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       :

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				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 _a       (	lowerCAmelCase__   ,							lowerCAmelCase__ ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			return self._value







	def 					__UpperCAmelCase(       self   ):
			return self._value


class _a       (	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."   )

			__A							:   Tuple	  =				None
			__A							:   Union[str, Any]	  =				None
			__A							:   Optional[int]	  =				None

			if isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ):
					__A							:   List[str]	  =				value
			elif isinstance(__UpperCAmelCase							,			(str, pathlib.Path)   ):
					__A							:   List[str]	  =				value
			elif isinstance(__UpperCAmelCase							,			torch.Tensor   ):
					__A							:   int	  =				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:
					__A							:   List[str]	  =				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:
					__A							:   int	  =				tempfile.mkdtemp()
					__A							:   List[str]	  =				os.path.join(__UpperCAmelCase							,			str(uuid.uuida()   ) + ".png"   )
					self._raw.save(self._path   )

					return self._path

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

					# There is likely simpler than load into image into save
					__A							:   Tuple	  =				Image.fromarray((array * 255).astype(np.uinta   )   )

					__A							:   Tuple	  =				tempfile.mkdtemp()
					__A							:   Union[str, Any]	  =				os.path.join(__UpperCAmelCase							,			str(uuid.uuida()   ) + ".png"   )

					img.save(self._path   )

					return self._path


class _a       (	lowerCAmelCase__ ):

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase=16_000   ):
			super().__init__(__UpperCAmelCase   )

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

			__A							:   Optional[int]	  =				None
			__A							:   str	  =				None

			__A							:   Any	  =				samplerate

			if isinstance(__UpperCAmelCase							,			(str, pathlib.Path)   ):
					__A							:   Any	  =				value
			elif isinstance(__UpperCAmelCase							,			torch.Tensor   ):
					__A							:   str	  =				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:
					__A							:   Tuple	  =				sf.read(self._path   )
					__A							:   Union[str, Any]	  =				torch.tensor(__UpperCAmelCase   )
					return self._tensor







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

			if self._tensor is not None:
					__A							:   int	  =				tempfile.mkdtemp()
					__A							:   List[str]	  =				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_   (  *_lowercase ,     **_lowercase    )							->	Union[str, Any]:
		__A							:   Union[str, Any]	  =				[(arg.to_raw() if isinstance(_lowercase ,     _lowercase    ) else arg) for arg in args]
		__A							:   Union[str, Any]	  =				{k: (v.to_raw() if isinstance(_lowercase ,     _lowercase    ) else v) for k, v in kwargs.items()}
		return args, kwargs





def    lowerCamelCase_   (  _lowercase ,     _lowercase=None    )							->	Optional[Any]:
		if isinstance(_lowercase ,     _lowercase    ):
				__A							:   Dict	  =				{}
				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:
										__A							:   Union[str, Any]	  =				AGENT_TYPE_MAPPING[output_types[i]](_lowercase    )
								else:
										__A							:   Union[str, Any]	  =				AgentType(_lowercase    )

						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(_lowercase ,     _k    ):
												__A							:   List[str]	  =				_v(_lowercase    )
								if k not in decoded_outputs:
										__A							:   str	  =				AgentType[v]

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

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

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

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

		return decoded_outputs






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .image_encoder import PaintByExampleImageEncoder
					from .pipeline_paint_by_example import PaintByExamplePipeline





