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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {"""vocab_file""": """spiece.model"""}

__snake_case								=  {
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

__snake_case								=  {"""bert_for_seq_generation""": 5_12}




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Dict				       =VOCAB_FILES_NAMES
	A__    :   Any				       =PRETRAINED_VOCAB_FILES_MAP
	A__    :   Tuple				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :   List[int]				       =[]
	A__    :   Tuple				       =["""input_ids""", """attention_mask"""]
	def __init__(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Union[str, Any]="<s>"       ,   UpperCAmelCase_							:   str="</s>"       ,   UpperCAmelCase_							:   str="<unk>"       ,   UpperCAmelCase_							:   int="<pad>"       ,   UpperCAmelCase_							:   Dict="<::::>"       ,   UpperCAmelCase_							:   Optional[Dict[str, Any]] = None       ,   **UpperCAmelCase_							:   Tuple       ,   ):
						SCREAMING_SNAKE_CASE__													= {} if sp_model_kwargs is None else sp_model_kwargs

						# Add extra_ids to the special token list
						super().__init__(
						    bos_token=UpperCAmelCase_       ,   eos_token=UpperCAmelCase_       ,   unk_token=UpperCAmelCase_       ,   pad_token=UpperCAmelCase_       ,   sep_token=UpperCAmelCase_       ,   sp_model_kwargs=self.sp_model_kwargs       ,   **UpperCAmelCase_       ,   )

						SCREAMING_SNAKE_CASE__													= vocab_file

						SCREAMING_SNAKE_CASE__													= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(UpperCAmelCase_							)
	@property
	def      A_				(			self							:   List[Any]							):
						return self.sp_model.get_piece_size()
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= {self.convert_ids_to_tokens(UpperCAmelCase_							): i for i in range(self.vocab_size							)}
						vocab.update(self.added_tokens_encoder							)
						return vocab
	def __getstate__(			self							:   str							):
						SCREAMING_SNAKE_CASE__													= self.__dict__.copy()
						SCREAMING_SNAKE_CASE__													= None
						return state
	def __setstate__(			self							:   Tuple       ,   UpperCAmelCase_							:   str							):
						SCREAMING_SNAKE_CASE__													= d

						# for backward compatibility
						if not hasattr(self       ,   'sp_model_kwargs'							):
											SCREAMING_SNAKE_CASE__													= {}

						SCREAMING_SNAKE_CASE__													= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(self.vocab_file							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   str							):
						return self.sp_model.encode(UpperCAmelCase_       ,   out_type=UpperCAmelCase_							)
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   Dict							):
						return self.sp_model.piece_to_id(UpperCAmelCase_							)
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.sp_model.IdToPiece(UpperCAmelCase_							)
						return token
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= ''
						for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
																out_string += self.sp_model.decode(UpperCAmelCase_							) + token
																SCREAMING_SNAKE_CASE__													= []
											else:
																current_sub_tokens.append(UpperCAmelCase_							)
						out_string += self.sp_model.decode(UpperCAmelCase_							)
						return out_string.strip()





	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Optional[str] = None							):
						if not os.path.isdir(UpperCAmelCase_							):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
											return
						SCREAMING_SNAKE_CASE__													= os.path.join(
						    UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

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

						return (out_vocab_file,)







from collections import OrderedDict
from typing import Mapping

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


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   int				       ="""xmod"""
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[int]=30522       ,   UpperCAmelCase_							:   Union[str, Any]=768       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=3072       ,   UpperCAmelCase_							:   Dict="gelu"       ,   UpperCAmelCase_							:   str=0.1       ,   UpperCAmelCase_							:   Dict=0.1       ,   UpperCAmelCase_							:   Optional[Any]=512       ,   UpperCAmelCase_							:   Any=2       ,   UpperCAmelCase_							:   Optional[Any]=0.02       ,   UpperCAmelCase_							:   Optional[Any]=1e-1_2       ,   UpperCAmelCase_							:   Any=1       ,   UpperCAmelCase_							:   Dict=0       ,   UpperCAmelCase_							:   Optional[int]=2       ,   UpperCAmelCase_							:   Tuple="absolute"       ,   UpperCAmelCase_							:   Union[str, Any]=True       ,   UpperCAmelCase_							:   Dict=None       ,   UpperCAmelCase_							:   Union[str, Any]=False       ,   UpperCAmelCase_							:   int=2       ,   UpperCAmelCase_							:   int=False       ,   UpperCAmelCase_							:   Optional[int]=True       ,   UpperCAmelCase_							:   List[Any]=True       ,   UpperCAmelCase_							:   Union[str, Any]=("en_XX",)       ,   UpperCAmelCase_							:   int=None       ,   **UpperCAmelCase_							:   str       ,   ):
						super().__init__(pad_token_id=UpperCAmelCase_       ,   bos_token_id=UpperCAmelCase_       ,   eos_token_id=UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= vocab_size
						SCREAMING_SNAKE_CASE__													= hidden_size
						SCREAMING_SNAKE_CASE__													= num_hidden_layers
						SCREAMING_SNAKE_CASE__													= num_attention_heads
						SCREAMING_SNAKE_CASE__													= hidden_act
						SCREAMING_SNAKE_CASE__													= intermediate_size
						SCREAMING_SNAKE_CASE__													= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__													= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__													= max_position_embeddings
						SCREAMING_SNAKE_CASE__													= type_vocab_size
						SCREAMING_SNAKE_CASE__													= initializer_range
						SCREAMING_SNAKE_CASE__													= layer_norm_eps
						SCREAMING_SNAKE_CASE__													= position_embedding_type
						SCREAMING_SNAKE_CASE__													= use_cache
						SCREAMING_SNAKE_CASE__													= classifier_dropout
						SCREAMING_SNAKE_CASE__													= pre_norm
						SCREAMING_SNAKE_CASE__													= adapter_reduction_factor
						SCREAMING_SNAKE_CASE__													= adapter_layer_norm
						SCREAMING_SNAKE_CASE__													= adapter_reuse_layer_norm
						SCREAMING_SNAKE_CASE__													= ln_before_adapter
						SCREAMING_SNAKE_CASE__													= list(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= default_language





class 						lowercase__  (						_UpperCAmelCase  ):
	@property
	def      A_				(			self							:   List[Any]							):
						if self.task == "multiple-choice":
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)





