


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	:							Any           =  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	:							List[Any]           =  object

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf


class        __lowerCamelCase      :


				"""simple docstring"""







				def __init__(				self     :    Tuple	,							lowerCamelCase_     :    Optional[Any]					):
								_lowerCAmelCase  						=value






				def __str__(				self     :    List[str]					):
								return self.to_string()






				def        lowerCAmelCase__			(				self     :    int					):
								logger.error(
								    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""					)
								return self._value






				def        lowerCAmelCase__			(				self     :    Optional[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        __lowerCamelCase      (				lowerCamelCase_				,		lowerCamelCase_ ):


				"""simple docstring"""







				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								return self._value






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


class        __lowerCamelCase      (				lowerCamelCase_				,		lowerCamelCase_ ):


				"""simple docstring"""







				def __init__(				self     :    List[Any]	,							lowerCamelCase_     :    str					):
								super().__init__(lowerCamelCase_					)

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

								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=None

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






				def        lowerCAmelCase__			(				self     :    Dict	,							lowerCamelCase_     :    str=None	,							lowerCamelCase_     :    Optional[Any]=None					):
								from IPython.display import Image, display

								display(Image(self.to_string()					)					)






				def        lowerCAmelCase__			(				self     :    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        lowerCAmelCase__			(				self     :    Optional[Any]					):
								if self._path is not None:
												return self._path

								if self._raw is not None:
												_lowerCAmelCase  						=tempfile.mkdtemp()
												_lowerCAmelCase  						=os.path.join(lowerCamelCase_	,							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 * 255).astype(np.uinta					)					)

												_lowerCAmelCase  						=tempfile.mkdtemp()
												_lowerCAmelCase  						=os.path.join(lowerCamelCase_	,							str(uuid.uuida()					) + """.png"""					)

												img.save(self._path					)

												return self._path


class        __lowerCamelCase      (				lowerCamelCase_ ):


				"""simple docstring"""







				def __init__(				self     :    str	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    List[Any]=1_6000					):
								super().__init__(lowerCamelCase_					)

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

								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=None

								_lowerCAmelCase  						=samplerate

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






				def        lowerCAmelCase__			(				self     :    str	,							lowerCamelCase_     :    str=None	,							lowerCamelCase_     :    Dict=None					):
								from IPython.display import Audio, display

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






				def        lowerCAmelCase__			(				self     :    Optional[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(lowerCamelCase_					)
												return self._tensor






				def        lowerCAmelCase__			(				self     :    List[str]					):
								if self._path is not None:
												return self._path

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

if is_vision_available():
		__SCREAMING_SNAKE_CASE	:							Tuple           =  AgentImage

def 		snake_case_   (				*lowercase__     :    Any       ,     **lowercase__     :    Tuple     ):






				'''simple docstring'''






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





def 		snake_case_   (				lowercase__     :    Union[str, Any]       ,     lowercase__     :    List[str]=None     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								_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]](lowercase__     )
																else:
																				_lowerCAmelCase  						=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     ):
																								_lowerCAmelCase  						=_v(lowercase__     )
																if k not in decoded_outputs:
																				_lowerCAmelCase  						=AgentType[v]

				elif isinstance(lowercase__       ,     (list, tuple)     ):
								_lowerCAmelCase  						=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
																_lowerCAmelCase  						=False
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(lowercase__       ,     _k     ):
																								decoded_outputs.append(_v(lowercase__     )     )
																								_lowerCAmelCase  						=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
												_lowerCAmelCase  						=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 typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							str           =  ['''PegasusTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  ['''PegasusTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PegasusForCausalLM''',
		    '''PegasusForConditionalGeneration''',
		    '''PegasusModel''',
		    '''PegasusPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''TFPegasusForConditionalGeneration''',
		    '''TFPegasusModel''',
		    '''TFPegasusPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''FlaxPegasusForConditionalGeneration''',
		    '''FlaxPegasusModel''',
		    '''FlaxPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Dict           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

