


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


snake_case							:       Optional[Any]     			=      logging.get_logger(__name__)

snake_case							:       Union[str, Any]     			=      '''▁'''

snake_case							:       Dict     			=      {'''vocab_file''': '''prophetnet.tokenizer'''}

snake_case							:       Optional[Any]     			=      {
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

snake_case							:       Dict     			=      {
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

snake_case							:       Union[str, Any]     			=      {
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]   ):
							a__             =     collections.OrderedDict()
							with open(__lowerCAmelCase					,    'r'					,    encoding='utf-8'   ) as reader:
														a__             =     reader.readlines()
							for index, token in enumerate(__lowerCAmelCase   ):
														a__             =     token.rstrip('\n'   )
														a__             =     index
							return vocab







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Union[str, Any]          =			VOCAB_FILES_NAMES
					UpperCAmelCase__					:    List[str]          =			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__					:    Union[str, Any]          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__					:    Optional[Any]          =			['''input_ids''', '''attention_mask''']
					def __init__(      self   :Union[str, Any]			,__snake_case   :List[Any]			,__snake_case   :Tuple="[SEP]"			,__snake_case   :Tuple="[SEP]"			,__snake_case   :str="[SEP]"			,__snake_case   :int="[UNK]"			,__snake_case   :Tuple="[PAD]"			,__snake_case   :Optional[int]="[CLS]"			,__snake_case   :Any="[MASK]"			,__snake_case   :Optional[Dict[str, Any]] = None			,**__snake_case   :Union[str, Any]			,)					->    None:
												a__             =     {} if sp_model_kwargs is None else sp_model_kwargs

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

												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__             =     spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(str(__snake_case      )      )
												a__             =     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__             =     {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

												for i in range(10      ):
																			a__             =     F'[unused{i}]'
																			a__             =     5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												a__             =     12
												a__             =     {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(__snake_case      )
					def __getstate__(      self   :List[str]      )					->    Union[str, Any]:
												a__             =     self.__dict__.copy()
												a__             =     None
												return state
					def __setstate__(      self   :Dict			,__snake_case   :Dict      )					->    List[Any]:
												a__             =     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__             =     {}

												a__             =     spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(self.vocab_file      )
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :List[int]			,__snake_case   :Optional[List[int]] = None			,__snake_case   :bool = False      )					->    List[int]:

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

												if token_ids_a is None:
																			return ([0] * len(__snake_case      )) + [1]
												return ([0] * len(__snake_case      )) + [1] + ([0] * len(__snake_case      )) + [1]
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :List[int]			,__snake_case   :Optional[List[int]] = None      )					->    List[int]:
												a__             =     [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  lowerCamelCase__(      self   :Optional[Any]      )					->    Optional[Any]:
												return len(self.sp_model      ) + self.fairseq_offset
					def  lowerCamelCase__(      self   :str      )					->    List[str]:
												a__             =     {self.convert_ids_to_tokens(__snake_case      ): i for i in range(self.vocab_size      )}
												vocab.update(self.added_tokens_encoder      )
												return vocab
					def  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :str      )					->    str:
												return self.sp_model.encode(__snake_case			,out_type=__snake_case      )
					def  lowerCamelCase__(      self   :Optional[int]			,__snake_case   :str      )					->    int:
												if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
												a__             =     self.sp_model.PieceToId(__snake_case      )

												# 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  lowerCamelCase__(      self   :Any			,__snake_case   :Optional[Any]      )					->    Any:
												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  lowerCamelCase__(      self   :Tuple			,__snake_case   :Any      )					->    Any:
												a__             =     ''.join(__snake_case      ).replace(__snake_case			,' '      ).strip()
												return out_string
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :str			,__snake_case   :Optional[str] = None      )					->    Tuple[str]:
												if not os.path.isdir(__snake_case      ):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
																			return
												a__             =     os.path.join(
												    __snake_case			,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

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

												return (out_vocab_file,)



					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :List[int]			,__snake_case   :Optional[List[int]] = None      )					->    List[int]:

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case							:       str     			=      {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Any     			=      [
						    '''RagModel''',
						    '''RagPreTrainedModel''',
						    '''RagSequenceForGeneration''',
						    '''RagTokenForGeneration''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''TFRagModel''',
						    '''TFRagPreTrainedModel''',
						    '''TFRagSequenceForGeneration''',
						    '''TFRagTokenForGeneration''',
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						snake_case							:       int     			=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




