
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   hidden_size
            __snake_case						       =   num_hidden_layers
            __snake_case						       =   num_attention_heads
            __snake_case						       =   intermediate_size
            __snake_case						       =   hidden_act
            __snake_case						       =   hidden_dropout_prob
            __snake_case						       =   attention_probs_dropout_prob
            __snake_case						       =   max_position_embeddings
            __snake_case						       =   type_vocab_size
            __snake_case						       =   initializer_range
            __snake_case						       =   relative_attention
            __snake_case						       =   max_relative_positions
            __snake_case						       =   pad_token_id
            __snake_case						       =   position_biased_input

            # Backwards compatibility
            if type(SCREAMING_SNAKE_CASE_    ) == str:
                   __snake_case						       =   [x.strip() for x in pos_att_type.lower().split('|'    )]

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

            __snake_case						       =   kwargs.get('pooler_hidden_size'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pooler_dropout
            __snake_case						       =   pooler_hidden_act







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]    )
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]    )

     @property
     def 						__lowerCamelCase				(  self    ):
            return 12

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 3							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            __snake_case						       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_							,       framework=SCREAMING_SNAKE_CASE_    )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs


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


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

if is_vision_available():
 import PIL.Image
 from PIL import Image
 from PIL.Image import Image as ImageType
else:
 lowerCamelCase_				:							Union[str, Any]       =    object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   value

     def __str__(  self    ):
            return self.to_string()

     def 						__lowerCamelCase				(  self    ):
            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    ):
            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					):

     def 						__lowerCamelCase				(  self    ):
            return self._value

     def 						__lowerCamelCase				(  self    ):
            return self._value







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):

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

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

            __snake_case						       =   None
            __snake_case						       =   None
            __snake_case						       =   None

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Image, display

            display(Image(self.to_string()    )    )

     def 						__lowerCamelCase				(  self    ):
            if self._raw is not None:
                   return self._raw

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

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

            if self._raw is not None:
                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   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						       =   self._tensor.cpu().detach().numpy()

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

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

                   img.save(self._path    )

                   return self._path







class  _lowerCamelCase			(lowerCamelCase					):

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

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

            __snake_case						       =   None
            __snake_case						       =   None

            __snake_case						       =   samplerate

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=None    ):
            from IPython.display import Audio, display

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

     def 						__lowerCamelCase				(  self    ):
            if self._tensor is not None:
                   return self._tensor

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

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

            if self._tensor is not None:
                   __snake_case						       =   tempfile.mkdtemp()
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       str(uuid.uuida()    ) + '.wav'    )
                   sf.write(self._path							,       self._tensor							,       samplerate=self.samplerate    )
                   return self._path





lowerCamelCase_				:							Optional[Any]       =    {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
lowerCamelCase_				:							Any       =    {str: AgentText}

if is_vision_available():
 lowerCamelCase_				:							Optional[int]       =    AgentImage
def        __lowercase(							*__snake_case				:		Optional[Any]    ,**__snake_case				:		str					)       ->   Tuple:
       __snake_case						       =   [(arg.to_raw() if isinstance(__snake_case    ,__snake_case					) else arg) for arg in args]
       __snake_case						       =   {k: (v.to_raw() if isinstance(__snake_case    ,__snake_case					) else v) for k, v in kwargs.items()}
       return args, kwargs
def        __lowercase(							__snake_case				:		Optional[int]    ,__snake_case				:		List[str]=None					)       ->   Tuple:
       if isinstance(__snake_case    ,__snake_case					):
              __snake_case						       =   {}
              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						       =   AGENT_TYPE_MAPPING[output_types[i]](__snake_case					)
                            else:
                                   __snake_case						       =   AgentType(__snake_case					)

                     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(__snake_case    ,_k					):
                                          __snake_case						       =   _v(__snake_case					)
                            if k not in decoded_outputs:
                                   __snake_case						       =   AgentType[v]

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

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

       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						       =   AGENT_TYPE_MAPPING[output_types[0]](__snake_case					)

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

       return decoded_outputs
