


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


A :       Tuple						=   logging.get_logger(__name__)

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

if is_torch_available():
    import torch

if is_soundfile_availble():
    import soundfile as sf







class      _lowercase					:


       """simple docstring"""




       def __init__(  self				:     Optional[Any]   , __lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    value





       def __str__(  self				:     List[str]			):


             '''simple docstring'''



             return self.to_string()





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             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				:     str			):


             '''simple docstring'''



             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					(				lowercase__				,    lowercase__):


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             return self._value





       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             return self._value







class      _lowercase					(				lowercase__				,    lowercase__):


       """simple docstring"""




       def __init__(  self				:     Optional[int]   , __lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             super().__init__(__lowerCamelCase			)

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

             lowerCamelCase__						:							List[str]       =    None
             lowerCamelCase__						:							List[str]       =    None
             lowerCamelCase__						:							Tuple       =    None

             if isinstance(__lowerCamelCase   , __lowerCamelCase			):
                   lowerCamelCase__						:							str       =    value
             elif isinstance(__lowerCamelCase   , (str, pathlib.Path)			):
                   lowerCamelCase__						:							Dict       =    value
             elif isinstance(__lowerCamelCase   , torch.Tensor			):
                   lowerCamelCase__						:							Union[str, Any]       =    value
             else:
                   raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(__lowerCamelCase			)}"			)





       def 		lowerCAmelCase	(  self				:     Optional[int]   , __lowerCamelCase				:     List[str]=None   , __lowerCamelCase				:     List[str]=None			):


             '''simple docstring'''



             from IPython.display import Image, display

             display(Image(self.to_string()			)			)





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             if self._raw is not None:
                   return self._raw

             if self._path is not None:
                   lowerCamelCase__						:							Dict       =    Image.open(self._path			)
                   return self._raw





       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             if self._path is not None:
                   return self._path

             if self._raw is not None:
                   lowerCamelCase__						:							Optional[int]       =    tempfile.mkdtemp()
                   lowerCamelCase__						:							str       =    os.path.join(__lowerCamelCase   , str(uuid.uuida()			) + ".png"			)
                   self._raw.save(self._path			)

                   return self._path

             if self._tensor is not None:
                   lowerCamelCase__						:							int       =    self._tensor.cpu().detach().numpy()

                   # There is likely simpler than load into image into save
                   lowerCamelCase__						:							List[Any]       =    Image.fromarray((array * 255).astype(np.uinta			)			)

                   lowerCamelCase__						:							Optional[Any]       =    tempfile.mkdtemp()
                   lowerCamelCase__						:							str       =    os.path.join(__lowerCamelCase   , str(uuid.uuida()			) + ".png"			)

                   img.save(self._path			)

                   return self._path







class      _lowercase					(				lowercase__):


       """simple docstring"""




       def __init__(  self				:     Dict   , __lowerCamelCase				:     int   , __lowerCamelCase				:     int=16000			):


             '''simple docstring'''



             super().__init__(__lowerCamelCase			)

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

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

             lowerCamelCase__						:							str       =    samplerate

             if isinstance(__lowerCamelCase   , (str, pathlib.Path)			):
                   lowerCamelCase__						:							List[str]       =    value
             elif isinstance(__lowerCamelCase   , torch.Tensor			):
                   lowerCamelCase__						:							Union[str, Any]       =    value
             else:
                   raise ValueError(f"Unsupported audio type: {type(__lowerCamelCase			)}"			)





       def 		lowerCAmelCase	(  self				:     List[str]   , __lowerCamelCase				:     Union[str, Any]=None   , __lowerCamelCase				:     Optional[Any]=None			):


             '''simple docstring'''



             from IPython.display import Audio, display

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





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             if self._tensor is not None:
                   return self._tensor

             if self._path is not None:
                   lowerCamelCase__       ,  lowerCamelCase__						:							Dict       =    sf.read(self._path			)
                   lowerCamelCase__						:							int       =    torch.tensor(__lowerCamelCase			)
                   return self._tensor





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             if self._path is not None:
                   return self._path

             if self._tensor is not None:
                   lowerCamelCase__						:							Optional[int]       =    tempfile.mkdtemp()
                   lowerCamelCase__						:							Optional[int]       =    os.path.join(__lowerCamelCase   , str(uuid.uuida()			) + ".wav"			)
                   sf.write(self._path   , self._tensor   , samplerate=self.samplerate			)
                   return self._path



A :       Optional[int]						=   {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
A :       List[str]						=   {str: AgentText}

if is_vision_available():
    A :       Tuple						=   AgentImage

def  lowercase_						(						*_A     :       List[str]       ,   **_A     :       Tuple		):

      """simple docstring"""




      lowerCamelCase__						:							Tuple       =    [(arg.to_raw() if isinstance(_A       ,   _A		) else arg) for arg in args]
      lowerCamelCase__						:							List[str]       =    {k: (v.to_raw() if isinstance(_A       ,   _A		) else v) for k, v in kwargs.items()}
      return args, kwargs


def  lowercase_						(						_A     :       Any       ,   _A     :       Optional[int]=None		):

      """simple docstring"""




      if isinstance(_A       ,   _A		):
            lowerCamelCase__						:							List[str]       =    {}
            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]](_A		)
                        else:
                              lowerCamelCase__						:							Optional[int]       =    AgentType(_A		)

                  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(_A       ,   _k		):
                                    lowerCamelCase__						:							Dict       =    _v(_A		)
                        if k not in decoded_outputs:
                              lowerCamelCase__						:							List[str]       =    AgentType[v]

      elif isinstance(_A       ,   (list, tuple)		):
            lowerCamelCase__						:							Any       =    type(_A		)()
            for i, v in enumerate(_A		):
                  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]](_A		)		)
                        else:
                              decoded_outputs.append(AgentType(_A		)		)
                  else:
                        # If the class does not have defined output, then we map according to the type
                        lowerCamelCase__						:							List[str]       =    False
                        for _k, _v in INSTANCE_TYPE_MAPPING.items():
                              if isinstance(_A       ,   _k		):
                                    decoded_outputs.append(_v(_A		)		)
                                    lowerCamelCase__						:							str       =    True

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

      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__						:							List[str]       =    AGENT_TYPE_MAPPING[output_types[0]](_A		)

            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(_A       ,   _k		):
                              return _v(_A		)
                  return AgentType(_A		)

      return decoded_outputs






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A :       Optional[int]						=   {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[Any]						=   ["MPNetTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[str]						=   [
        "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MPNetForMaskedLM",
        "MPNetForMultipleChoice",
        "MPNetForQuestionAnswering",
        "MPNetForSequenceClassification",
        "MPNetForTokenClassification",
        "MPNetLayer",
        "MPNetModel",
        "MPNetPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       Tuple						=   [
        "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMPNetEmbeddings",
        "TFMPNetForMaskedLM",
        "TFMPNetForMultipleChoice",
        "TFMPNetForQuestionAnswering",
        "TFMPNetForSequenceClassification",
        "TFMPNetForTokenClassification",
        "TFMPNetMainLayer",
        "TFMPNetModel",
        "TFMPNetPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    A :       Union[str, Any]						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


