





"""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


a_			=      logging.get_logger(__name__)

a_			=      '▁'

a_			=      {'vocab_file': 'prophetnet.tokenizer'}

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

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

a_			=      {
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}







def 		__lowercase					(       snake_case_  :  Union[str, Any]						)       ->Any:



				'''simple docstring'''







				__A   :						List[str]									=					collections.OrderedDict()
				with open(snake_case_    ,'''r'''    ,encoding='''utf-8'''						) as reader:
								__A   :						str									=					reader.readlines()
				for index, token in enumerate(snake_case_						):
								__A   :						Tuple									=					token.rstrip('''\n'''						)
								__A   :						int									=					index
				return vocab






class 		__snake_case (		_lowerCAmelCase					):





	"""simple docstring"""


	_lowerCamelCase				=					VOCAB_FILES_NAMES
	_lowerCamelCase				=					PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase				=					["input_ids", "attention_mask"]

	def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase="[SEP]" ,						__lowerCamelCase="[SEP]" ,						__lowerCamelCase="[SEP]" ,						__lowerCamelCase="[UNK]" ,						__lowerCamelCase="[PAD]" ,						__lowerCamelCase="[CLS]" ,						__lowerCamelCase="[MASK]" ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



					'''simple docstring'''




					__A   :						Any									=					{} if sp_model_kwargs is None else sp_model_kwargs

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

					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[Any]									=					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
					self.sp_model.Load(str(_lowercase  )  )
					__A   :						int									=					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   :						List[Any]									=					{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

					for i in range(10  ):
									__A   :						List[Any]									=					F"""[unused{i}]"""
									__A   :						int									=					5 + i

					# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
					__A   :						List[str]									=					12
					__A   :						Union[str, 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(_lowercase  )

	def __getstate__(   self  ):



					'''simple docstring'''




					__A   :						Tuple									=					self.__dict__.copy()
					__A   :						Optional[Any]									=					None
					return state

	def __setstate__(   self ,						__lowerCamelCase  ):



					'''simple docstring'''




					__A   :						Union[str, Any]									=					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   :						List[Any]									=					{}

					__A   :						Dict									=					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
					self.sp_model.Load(self.vocab_file  )

	def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = False  ):



					'''simple docstring'''




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

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

	def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



					'''simple docstring'''




					__A   :						str									=					[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  UpperCamelCase__(   self  ):



					'''simple docstring'''




					return len(self.sp_model  ) + self.fairseq_offset

	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					__A   :						Any									=					{self.convert_ids_to_tokens(_lowercase  ): i for i in range(self.vocab_size  )}
					vocab.update(self.added_tokens_encoder  )
					return vocab

	def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



					'''simple docstring'''




					return self.sp_model.encode(_lowercase ,						out_type=_lowercase  )

	def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



					'''simple docstring'''




					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					__A   :						Union[str, Any]									=					self.sp_model.PieceToId(_lowercase  )

					# 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  UpperCamelCase__(   self ,						__lowerCamelCase  ):



					'''simple docstring'''




					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  UpperCamelCase__(   self ,						__lowerCamelCase  ):



					'''simple docstring'''




					__A   :						int									=					''''''.join(_lowercase  ).replace(_lowercase ,						''' '''  ).strip()
					return out_string

	def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



					'''simple docstring'''




					if not os.path.isdir(_lowercase  ):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
									return
					__A   :						Union[str, Any]									=					os.path.join(
					    _lowercase ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

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

					return (out_vocab_file,)







	def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



					'''simple docstring'''




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











"""simple docstring"""



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_			=      {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""GPTNeoXTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXForCausalLM""",
           """GPTNeoXForQuestionAnswering""",
           """GPTNeoXForSequenceClassification""",
           """GPTNeoXForTokenClassification""",
           """GPTNeoXLayer""",
           """GPTNeoXModel""",
           """GPTNeoXPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox import (
                  GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXForCausalLM,
                  GPTNeoXForQuestionAnswering,
                  GPTNeoXForSequenceClassification,
                  GPTNeoXForTokenClassification,
                  GPTNeoXLayer,
                  GPTNeoXModel,
                  GPTNeoXPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




