



'''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_				(							__magic_name__   ):





	def __get__(						self	,	A	,	A=None					)    ->    List[Any]:







								if obj is None:
															return self
								if self.fget is None:
															raise AttributeError("""unreadable attribute"""					)
								UpperCAmelCase  :	List[Any]							  =	"""__cached_""" + self.fget.__name__
								UpperCAmelCase  :	Optional[Any]							  =	getattr(__lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase					)
								if cached is None:
															UpperCAmelCase  :	str							  =	self.fget(__lowerCAmelCase					)
															setattr(__lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase					)
								return cached




def    __lowerCamelCase						(      _lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	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						(      _lowercase	)   ->    List[str]:







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

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

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

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

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

							return isinstance(__snake_case			,       np.ndarray	)



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							return isinstance(__snake_case			,       np.ndarray	)



def    __lowerCamelCase						(      _lowercase	)   ->    Dict:
							return _is_numpy(__snake_case	)



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							import torch

							return isinstance(__snake_case			,       torch.Tensor	)



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[int]:
							return False if not is_torch_available() else _is_torch(__snake_case	)



def    __lowerCamelCase						(      _lowercase	)   ->    str:
							import torch

							return isinstance(__snake_case			,       torch.device	)



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[int]:
							return False if not is_torch_available() else _is_torch_device(__snake_case	)



def    __lowerCamelCase						(      _lowercase	)   ->    List[Any]:
							import torch

							if isinstance(__snake_case			,       __snake_case	):
														if hasattr(__snake_case			,       __snake_case	):
																					UpperCAmelCase  :	int							  =	getattr(__snake_case			,       __snake_case	)
														else:
																					return False
							return isinstance(__snake_case			,       torch.dtype	)



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							return False if not is_torch_available() else _is_torch_dtype(__snake_case	)



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[Any]:
							import tensorflow as tf

							return isinstance(__snake_case			,       tf.Tensor	)



def    __lowerCamelCase						(      _lowercase	)   ->    Union[str, Any]:
							return False if not is_tf_available() else _is_tensorflow(__snake_case	)



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[int]:
							import tensorflow as tf

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



def    __lowerCamelCase						(      _lowercase	)   ->    str:
							return False if not is_tf_available() else _is_tf_symbolic_tensor(__snake_case	)



def    __lowerCamelCase						(      _lowercase	)   ->    List[str]:
							import jax.numpy as jnp  # noqa: F811

							return isinstance(__snake_case			,       jnp.ndarray	)



def    __lowerCamelCase						(      _lowercase	)   ->    str:
							return False if not is_flax_available() else _is_jax(__snake_case	)



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[Any]:







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



def    __lowerCamelCase						(      _lowercase	)   ->    Any:







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

class     UpperCamelCase_				(							__magic_name__   ):





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





	def __delitem__(						self	,	*A	,	**A					)    ->    Dict:
								raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''					)





	def 				_lowercase(						self	,	*A	,	**A					)    ->    List[Any]:
								raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''					)





	def 				_lowercase(						self	,	*A	,	**A					)    ->    Union[str, Any]:
								raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''					)





	def 				_lowercase(						self	,	*A	,	**A					)    ->    Any:
								raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''					)





	def __getitem__(						self	,	A					)    ->    str:







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





	def __setattr__(						self	,	A	,	A					)    ->    Dict:







								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	,	A	,	A					)    ->    Optional[int]:
								super().__setitem__(__lowerCAmelCase	,	__lowerCAmelCase					)
								# Don't call self.__setattr__ to avoid recursion errors
								super().__setattr__(__lowerCAmelCase	,	__lowerCAmelCase					)






	def 				_lowercase(						self					)    ->    str:
								return tuple(self[k] for k in self.keys()					)




class     UpperCamelCase_				(							__magic_name__   ,  __magic_name__   ):





	@classmethod
	def 				_lowercase(						cls	,	A					)    ->    int:
								raise ValueError(
								    f'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()					)}'''					)




class     UpperCamelCase_				(							__magic_name__   ):
	lowercase    =				'longest'
	lowercase    =				'max_length'
	lowercase    =				'do_not_pad'




class     UpperCamelCase_				(							__magic_name__   ):
	lowercase    =				'pt'
	lowercase    =				'tf'
	lowercase    =				'np'
	lowercase    =				'jax'




class     UpperCamelCase_				:





	def __init__(						self	,	A					)    ->    Optional[int]:
								UpperCAmelCase  :	List[Any]							  =	context_managers
								UpperCAmelCase  :	Optional[int]							  =	ExitStack()





	def __enter__(						self					)    ->    str:







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






	def __exit__(						self	,	*A	,	**A					)    ->    Dict:
								self.stack.__exit__(*__lowerCAmelCase	,	**__lowerCAmelCase					)




def    __lowerCamelCase						(      _lowercase	)   ->    str:
							UpperCAmelCase  :	List[str]							  =	infer_framework(__snake_case	)
							if framework == "tf":
														UpperCAmelCase  :	Tuple							  =	inspect.signature(model_class.call	)  # TensorFlow models
							elif framework == "pt":
														UpperCAmelCase  :	str							  =	inspect.signature(model_class.forward	)  # PyTorch models
							else:
														UpperCAmelCase  :	str							  =	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						(      _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	Optional[Any]							  =	model_class.__name__
							UpperCAmelCase  :	Union[str, Any]							  =	infer_framework(__snake_case	)
							if framework == "tf":
														UpperCAmelCase  :	Dict							  =	inspect.signature(model_class.call	)  # TensorFlow models
							elif framework == "pt":
														UpperCAmelCase  :	Optional[int]							  =	inspect.signature(model_class.forward	)  # PyTorch models
							else:
														UpperCAmelCase  :	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						(      _lowercase			,       _lowercase = ""			,       _lowercase = "."	)   ->    Union[str, Any]:







							def _flatten_dict(_lowercase			,       _lowercase=""			,       _lowercase="."	):
														for k, v in d.items():
																					UpperCAmelCase  :	str							  =	str(__snake_case	) + delimiter + str(__snake_case	) if parent_key else k
																					if v and isinstance(__snake_case			,       __snake_case	):
																												yield from flatten_dict(__snake_case			,       __snake_case			,       delimiter=__snake_case	).items()
																					else:
																												yield key, v

							return dict(_flatten_dict(__snake_case			,       __snake_case			,       __snake_case	)	)



@contextmanager
def    __lowerCamelCase						(      _lowercase			,       _lowercase = False	)   ->    List[Any]:







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



def    __lowerCamelCase						(      _lowercase			,       _lowercase=None	)   ->    List[Any]:







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

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[str]:







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

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase=None	)   ->    Union[str, Any]:







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

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    int:







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

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



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[int]:







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

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:







							for key, value in auto_map.items():
														if isinstance(__snake_case			,       (tuple, list)	):
																					UpperCAmelCase  :	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:
																					UpperCAmelCase  :	str							  =	F'''{repo_id}--{value}'''

							return auto_map



def    __lowerCamelCase						(      _lowercase	)   ->    Dict:







							for base_class in inspect.getmro(__snake_case	):
														UpperCAmelCase  :	int							  =	base_class.__module__
														UpperCAmelCase  :	List[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'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
							from .tokenization_barthez import BarthezTokenizer
else:
							a     :    int      						=					None

a     :    List[Any]      						=					logging.get_logger(__name__)

a     :    Dict      						=					{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a     :    Union[str, Any]      						=					{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

a     :    List[Any]      						=					{
    """moussaKam/mbarthez""": 1_0_2_4,
    """moussaKam/barthez""": 1_0_2_4,
    """moussaKam/barthez-orangesum-title""": 1_0_2_4,
}

a     :    int      						=					"""▁"""

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





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

								super().__init__(
								    A	,	tokenizer_file=A	,	bos_token=A	,	eos_token=A	,	unk_token=A	,	sep_token=A	,	cls_token=A	,	pad_token=A	,	mask_token=A	,	**A	,	)

								UpperCAmelCase  :	Union[str, Any]							  =	vocab_file
								UpperCAmelCase  :	int							  =	False if not self.vocab_file else True





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase  :	Optional[int]							  =	[self.cls_token_id]
								UpperCAmelCase  :	List[str]							  =	[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Optional[int]							  =	[self.sep_token_id]
								UpperCAmelCase  :	List[Any]							  =	[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 				_lowercase(						self	,	A	,	A = 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(A					):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								UpperCAmelCase  :	str							  =	os.path.join(
								    A	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

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

								return (out_vocab_file,)

