




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


__SCREAMING_SNAKE_CASE     : Tuple      =logging.get_logger(__name__)

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

if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf






class       A      :
     """simple docstring"""

     def __init__(     self	: Union[str, Any]						,	A__	: str      )					->							Any:
           lowerCamelCase__   	=					value




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




     def 			__snake_case (     self	: List[str]      )					->							Union[str, Any]:
           logger.error(
               'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'      )
           return self._value




     def 			__snake_case (     self	: Optional[Any]      )					->							List[str]:
           logger.error(
               'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'      )
           return str(self._value      )






class       A      (		_lowerCAmelCase      ,    _lowerCAmelCase		):
     """simple docstring"""

     def 			__snake_case (     self	: List[Any]      )					->							str:
           return self._value




     def 			__snake_case (     self	: Optional[int]      )					->							Optional[Any]:
           return self._value






class       A      (		_lowerCAmelCase      ,    _lowerCAmelCase		):
     """simple docstring"""

     def __init__(     self	: Optional[int]						,	A__	: Any      )					->							List[str]:
           super().__init__(A__      )

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

           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					None

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




     def 			__snake_case (     self	: Dict						,	A__	: Any=None						,	A__	: Optional[Any]=None      )					->							Any:
           from IPython.display import Image, display

           display(Image(self.to_string()      )      )




     def 			__snake_case (     self	: Union[str, Any]      )					->							Dict:

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

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




     def 			__snake_case (     self	: Union[str, Any]      )					->							Any:

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

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

                 return self._path

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

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

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

                 img.save(self._path      )

                 return self._path






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     def __init__(     self	: Tuple						,	A__	: Tuple						,	A__	: str=1_6_0_0_0      )					->							Tuple:
           super().__init__(A__      )

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

           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					None

           lowerCamelCase__   	=					samplerate

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




     def 			__snake_case (     self	: Dict						,	A__	: Union[str, Any]=None						,	A__	: Tuple=None      )					->							List[str]:
           from IPython.display import Audio, display

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




     def 			__snake_case (     self	: Any      )					->							Union[str, Any]:

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

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




     def 			__snake_case (     self	: Tuple      )					->							Optional[Any]:

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

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

if is_vision_available():
      __SCREAMING_SNAKE_CASE     : str      =AgentImage

def       lowerCAmelCase_      (	*lowerCamelCase:							Union[str, Any]  ,					**lowerCamelCase:							List[str]    )       ->      Optional[int]:


      '''simple docstring'''




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


def       lowerCAmelCase_      (	lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							List[str]=None    )       ->      Dict:


      '''simple docstring'''




      if isinstance(_UpperCamelCase  ,					_UpperCamelCase    ):
            lowerCamelCase__   	=					{}
            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__   	=					AGENT_TYPE_MAPPING[output_types[i]](_UpperCamelCase    )
                        else:
                              lowerCamelCase__   	=					AgentType(_UpperCamelCase    )

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

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

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

      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__   	=					AGENT_TYPE_MAPPING[output_types[0]](_UpperCamelCase    )

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

      return decoded_outputs
"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

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

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "OpenLlamaForCausalLM",
					    "OpenLlamaModel",
					    "OpenLlamaPreTrainedModel",
					    "OpenLlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

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


