

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     a__  (    snake_case	):
			"""simple docstring"""







			def __get__(			self					,					lowercase					,					lowercase=None				)     ->  Optional[Any]:
							'''simple docstring'''






							if obj is None:
											return self
							if self.fget is None:
											raise AttributeError("unreadable attribute"				)
							A__						=	"__cached_" + self.fget.__name__
							A__						=	getattr(lowercase					,					lowercase					,					lowercase				)
							if cached is None:
											A__						=	self.fget(lowercase				)
											setattr(lowercase					,					lowercase					,					lowercase				)
							return cached



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[int]					)	->					Any:




				'''simple docstring'''





				A__						=	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__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]					)	->					List[Any]:




				'''simple docstring'''





				if is_torch_fx_proxy(SCREAMING_SNAKE_CASE_					):
								return True
				if is_torch_available():
								import torch

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

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

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

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

				return isinstance(SCREAMING_SNAKE_CASE_		,				np.ndarray					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					Tuple:




				'''simple docstring'''





				return isinstance(SCREAMING_SNAKE_CASE_		,				np.ndarray					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int					)	->					Optional[Any]:




				'''simple docstring'''





				return _is_numpy(SCREAMING_SNAKE_CASE_					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple					)	->					Optional[Any]:




				'''simple docstring'''





				import torch

				return isinstance(SCREAMING_SNAKE_CASE_		,				torch.Tensor					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					Any:




				'''simple docstring'''





				return False if not is_torch_available() else _is_torch(SCREAMING_SNAKE_CASE_					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]					)	->					str:




				'''simple docstring'''





				import torch

				return isinstance(SCREAMING_SNAKE_CASE_		,				torch.device					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]					)	->					Tuple:




				'''simple docstring'''





				return False if not is_torch_available() else _is_torch_device(SCREAMING_SNAKE_CASE_					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					Tuple:




				'''simple docstring'''





				import torch

				if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					):
								if hasattr(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					):
												A__						=	getattr(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)
								else:
												return False
				return isinstance(SCREAMING_SNAKE_CASE_		,				torch.dtype					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]					)	->					Optional[Any]:




				'''simple docstring'''





				return False if not is_torch_available() else _is_torch_dtype(SCREAMING_SNAKE_CASE_					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]					)	->					Optional[Any]:




				'''simple docstring'''





				import tensorflow as tf

				return isinstance(SCREAMING_SNAKE_CASE_		,				tf.Tensor					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]					)	->					Union[str, Any]:




				'''simple docstring'''





				return False if not is_tf_available() else _is_tensorflow(SCREAMING_SNAKE_CASE_					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str					)	->					Union[str, Any]:




				'''simple docstring'''





				import tensorflow as tf

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict					)	->					int:




				'''simple docstring'''





				return False if not is_tf_available() else _is_tf_symbolic_tensor(SCREAMING_SNAKE_CASE_					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]					)	->					str:




				'''simple docstring'''





				import jax.numpy as jnp  # noqa: F811

				return isinstance(SCREAMING_SNAKE_CASE_		,				jnp.ndarray					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]					)	->					Any:




				'''simple docstring'''





				return False if not is_flax_available() else _is_jax(SCREAMING_SNAKE_CASE_					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple					)	->					str:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict					)	->					Union[str, Any]:




				'''simple docstring'''





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

class     a__  (    snake_case	):
			"""simple docstring"""







			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	fields(self				)

							# Safety and consistency checks
							if not len(lowercase				):
											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.'				)

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

							if other_fields_are_none and not is_tensor(lowercase				):
											if isinstance(lowercase					,					lowercase				):
															A__						=	first_field.items()
															A__						=	True
											else:
															try:
																			A__						=	iter(lowercase				)
																			A__						=	True
															except TypeError:
																			A__						=	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(lowercase				):
																			if (
																			    not isinstance(lowercase					,					(list, tuple)				)
																			    or not len(lowercase				) == 2
																			    or not isinstance(element[0]					,					lowercase				)
																			):
																							if idx == 0:
																											# If we do not have an iterator of key/values, set it as attribute
																											A__						=	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:
																							A__						=	element[1]
											elif first_field is not None:
															A__						=	first_field
							else:
											for field in class_fields:
															A__						=	getattr(self					,					field.name				)
															if v is not None:
																			A__						=	v


			def __delitem__(			self					,					*lowercase					,					**lowercase				)     ->  int:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  Optional[int]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  Dict:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  List[Any]:
							'''simple docstring'''






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


			def __getitem__(			self					,					lowercase				)     ->  Optional[Any]:
							'''simple docstring'''






							if isinstance(lowercase					,					lowercase				):
											A__						=	dict(self.items()				)
											return inner_dict[k]
							else:
											return self.to_tuple()[k]


			def __setattr__(			self					,					lowercase					,					lowercase				)     ->  str:
							'''simple docstring'''






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


			def __setitem__(			self					,					lowercase					,					lowercase				)     ->  str:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  Tuple[Any]:
							'''simple docstring'''






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

class     a__  (    snake_case							,		snake_case	):
			"""simple docstring"""







			@classmethod
			def    UpperCamelCase						(			cls					,					lowercase				)     ->  Optional[int]:
							'''simple docstring'''






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

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				'longest'
			__lowerCamelCase       				=				'max_length'
			__lowerCamelCase       				=				'do_not_pad'

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				'pt'
			__lowerCamelCase       				=				'tf'
			__lowerCamelCase       				=				'np'
			__lowerCamelCase       				=				'jax'

class     a__  :
			"""simple docstring"""







			def __init__(			self					,					lowercase				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	context_managers
							A__						=	ExitStack()


			def __enter__(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






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


			def __exit__(			self					,					*lowercase					,					**lowercase				)     ->  Union[str, Any]:
							'''simple docstring'''






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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]					)	->					List[Any]:




				'''simple docstring'''





				A__						=	infer_framework(SCREAMING_SNAKE_CASE_					)
				if framework == "tf":
								A__						=	inspect.signature(model_class.call					)  # TensorFlow models
				elif framework == "pt":
								A__						=	inspect.signature(model_class.forward					)  # PyTorch models
				else:
								A__						=	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__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]					)	->					str:




				'''simple docstring'''





				A__						=	model_class.__name__
				A__						=	infer_framework(SCREAMING_SNAKE_CASE_					)
				if framework == "tf":
								A__						=	inspect.signature(model_class.call					)  # TensorFlow models
				elif framework == "pt":
								A__						=	inspect.signature(model_class.forward					)  # PyTorch models
				else:
								A__						=	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__							(		SCREAMING_SNAKE_CASE_:							MutableMapping		,				SCREAMING_SNAKE_CASE_:							str = ""		,				SCREAMING_SNAKE_CASE_:							str = "."					)	->					Union[str, Any]:




				'''simple docstring'''





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

				return dict(_flatten_dict(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)					)



@contextmanager
def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							bool = False					)	->					Dict:




				'''simple docstring'''





				if use_temp_dir:
								with tempfile.TemporaryDirectory() as tmp_dir:
												yield tmp_dir
				else:
								yield working_dir



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							List[str]=None					)	->					List[Any]:




				'''simple docstring'''





				if is_numpy_array(SCREAMING_SNAKE_CASE_					):
								return np.transpose(SCREAMING_SNAKE_CASE_		,				axes=SCREAMING_SNAKE_CASE_					)
				elif is_torch_tensor(SCREAMING_SNAKE_CASE_					):
								return array.T if axes is None else array.permute(*SCREAMING_SNAKE_CASE_					)
				elif is_tf_tensor(SCREAMING_SNAKE_CASE_					):
								import tensorflow as tf

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					int:




				'''simple docstring'''





				if is_numpy_array(SCREAMING_SNAKE_CASE_					):
								return np.reshape(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)
				elif is_torch_tensor(SCREAMING_SNAKE_CASE_					):
								return array.reshape(*SCREAMING_SNAKE_CASE_					)
				elif is_tf_tensor(SCREAMING_SNAKE_CASE_					):
								import tensorflow as tf

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							List[str]=None					)	->					str:




				'''simple docstring'''





				if is_numpy_array(SCREAMING_SNAKE_CASE_					):
								return np.squeeze(SCREAMING_SNAKE_CASE_		,				axis=SCREAMING_SNAKE_CASE_					)
				elif is_torch_tensor(SCREAMING_SNAKE_CASE_					):
								return array.squeeze() if axis is None else array.squeeze(dim=SCREAMING_SNAKE_CASE_					)
				elif is_tf_tensor(SCREAMING_SNAKE_CASE_					):
								import tensorflow as tf

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]		,				SCREAMING_SNAKE_CASE_:							Any					)	->					Union[str, Any]:




				'''simple docstring'''





				if is_numpy_array(SCREAMING_SNAKE_CASE_					):
								return np.expand_dims(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)
				elif is_torch_tensor(SCREAMING_SNAKE_CASE_					):
								return array.unsqueeze(dim=SCREAMING_SNAKE_CASE_					)
				elif is_tf_tensor(SCREAMING_SNAKE_CASE_					):
								import tensorflow as tf

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str					)	->					Tuple:




				'''simple docstring'''





				if is_numpy_array(SCREAMING_SNAKE_CASE_					):
								return np.size(SCREAMING_SNAKE_CASE_					)
				elif is_torch_tensor(SCREAMING_SNAKE_CASE_					):
								return array.numel()
				elif is_tf_tensor(SCREAMING_SNAKE_CASE_					):
								import tensorflow as tf

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					Dict:




				'''simple docstring'''





				for key, value in auto_map.items():
								if isinstance(SCREAMING_SNAKE_CASE_		,				(tuple, list)					):
												A__						=	[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:
												A__						=	F'{repo_id}--{value}'

				return auto_map





def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Any					)	->					Union[str, Any]:




				'''simple docstring'''





				for base_class in inspect.getmro(SCREAMING_SNAKE_CASE_					):
								A__						=	base_class.__module__
								A__						=	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'''

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase							:   str  						=	logging.get_logger(__name__)

lowerCamelCase							:   int  						=	{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Union[str, Any]  					=							"""mvp"""
      lowerCAmelCase__					:     Optional[Any]  					=							["""past_key_values"""]
      lowerCAmelCase__					:     List[str]  					=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



      def __init__(self    :    Any   ,						UpperCamelCase    :    Optional[int]=50267   ,						UpperCamelCase    :    Tuple=1024   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Tuple=4096   ,						UpperCamelCase    :    Dict=16   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Optional[int]=4096   ,						UpperCamelCase    :    Optional[int]=16   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    List[Any]="gelu"   ,						UpperCamelCase    :    Union[str, Any]=1024   ,						UpperCamelCase    :    Optional[Any]=0.1   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    Optional[Any]=0.02   ,						UpperCamelCase    :    List[str]=0.0   ,						UpperCamelCase    :    List[str]=False   ,						UpperCamelCase    :    Optional[int]=True   ,						UpperCamelCase    :    Any=1   ,						UpperCamelCase    :    int=0   ,						UpperCamelCase    :    int=2   ,						UpperCamelCase    :    Any=True   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    int=100   ,						UpperCamelCase    :    Optional[Any]=800   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             lowercase__      			= vocab_size
             lowercase__      			= max_position_embeddings
             lowercase__      			= d_model
             lowercase__      			= encoder_ffn_dim
             lowercase__      			= encoder_layers
             lowercase__      			= encoder_attention_heads
             lowercase__      			= decoder_ffn_dim
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_attention_heads
             lowercase__      			= dropout
             lowercase__      			= attention_dropout
             lowercase__      			= activation_dropout
             lowercase__      			= activation_function
             lowercase__      			= init_std
             lowercase__      			= encoder_layerdrop
             lowercase__      			= decoder_layerdrop
             lowercase__      			= classifier_dropout
             lowercase__      			= use_cache
             lowercase__      			= encoder_layers
             lowercase__      			= scale_embedding  # scale factor will be sqrt(d_model) if True
             lowercase__      			= use_prompt
             lowercase__      			= prompt_length
             lowercase__      			= prompt_mid_dim

             super().__init__(
                 pad_token_id=UpperCamelCase   ,						bos_token_id=UpperCamelCase   ,						eos_token_id=UpperCamelCase   ,						is_encoder_decoder=UpperCamelCase   ,						decoder_start_token_id=UpperCamelCase   ,						forced_eos_token_id=UpperCamelCase   ,						**UpperCamelCase   ,						)

             if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''   ,						UpperCamelCase ):
                    lowercase__      			= self.bos_token_id
                    warnings.warn(
                        f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                        '''The config can simply be saved and uploaded again to be fixed.''' )




