






"""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							:       Any       					=		logging.get_logger(__name__)

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

if is_torch_available():
   import torch

if is_soundfile_availble():
   import soundfile as sf






class 		snake_case    :





    def __init__(				self      :   Dict							,						A      :   Tuple     ):


      '''simple docstring'''




      a	:     Tuple							=  value





    def __str__(				self      :   Any     ):


      '''simple docstring'''




      return self.to_string()





    def 							lowerCamelCase__ (				self      :   int     ):


      '''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 							lowerCamelCase__ (				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 		snake_case    (    UpperCAmelCase  ,					UpperCAmelCase			):





    def 							lowerCamelCase__ (				self      :   str     ):


      '''simple docstring'''




      return self._value





    def 							lowerCamelCase__ (				self      :   Any     ):


      '''simple docstring'''




      return self._value






class 		snake_case    (    UpperCAmelCase  ,					UpperCAmelCase			):





    def __init__(				self      :   Dict							,						A      :   Union[str, Any]     ):


      '''simple docstring'''




      super().__init__(SCREAMING_SNAKE_CASE_     )

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

      a	:     Dict							=  None
      a	:     int							=  None
      a	:     List[str]							=  None

      if isinstance(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     ):
        a	:     str							=  value
      elif isinstance(SCREAMING_SNAKE_CASE_							,						(str, pathlib.Path)     ):
        a	:     Optional[int]							=  value
      elif isinstance(SCREAMING_SNAKE_CASE_							,						torch.Tensor     ):
        a	:     int							=  value
      else:
        raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_     )}'''     )





    def 							lowerCamelCase__ (				self      :   Optional[Any]							,						A      :   Tuple=None							,						A      :   Optional[Any]=None     ):


      '''simple docstring'''




      from IPython.display import Image, display

      display(Image(self.to_string()     )     )





    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




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

      if self._path is not None:
        a	:     Optional[int]							=  Image.open(self._path     )
        return self._raw





    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




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

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

        return self._path

      if self._tensor is not None:
        a	:     Optional[int]							=  self._tensor.cpu().detach().numpy()

        # There is likely simpler than load into image into save
        a	:     Union[str, Any]							=  Image.fromarray((array * 2_5_5).astype(np.uinta     )     )

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

        img.save(self._path     )

        return self._path






class 		snake_case    (    UpperCAmelCase			):





    def __init__(				self      :   Dict							,						A      :   Dict							,						A      :   Dict=1_6_0_0_0     ):


      '''simple docstring'''




      super().__init__(SCREAMING_SNAKE_CASE_     )

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

      a	:     List[str]							=  None
      a	:     str							=  None

      a	:     Optional[int]							=  samplerate

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





    def 							lowerCamelCase__ (				self      :   Dict							,						A      :   int=None							,						A      :   Any=None     ):


      '''simple docstring'''




      from IPython.display import Audio, display

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





    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




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

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





    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




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

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




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

if is_vision_available():
   _UpperCamelCase							:       Optional[int]       					=		AgentImage




def      snake_case    (*A_ :Optional[Any]      ,     **A_ :str ):







  '''simple docstring'''

  a	:     Tuple							=  [(arg.to_raw() if isinstance(__snake_case      ,     __snake_case ) else arg) for arg in args]
  a	:     int							=  {k: (v.to_raw() if isinstance(__snake_case      ,     __snake_case ) else v) for k, v in kwargs.items()}
  return args, kwargs






def      snake_case    (A_ :Optional[int]      ,     A_ :List[str]=None ):







  '''simple docstring'''

  if isinstance(__snake_case      ,     __snake_case ):
    a	:     Optional[int]							=  {}
    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:
          a	:     Optional[Any]							=  AGENT_TYPE_MAPPING[output_types[i]](__snake_case )
        else:
          a	:     Tuple							=  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 ):
            a	:     List[str]							=  _v(__snake_case )
        if k not in decoded_outputs:
          a	:     str							=  AgentType[v]

  elif isinstance(__snake_case      ,     (list, tuple) ):
    a	:     Optional[int]							=  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
        a	:     Tuple							=  False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(__snake_case      ,     _k ):
            decoded_outputs.append(_v(__snake_case ) )
            a	:     List[Any]							=  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
      a	:     Optional[int]							=  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






from typing import Mapping

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


lowerCamelCase_				:							Union[str, Any]       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""longt5"""
     lowercase__				  =			["""past_key_values"""]
     lowercase__				  =			{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=32_128							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=64							,       SCREAMING_SNAKE_CASE_=2_048							,       SCREAMING_SNAKE_CASE_=6							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=8							,       SCREAMING_SNAKE_CASE_=127							,       SCREAMING_SNAKE_CASE_=16							,       SCREAMING_SNAKE_CASE_=32							,       SCREAMING_SNAKE_CASE_=128							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=1E-6							,       SCREAMING_SNAKE_CASE_=1.0							,       SCREAMING_SNAKE_CASE_="relu"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_="local"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=1							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   vocab_size
            __snake_case						       =   d_model
            __snake_case						       =   d_kv
            __snake_case						       =   d_ff
            __snake_case						       =   num_layers
            # default = symmetry
            __snake_case						       =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
            __snake_case						       =   num_heads
            __snake_case						       =   local_radius
            __snake_case						       =   global_block_size
            __snake_case						       =   relative_attention_num_buckets
            __snake_case						       =   relative_attention_max_distance
            __snake_case						       =   dropout_rate
            __snake_case						       =   layer_norm_epsilon
            __snake_case						       =   initializer_factor
            __snake_case						       =   feed_forward_proj
            __snake_case						       =   encoder_attention_type
            __snake_case						       =   use_cache

            __snake_case						       =   self.feed_forward_proj.split('-'    )
            __snake_case						       =   act_info[-1]
            __snake_case						       =   act_info[0] == 'gated'

            if len(SCREAMING_SNAKE_CASE_    ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_    ) > 2:
                   raise ValueError(
                       f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                       'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                       '\'gated-gelu\' or \'relu\''    )

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                   __snake_case						       =   'gelu_new'

            super().__init__(
                pad_token_id=SCREAMING_SNAKE_CASE_							,       eos_token_id=SCREAMING_SNAKE_CASE_							,       is_encoder_decoder=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_							,       )







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
            }
            if self.use_past:
                   __snake_case						       =   'past_encoder_sequence + sequence'
                   __snake_case						       =   {0: 'batch'}
                   __snake_case						       =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                   self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_							,       direction='inputs'    )

            return common_inputs

     @property
     def 						__lowerCamelCase				(  self    ):
            return 13
