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						:    Optional[int]     = 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						:    Tuple     = object

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf



class    lowercase_    :



							def __init__(						self      ,				lowercase_				):
													_snake_case						:     Dict				=    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    lowercase_    (     __snake_case						, __snake_case		):



							def        UpperCamelCase       (						self				):
													return self._value



							def        UpperCamelCase       (						self				):
													return self._value




class    lowercase_    (     __snake_case						, __snake_case		):



							def __init__(						self      ,				lowercase_				):
													super().__init__(lowercase_				)

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

													_snake_case						:     Union[str, Any]				=    None
													_snake_case						:     Optional[int]				=    None
													_snake_case						:     Union[str, Any]				=    None

													if isinstance(lowercase_      ,				lowercase_				):
																			_snake_case						:     str				=    value
													elif isinstance(lowercase_      ,				(str, pathlib.Path)				):
																			_snake_case						:     Optional[int]				=    value
													elif isinstance(lowercase_      ,				torch.Tensor				):
																			_snake_case						:     Union[str, Any]				=    value
													else:
																			raise ValueError(f"""Unsupported type for {self.__class__.__name__}: {type(lowercase_				)}"""				)



							def        UpperCamelCase       (						self      ,				lowercase_=None      ,				lowercase_=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:
																			_snake_case						:     Any				=    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:
																			_snake_case						:     Union[str, Any]				=    tempfile.mkdtemp()
																			_snake_case						:     int				=    os.path.join(lowercase_      ,				str(uuid.uuida()				) + ".png"				)
																			self._raw.save(self._path				)

																			return self._path

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

																			# There is likely simpler than load into image into save
																			_snake_case						:     Union[str, Any]				=    Image.fromarray((array * 255).astype(np.uinta				)				)

																			_snake_case						:     Optional[Any]				=    tempfile.mkdtemp()
																			_snake_case						:     Tuple				=    os.path.join(lowercase_      ,				str(uuid.uuida()				) + ".png"				)

																			img.save(self._path				)

																			return self._path




class    lowercase_    (     __snake_case		):



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

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

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

													_snake_case						:     Any				=    samplerate

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



							def        UpperCamelCase       (						self      ,				lowercase_=None      ,				lowercase_=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:
																			_snake_case				,_snake_case						:     List[str]				=    sf.read(self._path				)
																			_snake_case						:     Optional[int]				=    torch.tensor(lowercase_				)
																			return self._tensor



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

													if self._tensor is not None:
																			_snake_case						:     Any				=    tempfile.mkdtemp()
																			_snake_case						:     Optional[Any]				=    os.path.join(lowercase_      ,				str(uuid.uuida()				) + ".wav"				)
																			sf.write(self._path      ,				self._tensor      ,				samplerate=self.samplerate				)
																			return self._path




__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = {str: AgentText}

if is_vision_available():
				__SCREAMING_SNAKE_CASE						:    List[Any]     = AgentImage
def 					snake_case						(*__lowercase					,			**__lowercase						)       ->       List[Any]:

						'''simple docstring'''



						_snake_case						:     str				=    [(arg.to_raw() if isinstance(__lowercase					,			__lowercase						) else arg) for arg in args]
						_snake_case						:     Optional[Any]				=    {k: (v.to_raw() if isinstance(__lowercase					,			__lowercase						) else v) for k, v in kwargs.items()}
						return args, kwargs

def 					snake_case						(__lowercase					,			__lowercase=None						)       ->       Union[str, Any]:

						'''simple docstring'''



						if isinstance(__lowercase					,			__lowercase						):
												_snake_case						:     List[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]](__lowercase						)
																								else:
																														_snake_case						:     List[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						):
																																				_snake_case						:     Optional[int]				=    _v(__lowercase						)
																								if k not in decoded_outputs:
																														_snake_case						:     str				=    AgentType[v]

						elif isinstance(__lowercase					,			(list, tuple)						):
												_snake_case						:     int				=    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
																								_snake_case						:     str				=    False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(__lowercase					,			_k						):
																																				decoded_outputs.append(_v(__lowercase						)						)
																																				_snake_case						:     int				=    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
																		_snake_case						:     str				=    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 collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE						:    str     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = {
    '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    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'ernie'



							def __init__(						self      ,				lowercase_=30_522      ,				lowercase_=768      ,				lowercase_=12      ,				lowercase_=12      ,				lowercase_=3_072      ,				lowercase_="gelu"      ,				lowercase_=0.1      ,				lowercase_=0.1      ,				lowercase_=512      ,				lowercase_=2      ,				lowercase_=3      ,				lowercase_=False      ,				lowercase_=0.02      ,				lowercase_=1e-12      ,				lowercase_=0      ,				lowercase_="absolute"      ,				lowercase_=True      ,				lowercase_=None      ,				**lowercase_      ,				):
													super().__init__(pad_token_id=lowercase_      ,				**lowercase_				)

													_snake_case						:     List[Any]				=    vocab_size
													_snake_case						:     Dict				=    hidden_size
													_snake_case						:     Any				=    num_hidden_layers
													_snake_case						:     List[str]				=    num_attention_heads
													_snake_case						:     int				=    hidden_act
													_snake_case						:     Tuple				=    intermediate_size
													_snake_case						:     Tuple				=    hidden_dropout_prob
													_snake_case						:     Optional[int]				=    attention_probs_dropout_prob
													_snake_case						:     Any				=    max_position_embeddings
													_snake_case						:     List[str]				=    type_vocab_size
													_snake_case						:     List[Any]				=    task_type_vocab_size
													_snake_case						:     Optional[int]				=    use_task_id
													_snake_case						:     Tuple				=    initializer_range
													_snake_case						:     Union[str, Any]				=    layer_norm_eps
													_snake_case						:     Optional[Any]				=    position_embedding_type
													_snake_case						:     int				=    use_cache
													_snake_case						:     Optional[Any]				=    classifier_dropout




class    lowercase_    (     __snake_case		):



							@property
							def        UpperCamelCase       (						self				):
													if self.task == "multiple-choice":
																			_snake_case						:     str				=    {0: "batch", 1: "choice", 2: "sequence"}
													else:
																			_snake_case						:     Optional[int]				=    {0: "batch", 1: "sequence"}
													return OrderedDict(
													    [
													        ("input_ids", dynamic_axis),
													        ("attention_mask", dynamic_axis),
													        ("token_type_ids", dynamic_axis),
													        ("task_type_ids", dynamic_axis),
													    ]				)