



"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= """▁"""

SCREAMING_SNAKE_CASE_				= {"""vocab_file""": """prophetnet.tokenizer"""}

SCREAMING_SNAKE_CASE_				= {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

SCREAMING_SNAKE_CASE_				= {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

SCREAMING_SNAKE_CASE_				= {
    """microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Union[str, Any]:
		a_     :      Optional[int] 						=     collections.OrderedDict()
		with open(SCREAMING_SNAKE_CASE__, "r", encoding="utf-8"	) as reader:
				a_     :      Optional[Any] 						=     reader.readlines()
		for index, token in enumerate(SCREAMING_SNAKE_CASE__	):
				a_     :      Union[str, Any] 						=     token.rstrip("\n"	)
				a_     :      Tuple 						=     index
		return vocab




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					VOCAB_FILES_NAMES
							__lowerCAmelCase    					=					PRETRAINED_VOCAB_FILES_MAP
							__lowerCAmelCase    					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCAmelCase    					=					["input_ids", "attention_mask"]






							def __init__(   self			,     a_			,     a_="[SEP]"			,     a_="[SEP]"			,     a_="[SEP]"			,     a_="[UNK]"			,     a_="[PAD]"			,     a_="[CLS]"			,     a_="[MASK]"			,     a_ = None			,     **a_			,     ):
									a_     :      Optional[int] 						=     {} if sp_model_kwargs is None else sp_model_kwargs

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

									try:
											import sentencepiece as spm
									except ImportError:
											logger.warning(
											    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
											    " pip install sentencepiece"      )
											raise

									a_     :      Optional[int] 						=     spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(str(a_      )      )
									a_     :      Dict 						=     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'

									# put special tokens and [unused] tokens into the vocab
									a_     :      Tuple 						=     {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

									for i in range(1_0      ):
											a_     :      Tuple 						=     F"""[unused{i}]"""
											a_     :      str 						=     5 + i

									# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
									a_     :      Dict 						=     1_2
									a_     :      Any 						=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
									for k in self.fairseq_tokens_to_ids.keys():
											self.unique_no_split_tokens.append(a_      )






							def __getstate__(   self      ):
									a_     :      Tuple 						=     self.__dict__.copy()
									a_     :      str 						=     None
									return state






							def __setstate__(   self			,     a_      ):
									a_     :      List[str] 						=     d
									try:
											import sentencepiece as spm
									except ImportError:
											logger.warning(
											    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
											    " pip install sentencepiece"      )
											raise

									# for backward compatibility
									if not hasattr(self			,     "sp_model_kwargs"      ):
											a_     :      Optional[int] 						=     {}

									a_     :      List[Any] 						=     spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(self.vocab_file      )






							def snake_case_  (   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 ([0] * len(a_      )) + [1]
									return ([0] * len(a_      )) + [1] + ([0] * len(a_      )) + [1]






							def snake_case_  (   self			,     a_			,     a_ = None      ):
									a_     :      Any 						=     [self.sep_token_id]

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






							@property
							def snake_case_  (   self      ):
									return len(self.sp_model      ) + self.fairseq_offset






							def snake_case_  (   self      ):
									a_     :      Any 						=     {self.convert_ids_to_tokens(a_      ): i for i in range(self.vocab_size      )}
									vocab.update(self.added_tokens_encoder      )
									return vocab






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






							def snake_case_  (   self			,     a_      ):
									if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
									a_     :      Any 						=     self.sp_model.PieceToId(a_      )

									# 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 snake_case_  (   self			,     a_      ):
									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 snake_case_  (   self			,     a_      ):
									a_     :      List[Any] 						=     "".join(a_      ).replace(a_			,     " "      ).strip()
									return out_string






							def snake_case_  (   self			,     a_			,     a_ = None      ):
									if not os.path.isdir(a_      ):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
											return
									a_     :      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_      ) 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:
													a_     :      List[str] 						=     self.sp_model.serialized_model_proto()
													fi.write(a_      )

									return (out_vocab_file,)






							def snake_case_  (   self			,     a_			,     a_ = None      ):

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




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"mra"






							def __init__(   self			,     a_=5_0_2_6_5			,     a_=7_6_8			,     a_=1_2			,     a_=1_2			,     a_=3_0_7_2			,     a_="gelu"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=1			,     a_=0.02			,     a_=1e-5			,     a_="absolute"			,     a_=4			,     a_="full"			,     a_=0			,     a_=0			,     a_=1			,     a_=0			,     a_=2			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     **a_      )

									a_     :      Optional[int] 						=     vocab_size
									a_     :      Dict 						=     max_position_embeddings
									a_     :      str 						=     hidden_size
									a_     :      Optional[int] 						=     num_hidden_layers
									a_     :      List[Any] 						=     num_attention_heads
									a_     :      List[str] 						=     intermediate_size
									a_     :      Tuple 						=     hidden_act
									a_     :      List[Any] 						=     hidden_dropout_prob
									a_     :      List[str] 						=     attention_probs_dropout_prob
									a_     :      List[Any] 						=     initializer_range
									a_     :      Dict 						=     type_vocab_size
									a_     :      Union[str, Any] 						=     layer_norm_eps
									a_     :      List[str] 						=     position_embedding_type
									a_     :      Union[str, Any] 						=     block_per_row
									a_     :      Tuple 						=     approx_mode
									a_     :      Optional[Any] 						=     initial_prior_first_n_blocks
									a_     :      List[str] 						=     initial_prior_diagonal_n_blocks