (      self :							Optional[Any]    ):
        return self._value







      def 				_lowerCAmelCase       (      self :							Dict    ):
        return self._value

class  lowercase_  ( a_							,							a_						):







      def __init__(      self :							int		,  _lowercase :							Optional[Any]    ):
        super().__init__(_lowercase    )

        if not is_vision_available():
          raise ImportError("PIL must be installed in order to handle images."    )

        lowerCAmelCase__    :    Union[str, Any]								=					None
        lowerCAmelCase__    :    Any								=					None
        lowerCAmelCase__    :    Any								=					None

        if isinstance(_lowercase		,  _lowercase    ):
          lowerCAmelCase__    :    Optional[int]								=					value
        elif isinstance(_lowercase		,  (str, pathlib.Path)    ):
          lowerCAmelCase__    :    List[str]								=					value
        elif isinstance(_lowercase		,  torch.Tensor    ):
          lowerCAmelCase__    :    Optional[Any]								=					value
        else:
          raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(_lowercase    )}"    )







      def 				_lowerCAmelCase       (      self :							Dict		,  _lowercase :							Optional[int]=None		,  _lowercase :							Optional[Any]=None    ):
        from IPython.display import Image, display

        display(Image(self.to_string()    )    )







      def 				_lowerCAmelCase       (      self :							Optional[int]    ):
        if self._raw is not None:
          return self._raw

        if self._path is not None:
          lowerCAmelCase__    :    Optional[Any]								=					Image.open(self._path    )
          return self._raw







      def 				_lowerCAmelCase       (      self :							Optional[Any]    ):
        if self._path is not None:
          return self._path

        if self._raw is not None:
          lowerCAmelCase__    :    Tuple								=					tempfile.mkdtemp()
          lowerCAmelCase__    :    Any								=					os.path.join(_lowercase		,  str(uuid.uuida()    ) + ".png"    )
          self._raw.save(self._path    )

          return self._path

        if self._tensor is not None:
          lowerCAmelCase__    :    str								=					self._tensor.cpu().detach().numpy()

          # There is likely simpler than load into image into save
          lowerCAmelCase__    :    Any								=					Image.fromarray((array * 2_5_5).astype(np.uinta    )    )

          lowerCAmelCase__    :    str								=					tempfile.mkdtemp()
          lowerCAmelCase__    :    List[str]								=					os.path.join(_lowercase		,  str(uuid.uuida()    ) + ".png"    )

          img.save(self._path    )

          return self._path

class  lowercase_  ( a_						):







      def __init__(      self :							Any		,  _lowercase :							List[str]		,  _lowercase :							str=1_6_0_0_0    ):
        super().__init__(_lowercase    )

        if not is_soundfile_availble():
          raise ImportError("soundfile must be installed in order to handle audio."    )

        lowerCAmelCase__    :    List[str]								=					None
        lowerCAmelCase__    :    Any								=					None

        lowerCAmelCase__    :    Tuple								=					samplerate

        if isinstance(_lowercase		,  (str, pathlib.Path)    ):
          lowerCAmelCase__    :    Dict								=					value
        elif isinstance(_lowercase		,  torch.Tensor    ):
          lowerCAmelCase__    :    int								=					value
        else:
          raise ValueError(f"Unsupported audio type: {type(_lowercase    )}"    )







      def 				_lowerCAmelCase       (      self :							Union[str, Any]		,  _lowercase :							List[str]=None		,  _lowercase :							Union[str, Any]=None    ):
        from IPython.display import Audio, display

        display(Audio(self.to_string()		,  rate=self.samplerate    )    )







      def 				_lowerCAmelCase       (      self :							Tuple    ):
        if self._tensor is not None:
          return self._tensor

        if self._path is not None:
          lowerCAmelCase__				,		lowerCAmelCase__    :    Union[str, Any]								=					sf.read(self._path    )
          lowerCAmelCase__    :    str								=					torch.tensor(_lowercase    )
          return self._tensor







      def 				_lowerCAmelCase       (      self :							Dict    ):
        if self._path is not None:
          return self._path

        if self._tensor is not None:
          lowerCAmelCase__    :    List[str]								=					tempfile.mkdtemp()
          lowerCAmelCase__    :    Any								=					os.path.join(_lowercase		,  str(uuid.uuida()    ) + ".wav"    )
          sf.write(self._path		,  self._tensor		,  samplerate=self.samplerate    )
          return self._path
__UpperCAmelCase    				=      {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
__UpperCAmelCase    				=      {str: AgentText}

if is_vision_available():
   __UpperCAmelCase    				=      AgentImage





def     lowercase__	(  *lowerCamelCase     :  Any				,	**lowerCamelCase     :  int				)					->     int:
  lowerCAmelCase__    :    List[str]								=					[(arg.to_raw() if isinstance(lowerCamelCase				,	lowerCamelCase				) else arg) for arg in args]
  lowerCAmelCase__    :    Optional[Any]								=					{k: (v.to_raw() if isinstance(lowerCamelCase				,	lowerCamelCase				) else v) for k, v in kwargs.items()}
  return args, kwargs







def     lowercase__	(  lowerCamelCase     :  Dict				,	lowerCamelCase     :  Dict=None				)					->     Tuple:
  if isinstance(lowerCamelCase				,	lowerCamelCase				):
    lowerCAmelCase__    :    Dict								=					{}
    for i, (k, v) in enumerate(outputs.items()				):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          lowerCAmelCase__    :    str								=					AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase				)
        else:
          lowerCAmelCase__    :    Union[str, Any]								=					AgentType(lowerCamelCase				)

      else:
        # If the class does not have defined output, then we map according to the type
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(lowerCamelCase				,	_k				):
            lowerCAmelCase__    :    Any								=					_v(lowerCamelCase				)
        if k not in decoded_outputs:
          lowerCAmelCase__    :    int								=					AgentType[v]

  elif isinstance(lowerCamelCase				,	(list, tuple)				):
    lowerCAmelCase__    :    Dict								=					type(lowerCamelCase				)()
    for i, v in enumerate(lowerCamelCase				):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase				)				)
        else:
          decoded_outputs.append(AgentType(lowerCamelCase				)				)
      else:
        # If the class does not have defined output, then we map according to the type
        lowerCAmelCase__    :    List[Any]								=					False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(lowerCamelCase				,	_k				):
            decoded_outputs.append(_v(lowerCamelCase				)				)
            lowerCAmelCase__    :    List[str]								=					True

        if not found:
          decoded_outputs.append(AgentType(lowerCamelCase				)				)

  else:
    if output_types[0] in AGENT_TYPE_MAPPING:
      # If the class has defined outputs, we can map directly according to the class definition
      lowerCAmelCase__    :    Optional[Any]								=					AGENT_TYPE_MAPPING[output_types[0]](lowerCamelCase				)

    else:
      # If the class does not have defined output, then we map according to the type
      for _k, _v in INSTANCE_TYPE_MAPPING.items():
        if isinstance(lowerCamelCase				,	_k				):
          return _v(lowerCamelCase				)
      return AgentType(lowerCamelCase				)

  return decoded_outputs




"""simple docstring"""





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase    				=      logging.get_logger(__name__)

__UpperCAmelCase    				=      {
    "facebook/hubert-base-ls960": "https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class  lowercase_  ( a_						):
      __magic_name__     :							List[Any]          =				"""hubert"""







      def __init__(      self :							List[Any]		,  _lowercase :							Optional[int]=3_2		,  _lowercase :							Tuple=7_6_8		,  _lowercase :							List[Any]=1_2		,  _lowercase :							Dict=1_2		,  _lowercase :							Tuple=3_0_7_2		,  _lowercase :							str="gelu"		,  _lowercase :							Optional[int]=0.1		,  _lowercase :							int=0.1		,  _lowercase :							Tuple=0.1		,  _lowercase :							str=True		,  _lowercase :							Optional[Any]=0.0		,  _lowercase :							List[Any]=0.1		,  _lowercase :							Union[str, Any]=0.1		,  _lowercase :							Union[str, Any]=0.02		,  _lowercase :							List[Any]=1e-5		,  _lowercase :							List[Any]="group"		,  _lowercase :							Tuple="gelu"		,  _lowercase :							List[str]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,  _lowercase :							List[Any]=(5, 2, 2, 2, 2, 2, 2)		,  _lowercase :							Tuple=(1_0, 3, 3, 3, 3, 2, 2)		,  _lowercase :							Any=False		,  _lowercase :							str=1_2_8		,  _lowercase :							int=1_6		,  _lowercase :							str=False		,  _lowercase :							Union[str, Any]=True		,  _lowercase :							Optional[Any]=0.05		,  _lowercase :							List[Any]=1_0		,  _lowercase :							Dict=2		,  _lowercase :							Optional[int]=0.0		,  _lowercase :							List[str]=1_0		,  _lowercase :							List[str]=0		,  _lowercase :							Optional[Any]="sum"		,  _lowercase :							int=False		,  _lowercase :							Any=False		,  _lowercase :							Optional[Any]=2_5_6		,  _lowercase :							int=0		,  _lowercase :							Optional[int]=1		,  _lowercase :							List[Any]=2		,  **_lowercase :							Tuple		,  ):
        super().__init__(**_lowercase		,  pad_token_id=_lowercase		,  bos_token_id=_lowercase		,  eos_token_id=_lowercase    )
        lowerCAmelCase__    :    Optional[Any]								=					hidden_size
        lowerCAmelCase__    :    int								=					feat_extract_norm
        lowerCAmelCase__    :    Optional[Any]								=					feat_extract_activation
        lowerCAmelCase__    :    Optional[Any]								=					list(_lowercase    )
        lowerCAmelCase__    :    Union[str, Any]								=					list(_lowercase    )
        lowerCAmelCase__    :    Tuple								=					list(_lowercase    )
        lowerCAmelCase__    :    str								=					conv_bias
        lowerCAmelCase__    :    Optional[Any]								=					num_conv_pos_embeddings
        lowerCAmelCase__    :    List[str]								=					num_conv_pos_embedding_groups
        lowerCAmelCase__    :    List[Any]								=					len(self.conv_dim    )
        lowerCAmelCase__    :    Union[str, Any]								=					num_hidden_layers
        lowerCAmelCase__    :    str								=					intermediate_size
        lowerCAmelCase__    :    List[Any]								=					hidden_act
        lowerCAmelCase__    :    Optional[int]								=					num_attention_heads
        lowerCAmelCase__    :    Optional[int]								=					hidden_dropout
        lowerCAmelCase__    :    Dict								=					attention_dropout
        lowerCAmelCase__    :    Dict								=					activation_dropout
        lowerCAmelCase__    :    Optional[Any]								=					feat_proj_layer_norm
        lowerCAmelCase__    :    Union[str, Any]								=					feat_proj_dropout
        lowerCAmelCase__    :    Optional[Any]								=					final_dropout
        lowerCAmelCase__    :    Dict								=					layerdrop
        lowerCAmelCase__    :    Optional[int]								=					layer_norm_eps
        lowerCAmelCase__    :    str								=					initializer_range
        lowerCAmelCase__    :    int								=					vocab_size
        lowerCAmelCase__    :    int								=					do_stable_layer_norm
        lowerCAmelCase__    :    Union[str, Any]								=					use_weighted_layer_sum
        lowerCAmelCase__    :    Tuple								=					classifier_proj_size

        if (
            (len(self.conv_stride    ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
            or (len(self.conv_dim    ) != self.num_feat_extract_layers)
        ):
          raise ValueError(
              "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
              " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
              f" {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,"
              f" `len(config.conv_kernel) = {len(self.conv_kernel    )}`."    )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        lowerCAmelCase__    :    int								=					apply_spec_augment
        lowerCAmelCase__    :    Tuple								=					mask_time_prob
        lowerCAmelCase__    :    int								=					mask_time_length
        lowerCAmelCase__    :    Dict								=					mask_time_min_masks
        lowerCAmelCase__    :    Union[str, Any]								=					mask_feature_prob
        lowerCAmelCase__    :    Dict								=					mask_feature_length
        lowerCAmelCase__    :    Tuple								=					mask_feature_min_masks

        # ctc loss
        lowerCAmelCase__    :    Tuple								=					ctc_loss_reduction
        lowerCAmelCase__    :    List[str]								=					ctc_zero_infinity







      @property
      def 				_lowerCAmelCase       (      self :							Dict    ):
        return functools.reduce(operator.mul		,  self.conv_stride		,  1    )
