



"""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


A    =	logging.get_logger(__name__)

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

if is_torch_available():
			import torch

if is_soundfile_availble():
			import soundfile as sf

class 			_a     :



			def __init__( self       :							Union[str, Any]      ,  _lowercase       :							Optional[Any]		)       ->				Optional[int]:
									snake_case      : int							=   value



			def __str__( self       :							Union[str, Any]		)       ->				Any:
									return self.to_string()



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



			def 							__lowercase				( self       :							Dict		)       ->				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     (   SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__):



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



			def 							__lowercase				( self       :							Union[str, Any]		)       ->				List[str]:
									return self._value



class 			_a     (   SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__):



			def __init__( self       :							Optional[Any]      ,  _lowercase       :							Dict		)       ->				str:
									super().__init__(_lowercase		)

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

									snake_case      : Optional[int]							=   None
									snake_case      : List[str]							=   None
									snake_case      : Dict							=   None

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



			def 							__lowercase				( self       :							List[str]      ,  _lowercase       :							Dict=None      ,  _lowercase       :							int=None		)       ->				List[Any]:
									from IPython.display import Image, display

									display(Image(self.to_string()		)		)



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

									if self._path is not None:
															snake_case      : List[str]							=   Image.open(self._path		)
															return self._raw



			def 							__lowercase				( self       :							Dict		)       ->				List[str]:
									if self._path is not None:
															return self._path

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

															return self._path

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

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

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

															img.save(self._path		)

															return self._path



class 			_a     (   SCREAMING_SNAKE_CASE__):



			def __init__( self       :							Optional[int]      ,  _lowercase       :							Union[str, Any]      ,  _lowercase       :							Optional[Any]=16000		)       ->				Any:
									super().__init__(_lowercase		)

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

									snake_case      : Optional[Any]							=   None
									snake_case      : Dict							=   None

									snake_case      : Union[str, Any]							=   samplerate

									if isinstance(_lowercase      ,  (str, pathlib.Path)		):
															snake_case      : List[Any]							=   value
									elif isinstance(_lowercase      ,  torch.Tensor		):
															snake_case      : Union[str, Any]							=   value
									else:
															raise ValueError(F'''Unsupported audio type: {type(_lowercase		)}'''		)



			def 							__lowercase				( self       :							Optional[int]      ,  _lowercase       :							Optional[int]=None      ,  _lowercase       :							List[str]=None		)       ->				str:
									from IPython.display import Audio, display

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



			def 							__lowercase				( self       :							str		)       ->				Tuple:
									if self._tensor is not None:
															return self._tensor

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



			def 							__lowercase				( self       :							List[str]		)       ->				Optional[Any]:
									if self._path is not None:
															return self._path

									if self._tensor is not None:
															snake_case      : List[Any]							=   tempfile.mkdtemp()
															snake_case      : Any							=   os.path.join(_lowercase      ,  str(uuid.uuida()		) + ".wav"		)
															sf.write(self._path      ,  self._tensor      ,  samplerate=self.samplerate		)
															return self._path



A    =	{'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
A    =	{str: AgentText}

if is_vision_available():
			A    =	AgentImage

def    __SCREAMING_SNAKE_CASE		( *lowerCamelCase_:       Optional[Any]    ,		**lowerCamelCase_:       List[str]   ):
						"""simple docstring"""






						snake_case      : List[Any]							=   [(arg.to_raw() if isinstance(lowerCamelCase_    ,		lowerCamelCase_   ) else arg) for arg in args]
						snake_case      : Dict							=   {k: (v.to_raw() if isinstance(lowerCamelCase_    ,		lowerCamelCase_   ) else v) for k, v in kwargs.items()}
						return args, kwargs

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Union[str, Any]    ,		lowerCamelCase_:       Optional[int]=None   ):
						"""simple docstring"""






						if isinstance(lowerCamelCase_    ,		lowerCamelCase_   ):
												snake_case      : Tuple							=   {}
												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      : Tuple							=   AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase_   )
																								else:
																														snake_case      : str							=   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   ):
																																				snake_case      : Any							=   _v(lowerCamelCase_   )
																								if k not in decoded_outputs:
																														snake_case      : Any							=   AgentType[v]

						elif isinstance(lowerCamelCase_    ,		(list, tuple)   ):
												snake_case      : Union[str, Any]							=   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
																								snake_case      : int							=   False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(lowerCamelCase_    ,		_k   ):
																																				decoded_outputs.append(_v(lowerCamelCase_   )   )
																																				snake_case      : Optional[Any]							=   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
																		snake_case      : 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"""







from typing import TYPE_CHECKING

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


A    =	{'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'GPTJForCausalLM',
			    'GPTJForQuestionAnswering',
			    'GPTJForSequenceClassification',
			    'GPTJModel',
			    'GPTJPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TFGPTJForCausalLM',
			    'TFGPTJForQuestionAnswering',
			    'TFGPTJForSequenceClassification',
			    'TFGPTJModel',
			    'TFGPTJPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'FlaxGPTJForCausalLM',
			    'FlaxGPTJModel',
			    'FlaxGPTJPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gptj import (
						    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTJForCausalLM,
						    GPTJForQuestionAnswering,
						    GPTJForSequenceClassification,
						    GPTJModel,
						    GPTJPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_gptj import (
						    TFGPTJForCausalLM,
						    TFGPTJForQuestionAnswering,
						    TFGPTJForSequenceClassification,
						    TFGPTJModel,
						    TFGPTJPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
			import sys

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



