

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 					__UpperCamelCase					:







	def __init__( self:			str	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     value







	def __str__( self:			Dict   ):

						'''simple docstring'''





						return self.to_string()







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''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 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''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 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE      ):







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						return self._value







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						return self._value




class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE      ):







	def __init__( self:			Optional[Any]	,			__UpperCamelCase:			List[str]   ):

						'''simple docstring'''





						super().__init__(__UpperCamelCase   )

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

						__magic_name__				=     None
						__magic_name__				=     None
						__magic_name__				=     None

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			Optional[int]=None	,			__UpperCamelCase:			Any=None   ):

						'''simple docstring'''





						from IPython.display import Image, display

						display(Image(self.to_string()   )   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





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

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





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

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

											return self._path

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

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

											__magic_name__				=     tempfile.mkdtemp()
											__magic_name__				=     os.path.join(__UpperCamelCase	,			str(uuid.uuida()   ) + '.png'   )

											img.save(self._path   )

											return self._path




class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	def __init__( self:			Any	,			__UpperCamelCase:			List[str]	,			__UpperCamelCase:			List[str]=1_60_00   ):

						'''simple docstring'''





						super().__init__(__UpperCamelCase   )

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

						__magic_name__				=     None
						__magic_name__				=     None

						__magic_name__				=     samplerate

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







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			int=None	,			__UpperCamelCase:			Optional[Any]=None   ):

						'''simple docstring'''





						from IPython.display import Audio, display

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







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





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

						if self._path is not None:
											__magic_name__,						__magic_name__				=     sf.read(self._path   )
											__magic_name__				=     torch.tensor(__UpperCamelCase   )
											return self._tensor







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





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

						if self._tensor is not None:
											__magic_name__				=     tempfile.mkdtemp()
											__magic_name__				=     os.path.join(__UpperCamelCase	,			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        _lowercase  (					*a_					:       int       ,**a_					:       Any					)					->						Optional[int]:




					'''simple docstring'''







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




def        _lowercase  (					a_					:       List[Any]       ,a_					:       Union[str, Any]=None					)					->						int:




					'''simple docstring'''







					if isinstance(a_       ,a_					):
										__magic_name__				=     {}
										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:
																									__magic_name__				=     AGENT_TYPE_MAPPING[output_types[i]](a_					)
																				else:
																									__magic_name__				=     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					):
																														__magic_name__				=     _v(a_					)
																				if k not in decoded_outputs:
																									__magic_name__				=     AgentType[v]

					elif isinstance(a_       ,(list, tuple)					):
										__magic_name__				=     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
																				__magic_name__				=     False
																				for _k, _v in INSTANCE_TYPE_MAPPING.items():
																									if isinstance(a_       ,_k					):
																														decoded_outputs.append(_v(a_					)					)
																														__magic_name__				=     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
															__magic_name__				=     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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
    "nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
    "nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
    "nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
    "nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Dict												=	"ernie"







	def __init__( self:			List[Any]	,			__UpperCamelCase:			Dict=3_05_22	,			__UpperCamelCase:			str=7_68	,			__UpperCamelCase:			Tuple=12	,			__UpperCamelCase:			int=12	,			__UpperCamelCase:			Optional[Any]=30_72	,			__UpperCamelCase:			str="gelu"	,			__UpperCamelCase:			Any=0.1	,			__UpperCamelCase:			int=0.1	,			__UpperCamelCase:			int=5_12	,			__UpperCamelCase:			Any=2	,			__UpperCamelCase:			Tuple=3	,			__UpperCamelCase:			Any=False	,			__UpperCamelCase:			Optional[int]=0.02	,			__UpperCamelCase:			List[Any]=1E-12	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Tuple="absolute"	,			__UpperCamelCase:			Union[str, Any]=True	,			__UpperCamelCase:			Optional[Any]=None	,			**__UpperCamelCase:			List[Any]	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     hidden_act
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     type_vocab_size
						__magic_name__				=     task_type_vocab_size
						__magic_name__				=     use_task_id
						__magic_name__				=     initializer_range
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     position_embedding_type
						__magic_name__				=     use_cache
						__magic_name__				=     classifier_dropout




class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__magic_name__				=     {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__magic_name__				=     {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						        ('task_type_ids', dynamic_axis),
						    ]   )




