


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 lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(	self		,      _UpperCAmelCase					):
       snake_case_       =      value



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



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





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







class lowerCAmelCase_						( lowerCamelCase__					,						lowerCamelCase__       ):

 '''simple docstring'''

 def 						UpperCamelCase__     (	self					):
       return self._value





 def 						UpperCamelCase__     (	self					):
       return self._value







class lowerCAmelCase_						( lowerCamelCase__					,						lowerCamelCase__       ):

 '''simple docstring'''

 def __init__(	self		,      _UpperCAmelCase					):
       super().__init__(_UpperCAmelCase					)

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

       snake_case_       =      None
       snake_case_       =      None
       snake_case_       =      None

       if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					):
             snake_case_       =      value
       elif isinstance(_UpperCAmelCase		,      (str, pathlib.Path)					):
             snake_case_       =      value
       elif isinstance(_UpperCAmelCase		,      torch.Tensor					):
             snake_case_       =      value
       else:
             raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(_UpperCAmelCase					)}'''					)



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None					):
       from IPython.display import Image, display

       display(Image(self.to_string()					)					)



 def 						UpperCamelCase__     (	self					):
       if self._raw is not None:
             return self._raw

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





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

       if self._raw is not None:
             snake_case_       =      tempfile.mkdtemp()
             snake_case_       =      os.path.join(_UpperCAmelCase		,      str(uuid.uuida()					) + '''.png'''					)
             self._raw.save(self._path					)

             return self._path

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

             # There is likely simpler than load into image into save
             snake_case_       =      Image.fromarray((array * 2_55).astype(np.uinta					)					)

             snake_case_       =      tempfile.mkdtemp()
             snake_case_       =      os.path.join(_UpperCAmelCase		,      str(uuid.uuida()					) + '''.png'''					)

             img.save(self._path					)

             return self._path







class lowerCAmelCase_						( lowerCamelCase__       ):

 '''simple docstring'''

 def __init__(	self		,      _UpperCAmelCase		,      _UpperCAmelCase=1_60_00					):
       super().__init__(_UpperCAmelCase					)

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

       snake_case_       =      None
       snake_case_       =      None

       snake_case_       =      samplerate

       if isinstance(_UpperCAmelCase		,      (str, pathlib.Path)					):
             snake_case_       =      value
       elif isinstance(_UpperCAmelCase		,      torch.Tensor					):
             snake_case_       =      value
       else:
             raise ValueError(F'''Unsupported audio type: {type(_UpperCAmelCase					)}'''					)



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None					):
       from IPython.display import Audio, display

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



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

       if self._path is not None:
             snake_case_       ,    snake_case_       =      sf.read(self._path					)
             snake_case_       =      torch.tensor(_UpperCAmelCase					)
             return self._tensor





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

       if self._tensor is not None:
             snake_case_       =      tempfile.mkdtemp()
             snake_case_       =      os.path.join(_UpperCAmelCase		,      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        __lowerCAmelCase    (*SCREAMING_SNAKE_CASE					,					**SCREAMING_SNAKE_CASE					)->  Optional[int]:




      """simple docstring"""

      snake_case_       =      [(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					) else arg) for arg in args]
      snake_case_       =      {k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					) else v) for k, v in kwargs.items()}
      return args, kwargs






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE=None					)->  Tuple:




      """simple docstring"""

      if isinstance(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					):
            snake_case_       =      {}
            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:
                              snake_case_       =      AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE					)
                        else:
                              snake_case_       =      AgentType(SCREAMING_SNAKE_CASE					)

                  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(SCREAMING_SNAKE_CASE					,					_k					):
                                    snake_case_       =      _v(SCREAMING_SNAKE_CASE					)
                        if k not in decoded_outputs:
                              snake_case_       =      AgentType[v]

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

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

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

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

      return decoded_outputs



from typing import TYPE_CHECKING

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


UpperCAmelCase			=    {
    """configuration_big_bird""": ["""BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BigBirdConfig""", """BigBirdOnnxConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    ["""BigBirdTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    ["""BigBirdTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    [
       """BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BigBirdForCausalLM""",
       """BigBirdForMaskedLM""",
       """BigBirdForMultipleChoice""",
       """BigBirdForPreTraining""",
       """BigBirdForQuestionAnswering""",
       """BigBirdForSequenceClassification""",
       """BigBirdForTokenClassification""",
       """BigBirdLayer""",
       """BigBirdModel""",
       """BigBirdPreTrainedModel""",
       """load_tf_weights_in_big_bird""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    [
       """FlaxBigBirdForCausalLM""",
       """FlaxBigBirdForMaskedLM""",
       """FlaxBigBirdForMultipleChoice""",
       """FlaxBigBirdForPreTraining""",
       """FlaxBigBirdForQuestionAnswering""",
       """FlaxBigBirdForSequenceClassification""",
       """FlaxBigBirdForTokenClassification""",
       """FlaxBigBirdModel""",
       """FlaxBigBirdPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_big_bird import BigBirdTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_big_bird_fast import BigBirdTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_big_bird import (
          BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
          BigBirdForCausalLM,
          BigBirdForMaskedLM,
          BigBirdForMultipleChoice,
          BigBirdForPreTraining,
          BigBirdForQuestionAnswering,
          BigBirdForSequenceClassification,
          BigBirdForTokenClassification,
          BigBirdLayer,
          BigBirdModel,
          BigBirdPreTrainedModel,
          load_tf_weights_in_big_bird,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_big_bird import (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
          FlaxBigBirdModel,
          FlaxBigBirdPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)