



'''simple docstring'''




import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
      import jax.numpy as jnp






class 		_UpperCamelCase						(							lowerCamelCase__					):


   '''simple docstring'''

   def __get__(							self  :				str							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[Any]=None		):





     """simple docstring"""






     if obj is None:
       return self
     if self.fget is None:
       raise AttributeError("""unreadable attribute"""		)
     __SCREAMING_SNAKE_CASE :	Any					 =	"""__cached_""" + self.fget.__name__
     __SCREAMING_SNAKE_CASE :	str					 =	getattr(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
     if cached is None:
       __SCREAMING_SNAKE_CASE :	int					 =	self.fget(lowerCAmelCase__		)
       setattr(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
     return cached


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple      ):
  __SCREAMING_SNAKE_CASE :	List[str]					 =	val.lower()
  if val in {"y", "yes", "t", "true", "on", "1"}:
    return 1
  if val in {"n", "no", "f", "false", "off", "0"}:
    return 0
  raise ValueError(F"invalid truth value {val!r}"      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int]      ):
  if is_torch_fx_proxy(_lowerCamelCase      ):
    return True
  if is_torch_available():
    import torch

    if isinstance(_lowerCamelCase ,   torch.Tensor      ):
      return True
  if is_tf_available():
    import tensorflow as tf

    if isinstance(_lowerCamelCase ,   tf.Tensor      ):
      return True

  if is_flax_available():
    import jax.numpy as jnp
    from jax.core import Tracer

    if isinstance(_lowerCamelCase ,   (jnp.ndarray, Tracer)      ):
      return True

  return isinstance(_lowerCamelCase ,   np.ndarray      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[Any]      ):
  return isinstance(_lowerCamelCase ,   np.ndarray      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Dict      ):
  return _is_numpy(_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Any      ):
  import torch

  return isinstance(_lowerCamelCase ,   torch.Tensor      )


def        lowerCAmelCase_     (			_lowerCamelCase:     str      ):
  return False if not is_torch_available() else _is_torch(_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Dict      ):
  import torch

  return isinstance(_lowerCamelCase ,   torch.device      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Any      ):
  return False if not is_torch_available() else _is_torch_device(_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Any      ):
  import torch

  if isinstance(_lowerCamelCase ,   _lowerCamelCase      ):
    if hasattr(_lowerCamelCase ,   _lowerCamelCase      ):
      __SCREAMING_SNAKE_CASE :	Tuple					 =	getattr(_lowerCamelCase ,   _lowerCamelCase      )
    else:
      return False
  return isinstance(_lowerCamelCase ,   torch.dtype      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[Any]      ):
  return False if not is_torch_available() else _is_torch_dtype(_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     str      ):
  import tensorflow as tf

  return isinstance(_lowerCamelCase ,   tf.Tensor      )


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
  return False if not is_tf_available() else _is_tensorflow(_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Any      ):
  import tensorflow as tf

  # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
  if hasattr(_lowerCamelCase ,   """is_symbolic_tensor"""      ):
    return tf.is_symbolic_tensor(_lowerCamelCase      )
  return type(_lowerCamelCase      ) == tf.Tensor


def        lowerCAmelCase_     (			_lowerCamelCase:     Any      ):
  return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Any      ):
  import jax.numpy as jnp  # noqa: F811

  return isinstance(_lowerCamelCase ,   jnp.ndarray      )


def        lowerCAmelCase_     (			_lowerCamelCase:     List[str]      ):
  return False if not is_flax_available() else _is_jax(_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
  if isinstance(_lowerCamelCase ,   (dict, UserDict)      ):
    return {k: to_py_obj(_lowerCamelCase      ) for k, v in obj.items()}
  elif isinstance(_lowerCamelCase ,   (list, tuple)      ):
    return [to_py_obj(_lowerCamelCase      ) for o in obj]
  elif is_tf_tensor(_lowerCamelCase      ):
    return obj.numpy().tolist()
  elif is_torch_tensor(_lowerCamelCase      ):
    return obj.detach().cpu().tolist()
  elif is_jax_tensor(_lowerCamelCase      ):
    return np.asarray(_lowerCamelCase      ).tolist()
  elif isinstance(_lowerCamelCase ,   (np.ndarray, np.number)      ):  # tolist also works on 0d np arrays
    return obj.tolist()
  else:
    return obj


def        lowerCAmelCase_     (			_lowerCamelCase:     Any      ):
  if isinstance(_lowerCamelCase ,   (dict, UserDict)      ):
    return {k: to_numpy(_lowerCamelCase      ) for k, v in obj.items()}
  elif isinstance(_lowerCamelCase ,   (list, tuple)      ):
    return np.array(_lowerCamelCase      )
  elif is_tf_tensor(_lowerCamelCase      ):
    return obj.numpy()
  elif is_torch_tensor(_lowerCamelCase      ):
    return obj.detach().cpu().numpy()
  elif is_jax_tensor(_lowerCamelCase      ):
    return np.asarray(_lowerCamelCase      )
  else:
    return obj






class 		_UpperCamelCase						(							lowerCamelCase__					):


   '''simple docstring'''

   def UpperCamelCase__      (							self  :				Dict		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	int					 =	fields(self		)

     # Safety and consistency checks
     if not len(lowerCAmelCase__		):
       raise ValueError(F"{self.__class__.__name__} has no fields."		)
     if not all(field.default is None for field in class_fields[1:]		):
       raise ValueError(F"{self.__class__.__name__} should not have more than one required field."		)

     __SCREAMING_SNAKE_CASE :	Dict					 =	getattr(self							,  class_fields[0].name		)
     __SCREAMING_SNAKE_CASE :	Dict					 =	all(getattr(self							,  field.name		) is None for field in class_fields[1:]		)

     if other_fields_are_none and not is_tensor(lowerCAmelCase__		):
       if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
         __SCREAMING_SNAKE_CASE :	Any					 =	first_field.items()
         __SCREAMING_SNAKE_CASE :	Dict					 =	True
       else:
         try:
           __SCREAMING_SNAKE_CASE :	List[Any]					 =	iter(lowerCAmelCase__		)
           __SCREAMING_SNAKE_CASE :	Optional[int]					 =	True
         except TypeError:
           __SCREAMING_SNAKE_CASE :	int					 =	False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
       if first_field_iterator:
         for idx, element in enumerate(lowerCAmelCase__		):
           if (
               not isinstance(lowerCAmelCase__							,  (list, tuple)		)
               or not len(lowerCAmelCase__		) == 2
               or not isinstance(element[0]							,  lowerCAmelCase__		)
           ):
             if idx == 0:
               # If we do not have an iterator of key/values, set it as attribute
               __SCREAMING_SNAKE_CASE :	str					 =	first_field
             else:
               # If we have a mixed iterator, raise an error
               raise ValueError(
                   F"Cannot set key/value for {element}. It needs to be a tuple (key, value)."		)
             break
           setattr(self							,  element[0]							,  element[1]		)
           if element[1] is not None:
             __SCREAMING_SNAKE_CASE :	Optional[int]					 =	element[1]
       elif first_field is not None:
         __SCREAMING_SNAKE_CASE :	Optional[int]					 =	first_field
     else:
       for field in class_fields:
         __SCREAMING_SNAKE_CASE :	List[Any]					 =	getattr(self							,  field.name		)
         if v is not None:
           __SCREAMING_SNAKE_CASE :	Optional[int]					 =	v

   def __delitem__(							self  :				Optional[Any]							,  *lowerCAmelCase__  :				int							,  **lowerCAmelCase__  :				Tuple		):





     """simple docstring"""






     raise Exception(F"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."		)

   def UpperCamelCase__      (							self  :				str							,  *lowerCAmelCase__  :				Optional[Any]							,  **lowerCAmelCase__  :				Dict		):





     """simple docstring"""






     raise Exception(F"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."		)

   def UpperCamelCase__      (							self  :				str							,  *lowerCAmelCase__  :				Any							,  **lowerCAmelCase__  :				List[Any]		):





     """simple docstring"""






     raise Exception(F"You cannot use ``pop`` on a {self.__class__.__name__} instance."		)

   def UpperCamelCase__      (							self  :				int							,  *lowerCAmelCase__  :				List[str]							,  **lowerCAmelCase__  :				List[Any]		):





     """simple docstring"""






     raise Exception(F"You cannot use ``update`` on a {self.__class__.__name__} instance."		)

   def __getitem__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				Optional[Any]		):





     """simple docstring"""






     if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
       __SCREAMING_SNAKE_CASE :	Tuple					 =	dict(self.items()		)
       return inner_dict[k]
     else:
       return self.to_tuple()[k]

   def __setattr__(							self  :				str							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Any		):





     """simple docstring"""






     if name in self.keys() and value is not None:
       # Don't call self.__setitem__ to avoid recursion errors
       super().__setitem__(lowerCAmelCase__							,  lowerCAmelCase__		)
     super().__setattr__(lowerCAmelCase__							,  lowerCAmelCase__		)

   def __setitem__(							self  :				Tuple							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				str		):





     """simple docstring"""






     super().__setitem__(lowerCAmelCase__							,  lowerCAmelCase__		)
     # Don't call self.__setattr__ to avoid recursion errors
     super().__setattr__(lowerCAmelCase__							,  lowerCAmelCase__		)



   def UpperCamelCase__      (							self  :				Optional[Any]		):





     """simple docstring"""






     return tuple(self[k] for k in self.keys()		)






class 		_UpperCamelCase						(							lowerCamelCase__					,      lowerCamelCase__					):


   '''simple docstring'''

   @classmethod
   def UpperCamelCase__      (							cls  :				List[Any]							,  lowerCAmelCase__  :				Tuple		):





     """simple docstring"""






     raise ValueError(
         F"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()		)}"		)






class 		_UpperCamelCase						(							lowerCamelCase__					):


   '''simple docstring'''

   _A   :	Dict    					=	'''longest'''
   _A   :	Optional[Any]    					=	'''max_length'''
   _A   :	Tuple    					=	'''do_not_pad'''






class 		_UpperCamelCase						(							lowerCamelCase__					):


   '''simple docstring'''

   _A   :	Optional[int]    					=	'''pt'''
   _A   :	Union[str, Any]    					=	'''tf'''
   _A   :	Union[str, Any]    					=	'''np'''
   _A   :	Dict    					=	'''jax'''






class 		_UpperCamelCase						:


   '''simple docstring'''

   def __init__(							self  :				str							,  lowerCAmelCase__  :				List[ContextManager]		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	int					 =	context_managers
     __SCREAMING_SNAKE_CASE :	Dict					 =	ExitStack()

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





     """simple docstring"""






     for context_manager in self.context_managers:
       self.stack.enter_context(lowerCAmelCase__		)



   def __exit__(							self  :				Any							,  *lowerCAmelCase__  :				Any							,  **lowerCAmelCase__  :				List[str]		):





     """simple docstring"""






     self.stack.__exit__(*lowerCAmelCase__							,  **lowerCAmelCase__		)


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int]      ):
  __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	infer_framework(_lowerCamelCase      )
  if framework == "tf":
    __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	inspect.signature(model_class.call      )  # TensorFlow models
  elif framework == "pt":
    __SCREAMING_SNAKE_CASE :	int					 =	inspect.signature(model_class.forward      )  # PyTorch models
  else:
    __SCREAMING_SNAKE_CASE :	List[Any]					 =	inspect.signature(model_class.__call__      )  # Flax models

  for p in signature.parameters:
    if p == "return_loss" and signature.parameters[p].default is True:
      return True

  return False


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple      ):
  __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model_class.__name__
  __SCREAMING_SNAKE_CASE :	List[Any]					 =	infer_framework(_lowerCamelCase      )
  if framework == "tf":
    __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	inspect.signature(model_class.call      )  # TensorFlow models
  elif framework == "pt":
    __SCREAMING_SNAKE_CASE :	Dict					 =	inspect.signature(model_class.forward      )  # PyTorch models
  else:
    __SCREAMING_SNAKE_CASE :	List[Any]					 =	inspect.signature(model_class.__call__      )  # Flax models

  if "QuestionAnswering" in model_name:
    return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
  else:
    return [p for p in signature.parameters if "label" in p]


def        lowerCAmelCase_     (			_lowerCamelCase:     MutableMapping ,   _lowerCamelCase:     str = "" ,   _lowerCamelCase:     str = "."      ):

  def _flatten_dict(_lowerCamelCase:     str ,   _lowerCamelCase:     Any="" ,   _lowerCamelCase:     List[Any]="."      ):
    for k, v in d.items():
      __SCREAMING_SNAKE_CASE :	Tuple					 =	str(_lowerCamelCase      ) + delimiter + str(_lowerCamelCase      ) if parent_key else k
      if v and isinstance(_lowerCamelCase ,   _lowerCamelCase      ):
        yield from flatten_dict(_lowerCamelCase ,   _lowerCamelCase ,   delimiter=_lowerCamelCase      ).items()
      else:
        yield key, v

  return dict(_flatten_dict(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      )      )


@contextmanager
def        lowerCAmelCase_     (			_lowerCamelCase:     Dict ,   _lowerCamelCase:     bool = False      ):
  if use_temp_dir:
    with tempfile.TemporaryDirectory() as tmp_dir:
      yield tmp_dir
  else:
    yield working_dir


def        lowerCAmelCase_     (			_lowerCamelCase:     Union[str, Any] ,   _lowerCamelCase:     Optional[Any]=None      ):
  if is_numpy_array(_lowerCamelCase      ):
    return np.transpose(_lowerCamelCase ,   axes=_lowerCamelCase      )
  elif is_torch_tensor(_lowerCamelCase      ):
    return array.T if axes is None else array.permute(*_lowerCamelCase      )
  elif is_tf_tensor(_lowerCamelCase      ):
    import tensorflow as tf

    return tf.transpose(_lowerCamelCase ,   perm=_lowerCamelCase      )
  elif is_jax_tensor(_lowerCamelCase      ):
    return jnp.transpose(_lowerCamelCase ,   axes=_lowerCamelCase      )
  else:
    raise ValueError(F"Type not supported for transpose: {type(_lowerCamelCase      )}."      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Union[str, Any] ,   _lowerCamelCase:     Dict      ):
  if is_numpy_array(_lowerCamelCase      ):
    return np.reshape(_lowerCamelCase ,   _lowerCamelCase      )
  elif is_torch_tensor(_lowerCamelCase      ):
    return array.reshape(*_lowerCamelCase      )
  elif is_tf_tensor(_lowerCamelCase      ):
    import tensorflow as tf

    return tf.reshape(_lowerCamelCase ,   _lowerCamelCase      )
  elif is_jax_tensor(_lowerCamelCase      ):
    return jnp.reshape(_lowerCamelCase ,   _lowerCamelCase      )
  else:
    raise ValueError(F"Type not supported for reshape: {type(_lowerCamelCase      )}."      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     Dict=None      ):
  if is_numpy_array(_lowerCamelCase      ):
    return np.squeeze(_lowerCamelCase ,   axis=_lowerCamelCase      )
  elif is_torch_tensor(_lowerCamelCase      ):
    return array.squeeze() if axis is None else array.squeeze(dim=_lowerCamelCase      )
  elif is_tf_tensor(_lowerCamelCase      ):
    import tensorflow as tf

    return tf.squeeze(_lowerCamelCase ,   axis=_lowerCamelCase      )
  elif is_jax_tensor(_lowerCamelCase      ):
    return jnp.squeeze(_lowerCamelCase ,   axis=_lowerCamelCase      )
  else:
    raise ValueError(F"Type not supported for squeeze: {type(_lowerCamelCase      )}."      )


def        lowerCAmelCase_     (			_lowerCamelCase:     List[str] ,   _lowerCamelCase:     List[str]      ):
  if is_numpy_array(_lowerCamelCase      ):
    return np.expand_dims(_lowerCamelCase ,   _lowerCamelCase      )
  elif is_torch_tensor(_lowerCamelCase      ):
    return array.unsqueeze(dim=_lowerCamelCase      )
  elif is_tf_tensor(_lowerCamelCase      ):
    import tensorflow as tf

    return tf.expand_dims(_lowerCamelCase ,   axis=_lowerCamelCase      )
  elif is_jax_tensor(_lowerCamelCase      ):
    return jnp.expand_dims(_lowerCamelCase ,   axis=_lowerCamelCase      )
  else:
    raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase      )}."      )


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
  if is_numpy_array(_lowerCamelCase      ):
    return np.size(_lowerCamelCase      )
  elif is_torch_tensor(_lowerCamelCase      ):
    return array.numel()
  elif is_tf_tensor(_lowerCamelCase      ):
    import tensorflow as tf

    return tf.size(_lowerCamelCase      )
  elif is_jax_tensor(_lowerCamelCase      ):
    return array.size
  else:
    raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase      )}."      )


def        lowerCAmelCase_     (			_lowerCamelCase:     str ,   _lowerCamelCase:     Any      ):
  for key, value in auto_map.items():
    if isinstance(_lowerCamelCase ,   (tuple, list)      ):
      __SCREAMING_SNAKE_CASE :	Dict					 =	[F"{repo_id}--{v}" if (v is not None and """--""" not in v) else v for v in value]
    elif value is not None and "--" not in value:
      __SCREAMING_SNAKE_CASE :	Any					 =	F"{repo_id}--{value}"

  return auto_map


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int]      ):
  for base_class in inspect.getmro(_lowerCamelCase      ):
    __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	base_class.__module__
    __SCREAMING_SNAKE_CASE :	Any					 =	base_class.__name__
    if module.startswith("""tensorflow"""      ) or module.startswith("""keras"""      ) or name == "TFPreTrainedModel":
      return "tf"
    elif module.startswith("""torch"""      ) or name == "PreTrainedModel":
      return "pt"
    elif module.startswith("""flax"""      ) or module.startswith("""jax"""      ) or name == "FlaxPreTrainedModel":
      return "flax"
  else:
    raise TypeError(F"Could not infer framework from class {model_class}."      )




'''simple docstring'''




from typing import Mapping

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


UpperCamelCase__ :			Tuple												=				logging.get_logger(__name__)

UpperCamelCase__ :			List[str]												=				{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Union[str, Any]    					=	'''t5'''
			_A   :	Optional[Any]    					=	['''past_key_values''']
			_A   :	Any    					=	{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

			def __init__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]=3_2_1_2_8							,  lowerCAmelCase__  :				Dict=5_1_2							,  lowerCAmelCase__  :				str=6_4							,  lowerCAmelCase__  :				Tuple=2_0_4_8							,  lowerCAmelCase__  :				Optional[Any]=6							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Any=8							,  lowerCAmelCase__  :				Dict=3_2							,  lowerCAmelCase__  :				Dict=1_2_8							,  lowerCAmelCase__  :				Any=0.1							,  lowerCAmelCase__  :				str=1E-6							,  lowerCAmelCase__  :				str=1.0							,  lowerCAmelCase__  :				Optional[Any]="relu"							,  lowerCAmelCase__  :				List[str]=True							,  lowerCAmelCase__  :				Union[str, Any]=True							,  lowerCAmelCase__  :				Optional[Any]=0							,  lowerCAmelCase__  :				List[Any]=1							,  **lowerCAmelCase__  :				Dict							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	vocab_size
					__SCREAMING_SNAKE_CASE :	str					 =	d_model
					__SCREAMING_SNAKE_CASE :	str					 =	d_kv
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	d_ff
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_layers
					__SCREAMING_SNAKE_CASE :	List[str]					 =	(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	num_heads
					__SCREAMING_SNAKE_CASE :	Any					 =	relative_attention_num_buckets
					__SCREAMING_SNAKE_CASE :	int					 =	relative_attention_max_distance
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dropout_rate
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	layer_norm_epsilon
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	initializer_factor
					__SCREAMING_SNAKE_CASE :	Any					 =	feed_forward_proj
					__SCREAMING_SNAKE_CASE :	str					 =	use_cache

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.feed_forward_proj.split("""-"""		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	act_info[-1]
					__SCREAMING_SNAKE_CASE :	int					 =	act_info[0] == """gated"""

					if len(lowerCAmelCase__		) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__		) > 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":
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	"""gelu_new"""

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






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			@property
			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	{
					    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
					    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
					}
					if self.use_past:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""past_encoder_sequence + sequence"""
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{0: """batch"""}
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
					else:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	{0: """batch""", 1: """decoder_sequence"""}
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{0: """batch""", 1: """decoder_sequence"""}

					if self.use_past:
							self.fill_with_past_key_values_(lowerCAmelCase__							,  direction="""inputs"""		)

					return common_inputs



			@property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return 1_3