



'''simple docstring'''






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__:   Tuple								=			logging.get_logger(__name__)

UpperCamelCase__:   Optional[int]								=			{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase__:   Optional[int]								=			{
    "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"
        ),
    },
}

UpperCamelCase__:   Dict								=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

UpperCamelCase__:   Tuple								=			"▁"

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       VOCAB_FILES_NAMES
	lowerCamelCase__			 =       PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__			 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__			 =       ["""input_ids""", """attention_mask"""]







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

								UpperCAmelCase :       Union[str, Any] 							=		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__snake_case		,			eos_token=__snake_case		,			unk_token=__snake_case		,			sep_token=__snake_case		,			cls_token=__snake_case		,			pad_token=__snake_case		,			mask_token=__snake_case		,			sp_model_kwargs=self.sp_model_kwargs		,			**__snake_case		,			)

								UpperCAmelCase :       Optional[int] 							=		vocab_file
								UpperCAmelCase :       int 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(__snake_case		)		)

								UpperCAmelCase :       int 							=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								UpperCAmelCase :       Optional[Any] 							=		len(self.sp_model		) - 1
								UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def      A	(						self			:     Tuple		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase :       Union[str, Any] 							=		[self.cls_token_id]
								UpperCAmelCase :       Optional[Any] 							=		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







	def      A	(						self			:     int		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		,			__snake_case			:     bool = False		)			->		List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__snake_case		,			token_ids_a=__snake_case		,			already_has_special_tokens=__snake_case		)

								if token_ids_a is None:
															return [1] + ([0] * len(__snake_case		)) + [1]
								return [1] + ([0] * len(__snake_case		)) + [1, 1] + ([0] * len(__snake_case		)) + [1]







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Tuple 							=		[self.sep_token_id]
								UpperCAmelCase :       Tuple 							=		[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]







	@property
	def      A	(						self			:     Dict		)			->		Optional[int]:
								return len(self.sp_model		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Optional[Any] 							=		{self.convert_ids_to_tokens(__snake_case		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     str		)			->		List[str]:
								return self.sp_model.encode(__snake_case		,			out_type=__snake_case		)







	def      A	(						self			:     int		,			__snake_case			:     int		)			->		int:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase :       Optional[Any] 							=		self.sp_model.PieceToId(__snake_case		)

								return spm_id if spm_id else self.unk_token_id







	def      A	(						self			:     int		,			__snake_case			:     Any		)			->		List[Any]:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		[]
								UpperCAmelCase :       int 							=		''''''
								UpperCAmelCase :       Union[str, Any] 							=		False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(__snake_case		) + token
																						UpperCAmelCase :       str 							=		True
																						UpperCAmelCase :       List[str] 							=		[]
															else:
																						current_sub_tokens.append(__snake_case		)
																						UpperCAmelCase :       Optional[int] 							=		False
								out_string += self.sp_model.decode(__snake_case		)
								return out_string.strip()







	def __getstate__(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.__dict__.copy()
								UpperCAmelCase :       Any 							=		None
								return state







	def __setstate__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		)			->		List[Any]:
								UpperCAmelCase :       Dict 							=		d

								# for backward compatibility
								if not hasattr(self		,			'''sp_model_kwargs'''		):
															UpperCAmelCase :       Optional[Any] 							=		{}

								UpperCAmelCase :       Any 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
															return
								UpperCAmelCase :       Union[str, Any] 							=		os.path.join(
								    __snake_case		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(__snake_case		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file		,			__snake_case		)
								elif not os.path.isfile(self.vocab_file		):
															with open(__snake_case		,			'''wb'''		) as fi:
																						UpperCAmelCase :       Any 							=		self.sp_model.serialized_model_proto()
																						fi.write(__snake_case		)

								return (out_vocab_file,)










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
    from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Tuple        =  "bloom"
       UpperCAmelCase__				:					List[Any]        =  ["past_key_values"]
       UpperCAmelCase__				:					str        =  {
           "num_hidden_layers": "n_layer",
           "num_attention_heads": "n_head",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=25_0880,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=False,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  str							=	vocab_size
          # Backward compatibility with n_embed kwarg
          UpperCamelCase    :  Optional[Any]							=	kwargs.pop('n_embed',			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	hidden_size if n_embed is None else n_embed
          UpperCamelCase    :  Tuple							=	n_layer
          UpperCamelCase    :  Dict							=	n_head
          UpperCamelCase    :  List[Any]							=	layer_norm_epsilon
          UpperCamelCase    :  Optional[int]							=	initializer_range
          UpperCamelCase    :  int							=	use_cache
          UpperCamelCase    :  int							=	pretraining_tp
          UpperCamelCase    :  Optional[int]							=	apply_residual_connection_post_layernorm
          UpperCamelCase    :  str							=	hidden_dropout
          UpperCamelCase    :  str							=	attention_dropout

          UpperCamelCase    :  List[Any]							=	bos_token_id
          UpperCamelCase    :  Tuple							=	eos_token_id
          UpperCamelCase    :  Union[str, Any]							=	slow_but_exact

          super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )






class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  version.parse("1.12"							)
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = "default",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			)     ->		Any:
          super().__init__(SCREAMING_SNAKE_CASE_,			task=SCREAMING_SNAKE_CASE_,			patching_specs=SCREAMING_SNAKE_CASE_,			use_past=SCREAMING_SNAKE_CASE_       )
          if not getattr(self._config,			'pad_token_id',			SCREAMING_SNAKE_CASE_       ):
             # TODO: how to do that better?
             UpperCamelCase    :  Tuple							=	0
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  str							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs',			inverted_values_shape=SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Union[str, Any]							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  Optional[int]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_layer
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_head
       @property
       def 					snake_case_       (			self       )     ->		float:
          return 1e-3
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Mapping[str, Any]:
          UpperCamelCase    :  Dict							=	super(SCREAMING_SNAKE_CASE_,			self       ).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,			batch_size=SCREAMING_SNAKE_CASE_,			seq_length=SCREAMING_SNAKE_CASE_,			is_pair=SCREAMING_SNAKE_CASE_,			framework=SCREAMING_SNAKE_CASE_       )

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase    :  Any							=	OrderedDict({'input_ids': common_inputs['input_ids']}       )

          # Need to add the past_keys
          if self.use_past:
             if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'       )
             else:
                import torch

                UpperCamelCase	,     UpperCamelCase    :  int							=	common_inputs['input_ids'].shape
                # Not using the same length for past_key_values
                UpperCamelCase    :  Any							=	seqlen + 2
                UpperCamelCase    :  Optional[int]							=	self._config.hidden_size // self.num_attention_heads
                UpperCamelCase    :  Any							=	(
                    batch * self.num_attention_heads,
                    head_dim,
                    past_key_values_length,
                )
                UpperCamelCase    :  Optional[Any]							=	(
                    batch * self.num_attention_heads,
                    past_key_values_length,
                    head_dim,
                )
                UpperCamelCase    :  List[str]							=	[
                    (torch.zeros(SCREAMING_SNAKE_CASE_       ), torch.zeros(SCREAMING_SNAKE_CASE_       )) for _ in range(self.num_layers       )
                ]

          UpperCamelCase    :  str							=	common_inputs['attention_mask']
          if self.use_past:
             UpperCamelCase    :  int							=	ordered_inputs['attention_mask'].dtype
             UpperCamelCase    :  List[Any]							=	torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )],			dim=1       )

          return ordered_inputs






       @property
       def 					snake_case_       (			self       )     ->		int:
          return 13



