


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE					:       List[Any]							   =							{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       int							   =							[
			    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """CvtForImageClassification""",
			    """CvtModel""",
			    """CvtPreTrainedModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Tuple							   =							[
			    """TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFCvtForImageClassification""",
			    """TFCvtModel""",
			    """TFCvtPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_cvt import (
						    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CvtForImageClassification,
						    CvtModel,
						    CvtPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_cvt import (
						    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCvtForImageClassification,
						    TFCvtModel,
						    TFCvtPreTrainedModel,
						)


else:
			import sys

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







'''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     a :




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										snake_case							:  Any			=     value




				def __str__(				self						:					Optional[int]   ):
										return self.to_string()




				def 						__snake_case				(				self						:					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 						__snake_case				(				self						:					List[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     a (					__magic_name__					,__magic_name__     ):




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




				def 						__snake_case				(				self						:					str   ):
										return self._value




class     a (					__magic_name__					,__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					str   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

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

										snake_case							:  Union[str, Any]			=     None
										snake_case							:  List[Any]			=     None
										snake_case							:  List[str]			=     None

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




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										from IPython.display import Image, display

										display(Image(self.to_string()   )   )




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._raw is not None:
																return self._raw

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




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										if self._path is not None:
																return self._path

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

																return self._path

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

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

																snake_case							:  List[Any]			=     tempfile.mkdtemp()
																snake_case							:  Optional[Any]			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.png'''   )

																img.save(self._path   )

																return self._path




class     a (					__magic_name__     ):




				def __init__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int]=1_60_00   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

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

										snake_case							:  List[Any]			=     None
										snake_case							:  int			=     None

										snake_case							:  Tuple			=     samplerate

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




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None   ):
										from IPython.display import Audio, display

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




				def 						__snake_case				(				self						:					List[str]   ):
										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							:  Any			=     torch.tensor(SCREAMING_SNAKE_CASE_   )
																return self._tensor




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._path is not None:
																return self._path

										if self._tensor is not None:
																snake_case							:  Optional[Any]			=     tempfile.mkdtemp()
																snake_case							:  List[str]			=     os.path.join(SCREAMING_SNAKE_CASE_,     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   A			(							*A_				:       List[str]     ,				**A_				:       Any							):
						snake_case							:  str			=     [(arg.to_raw() if isinstance(A_     ,				A_							) else arg) for arg in args]
						snake_case							:  Dict			=     {k: (v.to_raw() if isinstance(A_     ,				A_							) else v) for k, v in kwargs.items()}
						return args, kwargs





def   A			(							A_				:       Union[str, Any]     ,				A_				:       Any=None							):
						if isinstance(A_     ,				A_							):
												snake_case							:  Union[str, Any]			=     {}
												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							:  Optional[int]			=     AGENT_TYPE_MAPPING[output_types[i]](A_							)
																								else:
																														snake_case							:  List[str]			=     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							):
																																				snake_case							:  Dict			=     _v(A_							)
																								if k not in decoded_outputs:
																														snake_case							:  List[str]			=     AgentType[v]

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

