


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


snake_case_  :     str							  =							logging.get_logger(__name__)

if is_vision_available():
       import PIL.Image
       from PIL import Image
       from PIL.Image import Image as ImageType
else:
       snake_case_  :     Optional[Any]							  =							object

if is_torch_available():
       import torch

if is_soundfile_availble():
       import soundfile as sf

class   A__							:




      def __init__(    self   : Optional[Any]			,      _a   : Optional[int]					)							->       Tuple:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=value




      def __str__(    self   : Dict					)							->       Optional[Any]:


          """simple docstring"""





          return self.to_string()




      def 							__UpperCamelCase			(    self   : Tuple					)							->       str:


          """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 							__UpperCamelCase			(    self   : List[Any]					)							->       str:


          """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   A__							(						UpperCamelCase__				,       UpperCamelCase__					):




      def 							__UpperCamelCase			(    self   : List[Any]					)							->       Tuple:


          """simple docstring"""





          return self._value




      def 							__UpperCamelCase			(    self   : List[Any]					)							->       List[str]:


          """simple docstring"""





          return self._value

class   A__							(						UpperCamelCase__				,       UpperCamelCase__					):




      def __init__(    self   : Tuple			,      _a   : List[str]					)							->       int:


          """simple docstring"""





          super().__init__(_a					)

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

          _SCREAMING_SNAKE_CASE     					=None
          _SCREAMING_SNAKE_CASE     					=None
          _SCREAMING_SNAKE_CASE     					=None

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




      def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : Optional[Any]=None			,      _a   : Optional[Any]=None					)							->       List[Any]:


          """simple docstring"""





          from IPython.display import Image, display

          display(Image(self.to_string()					)					)




      def 							__UpperCamelCase			(    self   : str					)							->       List[Any]:


          """simple docstring"""





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

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




      def 							__UpperCamelCase			(    self   : Any					)							->       Union[str, Any]:


          """simple docstring"""





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

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

              return self._path

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

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

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

              img.save(self._path					)

              return self._path

class   A__							(						UpperCamelCase__					):




      def __init__(    self   : Union[str, Any]			,      _a   : Dict			,      _a   : Dict=1_6000					)							->       Optional[int]:


          """simple docstring"""





          super().__init__(_a					)

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

          _SCREAMING_SNAKE_CASE     					=None
          _SCREAMING_SNAKE_CASE     					=None

          _SCREAMING_SNAKE_CASE     					=samplerate

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




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


          """simple docstring"""





          from IPython.display import Audio, display

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




      def 							__UpperCamelCase			(    self   : List[Any]					)							->       Tuple:


          """simple docstring"""





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

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




      def 							__UpperCamelCase			(    self   : List[Any]					)							->       Tuple:


          """simple docstring"""





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

          if self._tensor is not None:
              _SCREAMING_SNAKE_CASE     					=tempfile.mkdtemp()
              _SCREAMING_SNAKE_CASE     					=os.path.join(_a			,      str(uuid.uuida()					) + '''.wav'''					)
              sf.write(self._path			,      self._tensor			,      samplerate=self.samplerate					)
              return self._path
snake_case_  :     Dict							  =							{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
snake_case_  :     Dict							  =							{str: AgentText}

if is_vision_available():
       snake_case_  :     Union[str, Any]							  =							AgentImage







def   lowerCamelCase(				*a__		,**a__):
    _SCREAMING_SNAKE_CASE     					=[(arg.to_raw() if isinstance(a__		,a__) else arg) for arg in args]
    _SCREAMING_SNAKE_CASE     					={k: (v.to_raw() if isinstance(a__		,a__) else v) for k, v in kwargs.items()}
    return args, kwargs







def   lowerCamelCase(				a__		,a__=None):
    if isinstance(a__		,a__):
        _SCREAMING_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:
                    _SCREAMING_SNAKE_CASE     					=AGENT_TYPE_MAPPING[output_types[i]](a__)
                else:
                    _SCREAMING_SNAKE_CASE     					=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):
                        _SCREAMING_SNAKE_CASE     					=_v(a__)
                if k not in decoded_outputs:
                    _SCREAMING_SNAKE_CASE     					=AgentType[v]

    elif isinstance(a__		,(list, tuple)):
        _SCREAMING_SNAKE_CASE     					=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
                _SCREAMING_SNAKE_CASE     					=False
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(a__		,_k):
                        decoded_outputs.append(_v(a__))
                        _SCREAMING_SNAKE_CASE     					=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
            _SCREAMING_SNAKE_CASE     					=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 OnnxConfigWithPast
from ...utils import logging


snake_case_  :     Tuple							  =							logging.get_logger(__name__)

snake_case_  :     int							  =							{
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class   A__							(						UpperCamelCase__					):
						UpperCAmelCase     =  "plbart"
						UpperCAmelCase     =  ["past_key_values"]
						UpperCAmelCase     =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




						def __init__(    self   : Union[str, Any]			,      _a   : List[str]=5_0005			,      _a   : List[str]=1024			,      _a   : int=6			,      _a   : Union[str, Any]=3072			,      _a   : List[str]=12			,      _a   : int=6			,      _a   : Optional[int]=3072			,      _a   : Any=12			,      _a   : Any=0.0			,      _a   : Optional[Any]=0.0			,      _a   : List[Any]=True			,      _a   : Any=True			,      _a   : Any="gelu"			,      _a   : int=768			,      _a   : int=0.1			,      _a   : int=0.1			,      _a   : List[str]=0.0			,      _a   : Optional[int]=0.02			,      _a   : Dict=0.0			,      _a   : List[str]=True			,      _a   : List[Any]=1			,      _a   : Tuple=0			,      _a   : Tuple=2			,      _a   : Optional[int]=2			,      **_a   : Optional[int]			,      )							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=vocab_size
										_SCREAMING_SNAKE_CASE     					=max_position_embeddings
										_SCREAMING_SNAKE_CASE     					=d_model
										_SCREAMING_SNAKE_CASE     					=encoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=encoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=decoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=decoder_layers
										_SCREAMING_SNAKE_CASE     					=decoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=dropout
										_SCREAMING_SNAKE_CASE     					=attention_dropout
										_SCREAMING_SNAKE_CASE     					=activation_dropout
										_SCREAMING_SNAKE_CASE     					=activation_function
										_SCREAMING_SNAKE_CASE     					=init_std
										_SCREAMING_SNAKE_CASE     					=encoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=decoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=classifier_dropout
										_SCREAMING_SNAKE_CASE     					=use_cache
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=scale_embedding  # scale factor will be sqrt(d_model) if True
										super().__init__(
										    pad_token_id=_a			,      bos_token_id=_a			,      eos_token_id=_a			,      is_encoder_decoder=_a			,      forced_eos_token_id=_a			,      **_a			,      )

class   A__							(						UpperCamelCase__					):




						@property
						def 							__UpperCamelCase			(    self   : int					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										return OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
										    ]					)




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.use_past:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)
										else:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)