

"""simple docstring"""




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


A_				:      List[str]           =			logging.get_logger(__name__)

A_				:      Dict           =			"▁"

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

A_				:      Union[str, Any]           =			{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

A_				:      List[str]           =			{
    "facebook/xglm-564M": 2_048,
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[int]       		=				VOCAB_FILES_NAMES
			lowerCamelCase__ :		int       		=				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__ :		Dict       		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__ :		Optional[Any]       		=				['input_ids', 'attention_mask']

			def __init__(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Tuple="<s>"     ,					__UpperCAmelCase					:    Optional[int]="</s>"     ,					__UpperCAmelCase					:    Optional[int]="</s>"     ,					__UpperCAmelCase					:    Optional[int]="<s>"     ,					__UpperCAmelCase					:    List[Any]="<unk>"     ,					__UpperCAmelCase					:    Dict="<pad>"     ,					__UpperCAmelCase					:    Optional[Dict[str, Any]] = None     ,					**__UpperCAmelCase					:    Union[str, Any]     ,					) ->     None:
						SCREAMING_SNAKE_CASE__										=   {} if sp_model_kwargs is None else sp_model_kwargs

						# Compatibility with the original tokenizer
						SCREAMING_SNAKE_CASE__										=   7
						SCREAMING_SNAKE_CASE__										=   [F"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

						SCREAMING_SNAKE_CASE__										=   kwargs.get("""additional_special_tokens"""     ,					[]							)
						kwargs["additional_special_tokens"] += [
						    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
						]

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

						SCREAMING_SNAKE_CASE__										=   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(str(__UpperCAmelCase							)							)
						SCREAMING_SNAKE_CASE__										=   vocab_file

						# Original fairseq vocab and spm vocab must be "aligned":
						# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
						# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
						# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
						# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

						# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
						SCREAMING_SNAKE_CASE__										=   1

						# Mimic fairseq token-to-id alignment for the first 4 token
						SCREAMING_SNAKE_CASE__										=   {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

						SCREAMING_SNAKE_CASE__										=   len(self.sp_model							)
						SCREAMING_SNAKE_CASE__										=   {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
						self.fairseq_tokens_to_ids.update(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def __getstate__(       self					:    Optional[int]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.__dict__.copy()
						SCREAMING_SNAKE_CASE__										=   None
						SCREAMING_SNAKE_CASE__										=   self.sp_model.serialized_model_proto()
						return state

			def __setstate__(       self					:    Tuple     ,					__UpperCAmelCase					:    str							) ->     Any:
						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.LoadFromSerializedProto(self.sp_model_proto							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    List[int]     ,					__UpperCAmelCase					:    Optional[List[int]] = None							) ->     List[int]:

						if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
						SCREAMING_SNAKE_CASE__										=   [self.sep_token_id]
						return sep + token_ids_a + sep + sep + token_ids_a

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    List[int]     ,					__UpperCAmelCase					:    Optional[List[int]] = None     ,					__UpperCAmelCase					:    bool = False							) ->     List[int]:

						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__UpperCAmelCase     ,					token_ids_a=__UpperCAmelCase     ,					already_has_special_tokens=__UpperCAmelCase							)

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    List[int]     ,					__UpperCAmelCase					:    Optional[List[int]] = None							) ->     List[int]:
						SCREAMING_SNAKE_CASE__										=   [self.sep_token_id]

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

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Optional[Any]:
						return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[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        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    str							) ->     List[str]:
						return self.sp_model.encode(__UpperCAmelCase     ,					out_type=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    List[str]							) ->     Optional[Any]:
						if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
						SCREAMING_SNAKE_CASE__										=   self.sp_model.PieceToId(__UpperCAmelCase							)

						# Need to return unknown token if the SP model returned 0
						return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    Optional[Any]							) ->     List[str]:
						if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[int]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   """""".join(__UpperCAmelCase							).replace(__UpperCAmelCase     ,					""" """							).strip()
						return out_string





			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Optional[str] = None							) ->     Tuple[str]:
						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,)





"""simple docstring"""




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


A_				:      Union[str, Any]           =			{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		int       		=				'ernie_m'
			lowerCamelCase__ :		Dict[str, str]       		=				{"dropout": "classifier_dropout", "num_classes": "num_labels"}

			def __init__(       self					:    List[str]     ,					__UpperCAmelCase					:    int = 2_5_0_0_0_2     ,					__UpperCAmelCase					:    int = 7_6_8     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 3_0_7_2     ,					__UpperCAmelCase					:    str = "gelu"     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    int = 5_1_4     ,					__UpperCAmelCase					:    float = 0.02     ,					__UpperCAmelCase					:    int = 1     ,					__UpperCAmelCase					:    float = 1e-05     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					__UpperCAmelCase					:    List[Any]=False     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					**__UpperCAmelCase					:    Any     ,					) ->     Optional[Any]:
						super().__init__(pad_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__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   classifier_dropout
						SCREAMING_SNAKE_CASE__										=   is_decoder
						SCREAMING_SNAKE_CASE__										=   act_dropout


