
from collections import OrderedDict
from typing import Mapping

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


__snake_case					:							Dict           =							logging.get_logger(__name__)

__snake_case					:							Optional[Any]           =							{
    'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
}


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    Tuple												=			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
						__a     :    Dict												=			"""mobilebert"""


						def __init__(  self  , A=3_05_22  , A=5_12  , A=24  , A=4  , A=5_12  , A="relu"  , A=0.0  , A=0.1  , A=5_12  , A=2  , A=0.02  , A=1e-12  , A=0  , A=1_28  , A=True  , A=True  , A=1_28  , A=False  , A=True  , A=4  , A="no_norm"  , A=True  , A=None  , **A  , )  ->List[str]:
									super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_  , **SCREAMING_SNAKE_CASE_	)

									UpperCAmelCase__		:str 		=    vocab_size
									UpperCAmelCase__		:Optional[Any] 		=    hidden_size
									UpperCAmelCase__		:Union[str, Any] 		=    num_hidden_layers
									UpperCAmelCase__		:str 		=    num_attention_heads
									UpperCAmelCase__		:Tuple 		=    hidden_act
									UpperCAmelCase__		:Optional[int] 		=    intermediate_size
									UpperCAmelCase__		:str 		=    hidden_dropout_prob
									UpperCAmelCase__		:Any 		=    attention_probs_dropout_prob
									UpperCAmelCase__		:Dict 		=    max_position_embeddings
									UpperCAmelCase__		:List[str] 		=    type_vocab_size
									UpperCAmelCase__		:int 		=    initializer_range
									UpperCAmelCase__		:List[Any] 		=    layer_norm_eps
									UpperCAmelCase__		:Any 		=    embedding_size
									UpperCAmelCase__		:Tuple 		=    trigram_input
									UpperCAmelCase__		:str 		=    use_bottleneck
									UpperCAmelCase__		:Union[str, Any] 		=    intra_bottleneck_size
									UpperCAmelCase__		:Any 		=    use_bottleneck_attention
									UpperCAmelCase__		:Tuple 		=    key_query_shared_bottleneck
									UpperCAmelCase__		:int 		=    num_feedforward_networks
									UpperCAmelCase__		:Union[str, Any] 		=    normalization_type
									UpperCAmelCase__		:Union[str, Any] 		=    classifier_activation

									if self.use_bottleneck:
												UpperCAmelCase__		:Optional[int] 		=    intra_bottleneck_size
									else:
												UpperCAmelCase__		:Optional[int] 		=    hidden_size

									UpperCAmelCase__		:Optional[Any] 		=    classifier_dropout


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						@property
						def    A__       (  self	)  ->Tuple:
									if self.task == "multiple-choice":
												UpperCAmelCase__		:Any 		=    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
												UpperCAmelCase__		:List[str] 		=    {0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									        ('token_type_ids', dynamic_axis),
									    ]	)







'''simple docstring'''





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 :




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										snake_case							:  Any			=     value




				def __str__(				self						:					Optional[int]   ):
										return self.to_string()




				def 						__snake_case				(				self						:					Optional[Any]   ):
										logger.error(
										    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''   )
										return self._value




				def 						__snake_case				(				self						:					List[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     a (					__magic_name__					,__magic_name__     ):




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




				def 						__snake_case				(				self						:					str   ):
										return self._value




class     a (					__magic_name__					,__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					str   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

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

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

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  List[str]			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     (str, pathlib.Path)   ):
																snake_case							:  Any			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  Union[str, Any]			=     value
										else:
																raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_   )}"""   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										from IPython.display import Image, display

										display(Image(self.to_string()   )   )




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._raw is not None:
																return self._raw

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




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										if self._path is not None:
																return self._path

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

																return self._path

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

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

																snake_case							:  List[Any]			=     tempfile.mkdtemp()
																snake_case							:  Optional[Any]			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.png'''   )

																img.save(self._path   )

																return self._path




class     a (					__magic_name__     ):




				def __init__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int]=1_60_00   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

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

										snake_case							:  List[Any]			=     None
										snake_case							:  int			=     None

										snake_case							:  Tuple			=     samplerate

										if isinstance(SCREAMING_SNAKE_CASE_,     (str, pathlib.Path)   ):
																snake_case							:  int			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  List[Any]			=     value
										else:
																raise ValueError(F"""Unsupported audio type: {type(SCREAMING_SNAKE_CASE_   )}"""   )




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None   ):
										from IPython.display import Audio, display

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




				def 						__snake_case				(				self						:					List[str]   ):
										if self._tensor is not None:
																return self._tensor

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




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._path is not None:
																return self._path

										if self._tensor is not None:
																snake_case							:  Optional[Any]			=     tempfile.mkdtemp()
																snake_case							:  List[str]			=     os.path.join(SCREAMING_SNAKE_CASE_,     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   A			(							*A_				:       List[str]     ,				**A_				:       Any							):
						snake_case							:  str			=     [(arg.to_raw() if isinstance(A_     ,				A_							) else arg) for arg in args]
						snake_case							:  Dict			=     {k: (v.to_raw() if isinstance(A_     ,				A_							) else v) for k, v in kwargs.items()}
						return args, kwargs





def   A			(							A_				:       Union[str, Any]     ,				A_				:       Any=None							):
						if isinstance(A_     ,				A_							):
												snake_case							:  Union[str, 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]](A_							)
																								else:
																														snake_case							:  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							):
																																				snake_case							:  Dict			=     _v(A_							)
																								if k not in decoded_outputs:
																														snake_case							:  List[str]			=     AgentType[v]

						elif isinstance(A_     ,				(list, tuple)							):
												snake_case							:  Union[str, 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
																								snake_case							:  Union[str, Any]			=     False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(A_     ,				_k							):
																																				decoded_outputs.append(_v(A_							)							)
																																				snake_case							:  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
																		snake_case							:  List[str]			=     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

