


"""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      lowerCamelCase__   (     __A				):







					"""simple docstring"""


					def __get__(       self    : List[str] ,       UpperCamelCase    : Optional[int] ,       UpperCamelCase    : Dict=None  ):
												'''simple docstring'''






												if obj is None:
																			return self
												if self.fget is None:
																			raise AttributeError("""unreadable attribute"""  )
												__UpperCAmelCase   :						Optional[int]      							=					"__cached_" + self.fget.__name__
												__UpperCAmelCase   :						Dict      							=					getattr(lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_  )
												if cached is None:
																			__UpperCAmelCase   :						Union[str, Any]      							=					self.fget(lowerCAmelCase_  )
																			setattr(lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_  )
												return cached






def      lowerCamelCase       (     _UpperCamelCase		:      Tuple							)		->       List[str]:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					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       (     _UpperCamelCase		:      Tuple							)		->       Any:

							'''simple docstring'''



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

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

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

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

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

							return isinstance(A__       ,			np.ndarray							)
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple							)		->       Dict:

							'''simple docstring'''



							return isinstance(A__       ,			np.ndarray							)
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple							)		->       str:

							'''simple docstring'''



							return _is_numpy(A__							)
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple							)		->       str:

							'''simple docstring'''



							import torch

							return isinstance(A__       ,			torch.Tensor							)
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple							)		->       List[Any]:

							'''simple docstring'''



							return False if not is_torch_available() else _is_torch(A__							)
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       Optional[int]:

							'''simple docstring'''



							import torch

							return isinstance(A__       ,			torch.device							)
def      lowerCamelCase       (     _UpperCamelCase		:      str							)		->       str:

							'''simple docstring'''



							return False if not is_torch_available() else _is_torch_device(A__							)
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple							)		->       List[str]:

							'''simple docstring'''



							import torch

							if isinstance(A__       ,			A__							):
														if hasattr(A__       ,			A__							):
																					__UpperCAmelCase   :						Tuple      							=					getattr(A__       ,			A__							)
														else:
																					return False
							return isinstance(A__       ,			torch.dtype							)
def      lowerCamelCase       (     _UpperCamelCase		:      Any							)		->       Dict:

							'''simple docstring'''



							return False if not is_torch_available() else _is_torch_dtype(A__							)
def      lowerCamelCase       (     _UpperCamelCase		:      Dict							)		->       Any:

							'''simple docstring'''



							import tensorflow as tf

							return isinstance(A__       ,			tf.Tensor							)
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Optional[int]:

							'''simple docstring'''



							return False if not is_tf_available() else _is_tensorflow(A__							)
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       List[str]:

							'''simple docstring'''



							import tensorflow as tf

							# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
							if hasattr(A__       ,			"""is_symbolic_tensor"""							):
														return tf.is_symbolic_tensor(A__							)
							return type(A__							) == tf.Tensor
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple							)		->       Dict:

							'''simple docstring'''



							return False if not is_tf_available() else _is_tf_symbolic_tensor(A__							)
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[int]							)		->       str:

							'''simple docstring'''



							import jax.numpy as jnp  # noqa: F811

							return isinstance(A__       ,			jnp.ndarray							)
def      lowerCamelCase       (     _UpperCamelCase		:      Dict							)		->       Union[str, Any]:

							'''simple docstring'''



							return False if not is_flax_available() else _is_jax(A__							)
def      lowerCamelCase       (     _UpperCamelCase		:      Dict							)		->       List[str]:

							'''simple docstring'''



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

							'''simple docstring'''



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





class      lowerCamelCase__   (     __A				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					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.'''  )

												__UpperCAmelCase   :						List[str]      							=					getattr(self ,       class_fields[0].name  )
												__UpperCAmelCase   :						Optional[int]      							=					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_  ):
																										__UpperCAmelCase   :						str      							=					first_field.items()
																										__UpperCAmelCase   :						List[str]      							=					True
																			else:
																										try:
																																	__UpperCAmelCase   :						List[Any]      							=					iter(lowerCAmelCase_  )
																																	__UpperCAmelCase   :						Union[str, Any]      							=					True
																										except TypeError:
																																	__UpperCAmelCase   :						Any      							=					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
																																															__UpperCAmelCase   :						int      							=					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:
																																								__UpperCAmelCase   :						Any      							=					element[1]
																			elif first_field is not None:
																										__UpperCAmelCase   :						Any      							=					first_field
												else:
																			for field in class_fields:
																										__UpperCAmelCase   :						Optional[int]      							=					getattr(self ,       field.name  )
																										if v is not None:
																																	__UpperCAmelCase   :						Any      							=					v






					def __delitem__(       self    : List[str] ,       *UpperCamelCase    : Optional[int] ,       **UpperCamelCase    : List[str]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : List[Any] ,       *UpperCamelCase    : Optional[Any] ,       **UpperCamelCase    : Any  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Tuple ,       *UpperCamelCase    : Union[str, Any] ,       **UpperCamelCase    : Dict  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Dict ,       *UpperCamelCase    : Tuple ,       **UpperCamelCase    : List[str]  ):
												'''simple docstring'''






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






					def __getitem__(       self    : Optional[Any] ,       UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												if isinstance(lowerCAmelCase_ ,       lowerCAmelCase_  ):
																			__UpperCAmelCase   :						Optional[int]      							=					dict(self.items()  )
																			return inner_dict[k]
												else:
																			return self.to_tuple()[k]






					def __setattr__(       self    : List[Any] ,       UpperCamelCase    : Any ,       UpperCamelCase    : List[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    : Optional[Any] ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Dict  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






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






class      lowerCamelCase__   (     __A     ,    __A				):







					"""simple docstring"""


					@classmethod
					def lowerCamelCase__							(       cls    : List[Any] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






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






class      lowerCamelCase__   (     __A				):







					"""simple docstring"""


					__a	       =							"""longest"""
					__a	       =							"""max_length"""
					__a	       =							"""do_not_pad"""






class      lowerCamelCase__   (     __A				):







					"""simple docstring"""


					__a	       =							"""pt"""
					__a	       =							"""tf"""
					__a	       =							"""np"""
					__a	       =							"""jax"""






class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Any ,       UpperCamelCase    : List[ContextManager]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					context_managers
												__UpperCAmelCase   :						List[Any]      							=					ExitStack()






					def __enter__(       self    : Optional[int]  ):
												'''simple docstring'''






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






					def __exit__(       self    : Dict ,       *UpperCamelCase    : Optional[Any] ,       **UpperCamelCase    : Tuple  ):
												'''simple docstring'''






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






def      lowerCamelCase       (     _UpperCamelCase		:      List[str]							)		->       Optional[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					infer_framework(A__							)
							if framework == "tf":
														__UpperCAmelCase   :						Any      							=					inspect.signature(model_class.call							)  # TensorFlow models
							elif framework == "pt":
														__UpperCAmelCase   :						int      							=					inspect.signature(model_class.forward							)  # PyTorch models
							else:
														__UpperCAmelCase   :						int      							=					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       (     _UpperCamelCase		:      List[Any]							)		->       Optional[int]:

							'''simple docstring'''



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

							'''simple docstring'''



							def _flatten_dict(_UpperCamelCase		:      Dict       ,			_UpperCamelCase		:      List[str]=""       ,			_UpperCamelCase		:      str="."							):
														for k, v in d.items():
																					__UpperCAmelCase   :						Optional[int]      							=					str(A__							) + delimiter + str(A__							) if parent_key else k
																					if v and isinstance(A__       ,			A__							):
																												yield from flatten_dict(A__       ,			A__       ,			delimiter=A__							).items()
																					else:
																												yield key, v

							return dict(_flatten_dict(A__       ,			A__       ,			A__							)							)
@contextmanager
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      Union[str, Any] = False							)		->       Tuple:

							'''simple docstring'''



							if use_temp_dir:
														with tempfile.TemporaryDirectory() as tmp_dir:
																					yield tmp_dir
							else:
														yield working_dir
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      Dict=None							)		->       Optional[Any]:

							'''simple docstring'''



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

														return tf.transpose(A__       ,			perm=A__							)
							elif is_jax_tensor(A__							):
														return jnp.transpose(A__       ,			axes=A__							)
							else:
														raise ValueError(f'''Type not supported for transpose: {type(A__							)}.'''							)
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      List[Any]							)		->       int:

							'''simple docstring'''



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

														return tf.reshape(A__       ,			A__							)
							elif is_jax_tensor(A__							):
														return jnp.reshape(A__       ,			A__							)
							else:
														raise ValueError(f'''Type not supported for reshape: {type(A__							)}.'''							)
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      Dict=None							)		->       List[str]:

							'''simple docstring'''



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

														return tf.squeeze(A__       ,			axis=A__							)
							elif is_jax_tensor(A__							):
														return jnp.squeeze(A__       ,			axis=A__							)
							else:
														raise ValueError(f'''Type not supported for squeeze: {type(A__							)}.'''							)
def      lowerCamelCase       (     _UpperCamelCase		:      Dict       ,			_UpperCamelCase		:      str							)		->       int:

							'''simple docstring'''



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

														return tf.expand_dims(A__       ,			axis=A__							)
							elif is_jax_tensor(A__							):
														return jnp.expand_dims(A__       ,			axis=A__							)
							else:
														raise ValueError(f'''Type not supported for expand_dims: {type(A__							)}.'''							)
def      lowerCamelCase       (     _UpperCamelCase		:      Dict							)		->       Dict:

							'''simple docstring'''



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

														return tf.size(A__							)
							elif is_jax_tensor(A__							):
														return array.size
							else:
														raise ValueError(f'''Type not supported for expand_dims: {type(A__							)}.'''							)
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      Optional[int]							)		->       Any:

							'''simple docstring'''



							for key, value in auto_map.items():
														if isinstance(A__       ,			(tuple, list)							):
																					__UpperCAmelCase   :						Union[str, Any]      							=					[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:
																					__UpperCAmelCase   :						Dict      							=					f'''{repo_id}--{value}'''

							return auto_map
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]							)		->       Dict:

							'''simple docstring'''



							for base_class in inspect.getmro(A__							):
														__UpperCAmelCase   :						List[str]      							=					base_class.__module__
														__UpperCAmelCase   :						List[str]      							=					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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			lowerCamelCase_					=      None


lowerCamelCase_					=      logging.get_logger(__name__)


lowerCamelCase_					=      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_					=      {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_					=      {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
lowerCamelCase_					=      ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				VOCAB_FILES_NAMES
	__magic_name__	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__	      =				PRETRAINED_VOCAB_FILES_MAP
	__magic_name__	      =				['''input_ids''', '''attention_mask''']
	__magic_name__	      =				MBartTokenizer

	__magic_name__	      =				[]
	__magic_name__	      =				[]





	def __init__(      self					: List[str]	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: str=None	,	lowerCAmelCase_					: List[Any]="<s>"	,	lowerCAmelCase_					: Optional[Any]="</s>"	,	lowerCAmelCase_					: str="</s>"	,	lowerCAmelCase_					: str="<s>"	,	lowerCAmelCase_					: List[Any]="<unk>"	,	lowerCAmelCase_					: Tuple="<pad>"	,	lowerCAmelCase_					: Union[str, Any]="<mask>"	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: str=None	,	lowerCAmelCase_					: Optional[Any]=None	,	**lowerCAmelCase_					: Any	,	)						->			Tuple:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_     : Union[str, Any]			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else mask_token

				super().__init__(
				    vocab_file=lowerCAmelCase_	,	tokenizer_file=lowerCAmelCase_	,	bos_token=lowerCAmelCase_	,	eos_token=lowerCAmelCase_	,	sep_token=lowerCAmelCase_	,	cls_token=lowerCAmelCase_	,	unk_token=lowerCAmelCase_	,	pad_token=lowerCAmelCase_	,	mask_token=lowerCAmelCase_	,	src_lang=lowerCAmelCase_	,	tgt_lang=lowerCAmelCase_	,	additional_special_tokens=lowerCAmelCase_	,	**lowerCAmelCase_	,	)

				UpperCAmelCase_     : Tuple			     =					vocab_file
				UpperCAmelCase_     : str			     =					False if not self.vocab_file else True

				UpperCAmelCase_     : List[Any]			     =					FAIRSEQ_LANGUAGE_CODES.copy()

				if additional_special_tokens is not None:
							# Only add those special tokens if they are not already there.
							_additional_special_tokens.extend(
							    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

				self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}						)
				UpperCAmelCase_     : Tuple			     =					{
				    lang_code: self.convert_tokens_to_ids(lowerCAmelCase_						) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

				UpperCAmelCase_     : int			     =					src_lang if src_lang is not None else "en_XX"
				UpperCAmelCase_     : Union[str, Any]			     =					self.convert_tokens_to_ids(self._src_lang						)
				UpperCAmelCase_     : Any			     =					tgt_lang
				self.set_src_lang_special_tokens(self._src_lang						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			str:
				return self._src_lang





	@src_lang.setter
	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: str						)						->			None:
				UpperCAmelCase_     : List[Any]			     =					new_src_lang
				self.set_src_lang_special_tokens(self._src_lang						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				UpperCAmelCase_     : List[str]			     =					[self.sep_token_id]
				UpperCAmelCase_     : Dict			     =					[self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: Tuple	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str]	,	lowerCAmelCase_					: Optional[str]	,	**lowerCAmelCase_					: Union[str, Any]						)						->			Dict:
				if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
				UpperCAmelCase_     : str			     =					src_lang
				UpperCAmelCase_     : str			     =					self(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_	,	return_tensors=lowerCAmelCase_	,	**lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[int]			     =					self.convert_tokens_to_ids(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					tgt_lang_id
				return inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: str = "en_XX"	,	lowerCAmelCase_					: Optional[List[str]] = None	,	lowerCAmelCase_					: str = "ro_RO"	,	**lowerCAmelCase_					: Dict	,	)						->			BatchEncoding:
				UpperCAmelCase_     : List[Any]			     =					src_lang
				UpperCAmelCase_     : Tuple			     =					tgt_lang
				return super().prepare_seqaseq_batch(lowerCAmelCase_	,	lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Any:
				return self.set_src_lang_special_tokens(self.src_lang						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Dict:
				return self.set_tgt_lang_special_tokens(self.tgt_lang						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	lowerCAmelCase_					: List[Any]						)						->			None:
				UpperCAmelCase_     : int			     =					self.convert_tokens_to_ids(lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					[]
				UpperCAmelCase_     : str			     =					[self.eos_token_id, self.cur_lang_code]

				UpperCAmelCase_     : Union[str, Any]			     =					self.convert_ids_to_tokens(self.prefix_tokens						)
				UpperCAmelCase_     : List[Any]			     =					self.convert_ids_to_tokens(self.suffix_tokens						)

				UpperCAmelCase_     : Optional[Any]			     =					processors.TemplateProcessing(
				    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,	pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,	self.prefix_tokens + self.suffix_tokens						)						)	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: str						)						->			None:
				UpperCAmelCase_     : Union[str, Any]			     =					self.convert_tokens_to_ids(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					[]
				UpperCAmelCase_     : Optional[int]			     =					[self.eos_token_id, self.cur_lang_code]

				UpperCAmelCase_     : Tuple			     =					self.convert_ids_to_tokens(self.prefix_tokens						)
				UpperCAmelCase_     : Tuple			     =					self.convert_ids_to_tokens(self.suffix_tokens						)

				UpperCAmelCase_     : Optional[Any]			     =					processors.TemplateProcessing(
				    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,	pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,	self.prefix_tokens + self.suffix_tokens						)						)	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str] = None						)						->			Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."						)

				if not os.path.isdir(lowerCAmelCase_						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
							return
				UpperCAmelCase_     : List[str]			     =					os.path.join(
				    lowerCAmelCase_	,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

				if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCAmelCase_						):
							copyfile(self.vocab_file	,	lowerCAmelCase_						)

				return (out_vocab_file,)



