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


lowercase__		:Tuple			  =   logging.get_logger(__name__)

lowercase__		:List[Any]			  =   {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
       lowercase_	:	Tuple	 ='switch_transformers'
       lowercase_	:	Tuple	 =['past_key_values']
       lowercase_	:	Tuple	 ={'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}




       def __init__(    self       ,A__=3_2_1_2_8       ,A__=7_6_8       ,A__=6_4       ,A__=2_0_4_8       ,A__=6_4       ,A__=1_2       ,A__=3       ,A__=1_2       ,A__=3       ,A__=1_2       ,A__=8       ,A__=False       ,A__=0.01       ,A__="float32"       ,A__=False       ,A__=3_2       ,A__=1_2_8       ,A__=0.1       ,A__=1E-6       ,A__=0.001       ,A__=0.001       ,A__=1.0       ,A__="relu"       ,A__=True       ,A__=False       ,A__=True       ,A__=0       ,A__=1       ,**A__       ,):
            lowercase						     =						vocab_size
            lowercase						     =						d_model
            lowercase						     =						d_kv
            lowercase						     =						d_ff

            lowercase						     =						num_sparse_encoder_layers

            lowercase						     =						num_layers
            lowercase						     =						(
                num_decoder_layers if num_decoder_layers is not None else self.num_layers
            )  # default = symmetry
            lowercase						     =						num_sparse_decoder_layers

            # This tells us, each how many encoder layer we'll have to set a sparse layer.
            if self.num_sparse_encoder_layers > 0:
                 lowercase						     =						self.num_layers // self.num_sparse_encoder_layers
            else:
                 lowercase						     =						self.num_layers  # HACK: this will create 0 sparse layers

            # This tells us, each how many encoder layer we'll have to set a sparse layer.
            if self.num_sparse_decoder_layers > 0:
                 lowercase						     =						self.num_decoder_layers // self.num_sparse_decoder_layers
            else:
                 lowercase						     =						self.num_decoder_layers  # HACK: this will create 0 sparse layers

            lowercase						     =						num_heads
            lowercase						     =						num_experts
            lowercase						     =						expert_capacity
            lowercase						     =						router_bias
            lowercase						     =						router_jitter_noise
            if router_dtype not in ["float32", "float16", "bfloat16"]:
                 raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}')
            lowercase						     =						router_dtype

            lowercase						     =						router_ignore_padding_tokens
            lowercase						     =						relative_attention_num_buckets
            lowercase						     =						relative_attention_max_distance

            lowercase						     =						dropout_rate
            lowercase						     =						layer_norm_epsilon
            lowercase						     =						initializer_factor
            lowercase						     =						feed_forward_proj
            lowercase						     =						use_cache
            lowercase						     =						add_router_probs

            lowercase						     =						router_z_loss_coef
            lowercase						     =						router_aux_loss_coef

            lowercase						     =						self.feed_forward_proj.split('''-''')
            lowercase						     =						act_info[-1]
            lowercase						     =						act_info[0] == '''gated'''

            if len(__lowerCAmelCase) > 1 and act_info[0] != "gated" or len(__lowerCAmelCase) > 2:
                 raise ValueError(
                     f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                     '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                     '''\'gated-gelu\' or \'relu\'''')

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                 lowercase						     =						'''gelu_new'''

            super().__init__(
                pad_token_id=__lowerCAmelCase       ,eos_token_id=__lowerCAmelCase       ,is_encoder_decoder=__lowerCAmelCase       ,**__lowerCAmelCase       ,)






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


lowercase__		:str			  =   logging.get_logger(__name__)

lowercase__		:Any			  =   {"vocab_file": "sentencepiece.bpe.model"}

lowercase__		:Tuple			  =   {
    "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"
        ),
    },
}

lowercase__		:str			  =   {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

lowercase__		:int			  =   "▁"







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Union[str, Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	List[Any]	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Tuple	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




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

												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

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

												lowercase						     =						vocab_file
												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))

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

												lowercase						     =						len(self.sp_model) - 1
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def 			A__				(    self       ,A__       ,A__ = None):

												if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowercase						     =						[self.cls_token_id]
												lowercase						     =						[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):
												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__       ,token_ids_a=A__       ,already_has_special_tokens=A__)

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




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]
												lowercase						     =						[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):
												return len(self.sp_model)




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												return spm_id if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(A__)




							def 			A__				(    self       ,A__):
												lowercase						     =						[]
												lowercase						     =						''''''
												lowercase						     =						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(A__) + token
																						lowercase						     =						True
																						lowercase						     =						[]
																	else:
																						current_sub_tokens.append(A__)
																						lowercase						     =						False
												out_string += self.sp_model.decode(A__)
												return out_string.strip()




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d

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

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)





							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						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__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file       ,A__)
												elif not os.path.isfile(self.vocab_file):
																	with open(A__       ,'''wb''') as fi:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)




