
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


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
