




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 				__SCREAMING_SNAKE_CASE							:

					def __init__(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							value

					def __str__(				self					):







						'''simple docstring'''




						return self.to_string()

					def 	lowercase_							(				self					):







						'''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 	lowercase_							(				self					):







						'''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 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase  ):

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self._value





					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self._value


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase  ):

					def __init__(				self ,       __lowercase					):







						'''simple docstring'''




						super().__init__(__lowercase					)

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

						lowerCamelCase_							:		List[Any]							   =							None
						lowerCamelCase_							:		Optional[int]							   =							None
						lowerCamelCase_							:		Tuple							   =							None

						if isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Any							   =							value
						elif isinstance(__lowercase ,       (str, pathlib.Path)					):
							lowerCamelCase_							:		int							   =							value
						elif isinstance(__lowercase ,       torch.Tensor					):
							lowerCamelCase_							:		Dict							   =							value
						else:
							raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(__lowercase					)}"					)

					def 	lowercase_							(				self ,       __lowercase=None ,       __lowercase=None					):







						'''simple docstring'''




						from IPython.display import Image, display

						display(Image(self.to_string()					)					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




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

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





					def 	lowercase_							(				self					):







						'''simple docstring'''




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

						if self._raw is not None:
							lowerCamelCase_							:		Union[str, Any]							   =							tempfile.mkdtemp()
							lowerCamelCase_							:		List[Any]							   =							os.path.join(__lowercase ,       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_							:		Any							   =							Image.fromarray((array * 255).astype(np.uinta					)					)

							lowerCamelCase_							:		str							   =							tempfile.mkdtemp()
							lowerCamelCase_							:		Tuple							   =							os.path.join(__lowercase ,       str(uuid.uuida()					) + '.png'					)

							img.save(self._path					)

							return self._path


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase=16_000					):







						'''simple docstring'''




						super().__init__(__lowercase					)

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

						lowerCamelCase_							:		Union[str, Any]							   =							None
						lowerCamelCase_							:		Tuple							   =							None

						lowerCamelCase_							:		Tuple							   =							samplerate

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

					def 	lowercase_							(				self ,       __lowercase=None ,       __lowercase=None					):







						'''simple docstring'''




						from IPython.display import Audio, display

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

					def 	lowercase_							(				self					):







						'''simple docstring'''




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

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





					def 	lowercase_							(				self					):







						'''simple docstring'''




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

						if self._tensor is not None:
							lowerCamelCase_							:		Any							   =							tempfile.mkdtemp()
							lowerCamelCase_							:		List[str]							   =							os.path.join(__lowercase ,       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 					snake_case					(							*A						,   **A			)							->							Dict:




	'''simple docstring'''


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






def 					snake_case					(							A						,   A=None			)							->							str:




	'''simple docstring'''


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

	elif isinstance(A						,   (list, tuple)			):
		lowerCamelCase_							:		List[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[Any]							   =							False
				for _k, _v in INSTANCE_TYPE_MAPPING.items():
					if isinstance(A						,   _k			):
						decoded_outputs.append(_v(A			)			)
						lowerCamelCase_							:		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
			lowerCamelCase_							:		Dict							   =							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



"""simple docstring"""




from typing import TYPE_CHECKING

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


__A     :    int    =		{'configuration_bit': ['BIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BitConfig', 'BitOnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Optional[Any]    =		[
	    'BIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BitForImageClassification',
	    'BitModel',
	    'BitPreTrainedModel',
	    'BitBackbone',
	]


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    List[Any]    =		['BitImageProcessor']


if TYPE_CHECKING:
	from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bit import (
		    BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BitBackbone,
		    BitForImageClassification,
		    BitModel,
		    BitPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bit import BitImageProcessor

else:
	import sys

	__A     :    Union[str, Any]    =		_LazyModule(__name__, globals()['__file__'], _import_structure)





