import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


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

lowerCamelCase_				:		Tuple       						=       {
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCamelCase_				:		str       						=       {
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

lowerCamelCase_				:		Any       						=       {"""Helsinki-NLP/opus-mt-en-de""": 512}
lowerCamelCase_				:		Dict       						=       {}


class 						a__							(   _UpperCamelCase		):
			A__				:   Dict								=			VOCAB_FILES_NAMES
			A__				:   List[str]								=			PRETRAINED_VOCAB_FILES_MAP
			A__				:   List[Any]								=			PRETRAINED_INIT_CONFIGURATION
			A__				:   List[Any]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__				:   List[Any]								=			['input_ids', 'attention_mask']
			A__				:   Dict								=			re.compile('>>.+<<'		)  # type: re.Pattern



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase="<unk>"      ,						UpperCAmelCase="</s>"      ,						UpperCAmelCase="<pad>"      ,						UpperCAmelCase=5_1_2      ,						UpperCAmelCase = None      ,						UpperCAmelCase=False      ,						**UpperCAmelCase      ,						) ->    None:
								__a							=   {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
								    source_lang=__a      ,						target_lang=__a      ,						unk_token=__a      ,						eos_token=__a      ,						pad_token=__a      ,						model_max_length=__a      ,						sp_model_kwargs=self.sp_model_kwargs      ,						target_vocab_file=__a      ,						separate_vocabs=__a      ,						**__a      ,						)
								assert Path(__a    ).exists(), f'''cannot find spm source {source_spm}'''

								__a							=   separate_vocabs
								__a							=   load_json(__a    )
								if self.unk_token not in self.encoder:
													raise KeyError('<unk> token must be in vocab'    )
								assert self.pad_token in self.encoder

								if separate_vocabs:
													__a							=   load_json(__a    )
													__a							=   {v: k for k, v in self.target_encoder.items()}
													__a							=   []
								else:
													__a							=   {v: k for k, v in self.encoder.items()}
													__a							=   [k for k in self.encoder if k.startswith('>>'    ) and k.endswith('<<'    )]

								__a							=   source_lang
								__a							=   target_lang
								__a							=   [source_spm, target_spm]

								# load SentencePiece model for pre-processing
								__a							=   load_spm(__a      ,						self.sp_model_kwargs    )
								__a							=   load_spm(__a      ,						self.sp_model_kwargs    )
								__a							=   self.spm_source
								__a							=   self.encoder

								# Multilingual target side: default to using first supported language code.

								self._setup_normalizer()



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:





								try:
													from sacremoses import MosesPunctNormalizer

													__a							=   MosesPunctNormalizer(self.source_lang    ).normalize
								except (ImportError, FileNotFoundError):
													warnings.warn('Recommended: pip install sacremoses.'    )
													__a							=   lambda UpperCAmelCase    : x



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    str:
								return self.punc_normalizer(__a    ) if x else ""



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    List[Any]:
								return self.current_encoder.get(__a      ,						self.current_encoder[self.unk_token]    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								__a							=   self.language_code_re.match(__a    )
								__a							=   [match.group(0    )] if match else []
								return code, self.language_code_re.sub(''      ,						__a    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    List[str]:
								__a					,      __a							=   self.remove_language_code(__a    )
								__a							=   self.current_spm.encode(__a      ,						out_type=__a    )
								return code + pieces



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    str:
								return self.decoder.get(__a      ,						self.unk_token    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						**UpperCAmelCase    ) ->    List[Any]:
								return super().batch_decode(__a      ,						**__a    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						**UpperCAmelCase    ) ->    Any:
								return super().decode(__a      ,						**__a    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    str:
								__a							=   self.spm_source if self._decode_use_source_tokenizer else self.spm_target
								__a							=   []
								__a							=   ''
								for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																		out_string += sp_model.decode_pieces(__a    ) + token + " "
																		__a							=   []
													else:
																		current_sub_tokens.append(__a    )
								out_string += sp_model.decode_pieces(__a    )
								return out_string.strip()



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=None    ) ->    List[int]:





								if token_ids_a is None:
													return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								__a							=   self.spm_source
								__a							=   self.encoder



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a							=   self.spm_target
								if self.separate_vocabs:
													__a							=   self.target_encoder



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								return len(self.encoder    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = None    ) ->    Tuple[str]:





								if not os.path.isdir(__a    ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
													return
								__a							=   []

								if self.separate_vocabs:
													__a							=   os.path.join(
													    __a      ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']      ,						)
													__a							=   os.path.join(
													    __a      ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']      ,						)
													save_json(self.encoder      ,						__a    )
													save_json(self.target_encoder      ,						__a    )
													saved_files.append(__a    )
													saved_files.append(__a    )
								else:
													__a							=   os.path.join(
													    __a      ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']    )
													save_json(self.encoder      ,						__a    )
													saved_files.append(__a    )

								for spm_save_filename, spm_orig_path, spm_model in zip(
								    [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]      ,						self.spm_files      ,						[self.spm_source, self.spm_target]      ,						):
													__a							=   os.path.join(
													    __a      ,						(filename_prefix + '-' if filename_prefix else '') + spm_save_filename    )
													if os.path.abspath(__a    ) != os.path.abspath(__a    ) and os.path.isfile(__a    ):
																		copyfile(__a      ,						__a    )
																		saved_files.append(__a    )
													elif not os.path.isfile(__a    ):
																		with open(__a      ,						'wb'    ) as fi:
																							__a							=   spm_model.serialized_model_proto()
																							fi.write(__a    )
																		saved_files.append(__a    )

								return tuple(__a    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								return self.get_src_vocab()



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								return dict(self.encoder      ,						**self.added_tokens_encoder    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								return dict(self.target_encoder      ,						**self.added_tokens_decoder    )



			def __getstate__(						self    ) ->    Dict:
								__a							=   self.__dict__.copy()
								state.update(
								    {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}    )
								return state



			def __setstate__(						self      ,						UpperCAmelCase    ) ->    None:
								__a							=   d

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

								__a					,      __a							=   (load_spm(__a      ,						self.sp_model_kwargs    ) for f in self.spm_files)
								__a							=   self.spm_source
								self._setup_normalizer()



			def __SCREAMING_SNAKE_CASE							(						self      ,						*UpperCAmelCase      ,						**UpperCAmelCase    ) ->    Optional[Any]:
								return 1



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    List[str]:
								__a							=   set(self.all_special_ids    )  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special
								return [1 if x in all_special_ids else 0 for x in seq]







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = None      ,						UpperCAmelCase = False    ) ->    List[int]:





								if already_has_special_tokens:
													return self._special_token_mask(__a    )
								elif token_ids_a is None:
													return self._special_token_mask(__a    ) + [1]
								else:
													return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]






def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   sentencepiece.SentencePieceProcessor(**snake_case__						)
					spm.Load(snake_case__						)
					return spm
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					with open(snake_case__      ,				'w'						) as f:
										json.dump(snake_case__      ,				snake_case__      ,				indent=2						)



def 							lowerCAmelCase(	__lowerCamelCase						):
					with open(snake_case__      ,				'r'						) as f:
										return json.load(snake_case__						)







from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase			       =			logging.get_logger(__name__)

_UpperCamelCase			       =			{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""rembert"""




 def __init__(self       ,       __a=250300       ,       __a=1152       ,       __a=32       ,       __a=18       ,       __a=256       ,       __a=1664       ,       __a=4608       ,       __a="gelu"       ,       __a=0.0       ,       __a=0.0       ,       __a=0.1       ,       __a=512       ,       __a=2       ,       __a=0.02       ,       __a=1E-1_2       ,       __a=True       ,       __a=0       ,       __a=312       ,       __a=313       ,       **__a       ,       ) ->					str:






  """simple docstring"""





  super().__init__(pad_token_id=__a       ,       bos_token_id=__a       ,       eos_token_id=__a       ,       **__a )

  UpperCAmelCase__							=				vocab_size
  UpperCAmelCase__							=				input_embedding_size
  UpperCAmelCase__							=				output_embedding_size
  UpperCAmelCase__							=				max_position_embeddings
  UpperCAmelCase__							=				hidden_size
  UpperCAmelCase__							=				num_hidden_layers
  UpperCAmelCase__							=				num_attention_heads
  UpperCAmelCase__							=				intermediate_size
  UpperCAmelCase__							=				hidden_act
  UpperCAmelCase__							=				hidden_dropout_prob
  UpperCAmelCase__							=				attention_probs_dropout_prob
  UpperCAmelCase__							=				classifier_dropout_prob
  UpperCAmelCase__							=				initializer_range
  UpperCAmelCase__							=				type_vocab_size
  UpperCAmelCase__							=				layer_norm_eps
  UpperCAmelCase__							=				use_cache
  UpperCAmelCase__							=				False







class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 @property
 def   UpperCamelCase__							(self ) ->					Mapping[str, Mapping[int, str]]:






  """simple docstring"""





  if self.task == "multiple-choice":
   UpperCAmelCase__							=				{0: 'batch', 1: 'choice', 2: 'sequence'}
  else:
   UpperCAmelCase__							=				{0: 'batch', 1: 'sequence'}
  return OrderedDict(
      [
          ('input_ids', dynamic_axis),
          ('attention_mask', dynamic_axis),
          ('token_type_ids', dynamic_axis),
      ] )




 @property
 def   UpperCamelCase__							(self ) ->					float:






  """simple docstring"""





  return 1E-4



