

"""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


a__			:						List[str]							=			logging.get_logger(__name__)

if is_vision_available():
							import PIL.Image
							from PIL import Image
							from PIL.Image import Image as ImageType
else:
							a__			:						Tuple							=			object

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf


class       _snake_case  :







						def __init__(self  ,				UpperCAmelCase_				)      ->    Optional[Any]:
											__lowercase        =      value







						def __str__(self				)      ->    Any:
											return self.to_string()







						def 							_UpperCamelCase     (self				)      ->    List[Any]:
											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				)      ->    str:
											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 							_UpperCamelCase     (self				)      ->    Optional[int]:
											return self._value







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											return self._value



class       _snake_case  (    UpperCamelCase    ,      UpperCamelCase							):







						def __init__(self  ,				UpperCAmelCase_				)      ->    List[str]:
											super().__init__(UpperCAmelCase_				)

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

											__lowercase        =      None
											__lowercase        =      None
											__lowercase        =      None

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







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=None				)      ->    List[Any]:
											from IPython.display import Image, display

											display(Image(self.to_string()				)				)







						def 							_UpperCamelCase     (self				)      ->    str:
											if self._raw is not None:
																return self._raw

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







						def 							_UpperCamelCase     (self				)      ->    int:
											if self._path is not None:
																return self._path

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

																return self._path

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

																# There is likely simpler than load into image into save
																__lowercase        =      Image.fromarray((array * 2_5_5).astype(np.uinta				)				)

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

																img.save(self._path				)

																return self._path



class       _snake_case  (    UpperCamelCase							):







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=1_6_0_0_0				)      ->    Tuple:
											super().__init__(UpperCAmelCase_				)

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

											__lowercase        =      None
											__lowercase        =      None

											__lowercase        =      samplerate

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







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=None				)      ->    Dict:
											from IPython.display import Audio, display

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







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											if self._tensor is not None:
																return self._tensor

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







						def 							_UpperCamelCase     (self				)      ->    Dict:
											if self._path is not None:
																return self._path

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



a__			:						List[str]							=			{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
a__			:						Optional[int]							=			{str: AgentText}

if is_vision_available():
							a__			:						str							=			AgentImage





def 					__UpperCamelCase (     *a__:			Optional[int]			,						**a__:			Dict      )					->      List[Any]:




					"""simple docstring"""



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





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			Tuple=None      )					->      Dict:




					"""simple docstring"""



					if isinstance(a__			,						a__      ):
										__lowercase        =      {}
										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:
																									__lowercase        =      AGENT_TYPE_MAPPING[output_types[i]](a__      )
																				else:
																									__lowercase        =      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      ):
																														__lowercase        =      _v(a__      )
																				if k not in decoded_outputs:
																									__lowercase        =      AgentType[v]

					elif isinstance(a__			,						(list, tuple)      ):
										__lowercase        =      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
																				__lowercase        =      False
																				for _k, _v in INSTANCE_TYPE_MAPPING.items():
																									if isinstance(a__			,						_k      ):
																														decoded_outputs.append(_v(a__      )      )
																														__lowercase        =      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
															__lowercase        =      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





"""simple docstring"""

from typing import Mapping

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


a__			:						Dict							=			logging.get_logger(__name__)

a__			:						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       _snake_case  (    UpperCamelCase							):
						__snake_case			      =  'longt5'
						__snake_case			      =  ['past_key_values']
						__snake_case			      =  {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







						def __init__(self  ,				UpperCAmelCase_=3_2_1_2_8  ,				UpperCAmelCase_=5_1_2  ,				UpperCAmelCase_=6_4  ,				UpperCAmelCase_=2_0_4_8  ,				UpperCAmelCase_=6  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=8  ,				UpperCAmelCase_=1_2_7  ,				UpperCAmelCase_=1_6  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=1_2_8  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=1e-6  ,				UpperCAmelCase_=1.0  ,				UpperCAmelCase_="relu"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_="local"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=0  ,				UpperCAmelCase_=1  ,				**UpperCAmelCase_  ,				)      ->    int:
											__lowercase        =      vocab_size
											__lowercase        =      d_model
											__lowercase        =      d_kv
											__lowercase        =      d_ff
											__lowercase        =      num_layers
											# default = symmetry
											__lowercase        =      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											__lowercase        =      num_heads
											__lowercase        =      local_radius
											__lowercase        =      global_block_size
											__lowercase        =      relative_attention_num_buckets
											__lowercase        =      relative_attention_max_distance
											__lowercase        =      dropout_rate
											__lowercase        =      layer_norm_epsilon
											__lowercase        =      initializer_factor
											__lowercase        =      feed_forward_proj
											__lowercase        =      encoder_attention_type
											__lowercase        =      use_cache

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

											if len(UpperCAmelCase_				) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_				) > 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":
																__lowercase        =      '''gelu_new'''

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



class       _snake_case  (    UpperCamelCase							):







						@property
						def 							_UpperCamelCase     (self				)      ->    Mapping[str, Mapping[int, str]]:
											__lowercase        =      {
											    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
											    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
											}
											if self.use_past:
																__lowercase        =      '''past_encoder_sequence + sequence'''
																__lowercase        =      {0: '''batch'''}
																__lowercase        =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}

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

											return common_inputs







						@property
						def 							_UpperCamelCase     (self				)      ->    int:
											return 1_3


