


"""simple docstring"""






import json
import os
from typing import List, Optional, Tuple

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


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__snake_case												=							{
    '''vocab_file''': {
        '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json''',
    },
    '''merges_file''': {'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'''},
}

__snake_case												=							{
    '''microsoft/biogpt''': 1024,
}
def    A_ ( _lowerCAmelCase  :       Any							):






     """simple docstring"""

     _a     						=					set()
     _a     						=					word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)							)
          _a     						=					char
     return pairs


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Tuple     =		VOCAB_FILES_NAMES
     A_							:						List[str]     =		PRETRAINED_VOCAB_FILES_MAP
     A_							:						Optional[int]     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_							:						Tuple     =		['input_ids', 'attention_mask']


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase="<unk>"     ,					__UpperCAmelCase="<s>"     ,					__UpperCAmelCase="</s>"     ,					__UpperCAmelCase="</s>"     ,					__UpperCAmelCase="<pad>"     ,					**__UpperCAmelCase     ,					)							->				Tuple:
          super().__init__(
              bos_token=__UpperCAmelCase     ,					eos_token=__UpperCAmelCase     ,					sep_token=__UpperCAmelCase     ,					unk_token=__UpperCAmelCase     ,					pad_token=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

          try:
               import sacremoses
          except ImportError:
               raise ImportError(
                   '''You need to install sacremoses to use BioGptTokenizer. '''
                   '''See https://pypi.org/project/sacremoses/ for installation.'''				)

          _a     						=					'''en'''
          _a     						=					sacremoses
          # cache of sm.MosesTokenizer instance
          _a     						=					{}
          _a     						=					{}
          with open(__UpperCAmelCase     ,					encoding='''utf-8'''				) as vocab_handle:
               _a     						=					json.load(__UpperCAmelCase				)
          _a     						=					{v: k for k, v in self.encoder.items()}
          with open(__UpperCAmelCase     ,					encoding='''utf-8'''				) as merges_handle:
               _a     						=					merges_handle.read().split('''\n'''				)[:-1]
          _a     						=					[tuple(merge.split()[:2]				) for merge in merges]
          _a     						=					dict(zip(__UpperCAmelCase     ,					range(len(__UpperCAmelCase				)				)				)				)
          _a     						=					{}


     @property
     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          return len(self.encoder				)


     def 				_UpperCAmelCase   (   self				)							->				Dict:
          return dict(self.encoder     ,					**self.added_tokens_encoder				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Optional[Any]:
          if lang not in self.cache_moses_tokenizer:
               _a     						=					self.sm.MosesTokenizer(lang=__UpperCAmelCase				)
               _a     						=					moses_tokenizer
          return self.cache_moses_tokenizer[lang].tokenize(
              __UpperCAmelCase     ,					aggressive_dash_splits=__UpperCAmelCase     ,					return_str=__UpperCAmelCase     ,					escape=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Any:
          if lang not in self.cache_moses_detokenizer:
               _a     						=					self.sm.MosesDetokenizer(lang=__UpperCAmelCase				)
               _a     						=					moses_detokenizer
          return self.cache_moses_detokenizer[lang].detokenize(__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Union[str, Any]:
          _a     						=					tuple(token[:-1]				) + (token[-1] + '''</w>''',)
          if token in self.cache:
               return self.cache[token]
          _a     						=					get_pairs(__UpperCAmelCase				)

          if not pairs:
               return token + "</w>"

          while True:
               _a     						=					min(__UpperCAmelCase     ,					key=lambda __UpperCAmelCase				: self.bpe_ranks.get(__UpperCAmelCase     ,					float('''inf'''				)				)				)
               if bigram not in self.bpe_ranks:
                    break
               _a						,   _a     						=					bigram
               _a     						=					[]
               _a     						=					0
               while i < len(__UpperCAmelCase				):
                    try:
                         _a     						=					word.index(__UpperCAmelCase     ,					__UpperCAmelCase				)
                    except ValueError:
                         new_word.extend(word[i:]				)
                         break
                    else:
                         new_word.extend(word[i:j]				)
                         _a     						=					j

                    if word[i] == first and i < len(__UpperCAmelCase				) - 1 and word[i + 1] == second:
                         new_word.append(first + second				)
                         i += 2
                    else:
                         new_word.append(word[i]				)
                         i += 1
               _a     						=					tuple(__UpperCAmelCase				)
               _a     						=					new_word
               if len(__UpperCAmelCase				) == 1:
                    break
               else:
                    _a     						=					get_pairs(__UpperCAmelCase				)
          _a     						=					''' '''.join(__UpperCAmelCase				)
          if word == "\n  </w>":
               _a     						=					'''\n</w>'''
          _a     						=					word
          return word


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=False				)							->				Any:
          if bypass_tokenizer:
               _a     						=					text.split()
          else:
               _a     						=					self.moses_tokenize(__UpperCAmelCase     ,					self.lang				)

          _a     						=					[]
          for token in text:
               if token:
                    split_tokens.extend(list(self.bpe(__UpperCAmelCase				).split(''' '''				)				)				)

          return split_tokens


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Dict:
          return self.encoder.get(__UpperCAmelCase     ,					self.encoder.get(self.unk_token				)				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Union[str, Any]:
          return self.decoder.get(__UpperCAmelCase     ,					self.unk_token				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Tuple:
          _a     						=					[t.replace(''' '''     ,					''''''				).replace('''</w>'''     ,					''' '''				) for t in tokens]
          _a     						=					''''''.join(__UpperCAmelCase				).split()
          # detokenize
          _a     						=					self.moses_detokenize(__UpperCAmelCase     ,					self.lang				)
          return text


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				)							->				List[int]:
          if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
          _a     						=					[self.sep_token_id]
          return sep + token_ids_a + sep + token_ids_a


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 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				)
          # no bos used in fairseq
          if token_ids_a is not None:
               return [1] + ([0] * len(__UpperCAmelCase				)) + [1] + ([0] * len(__UpperCAmelCase				))
          return [1] + ([0] * len(__UpperCAmelCase				))


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				)							->				List[int]:
          _a     						=					[self.sep_token_id]

          # no bos used in fairseq
          if token_ids_a is None:
               return len(token_ids_a + sep				) * [0]
          return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				)							->				Tuple[str]:
          if not os.path.isdir(__UpperCAmelCase				):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
               return
          _a     						=					os.path.join(
              __UpperCAmelCase     ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
          _a     						=					os.path.join(
              __UpperCAmelCase     ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)

          with open(__UpperCAmelCase     ,					'''w'''     ,					encoding='''utf-8'''				) as f:
               f.write(json.dumps(self.encoder     ,					indent=2     ,					sort_keys=__UpperCAmelCase     ,					ensure_ascii=__UpperCAmelCase				) + '''\n'''				)

          _a     						=					0
          with open(__UpperCAmelCase     ,					'''w'''     ,					encoding='''utf-8'''				) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,					key=lambda __UpperCAmelCase				: kv[1]				):
                    if index != token_index:
                         logger.warning(
                             F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                             ''' Please check that the tokenizer is not corrupted!'''				)
                         _a     						=					token_index
                    writer.write(''' '''.join(__UpperCAmelCase				) + '''\n'''				)
                    index += 1

          return vocab_file, merge_file


     def __getstate__(   self				)							->				List[str]:
          _a     						=					self.__dict__.copy()
          _a     						=					None
          return state





     def __setstate__(   self     ,					__UpperCAmelCase				)							->				Any:
          _a     						=					d

          try:
               import sacremoses
          except ImportError:
               raise ImportError(
                   '''You need to install sacremoses to use XLMTokenizer. '''
                   '''See https://pypi.org/project/sacremoses/ for installation.'''				)

          _a     						=					sacremoses



"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						List[Any]     =		'deberta'


     def __init__(   self     ,					__UpperCAmelCase=50265     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=0.02     ,					__UpperCAmelCase=1e-7     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=-1     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase="gelu"     ,					**__UpperCAmelCase     ,					)							->				Any:
          super().__init__(**__UpperCAmelCase				)

          _a     						=					hidden_size
          _a     						=					num_hidden_layers
          _a     						=					num_attention_heads
          _a     						=					intermediate_size
          _a     						=					hidden_act
          _a     						=					hidden_dropout_prob
          _a     						=					attention_probs_dropout_prob
          _a     						=					max_position_embeddings
          _a     						=					type_vocab_size
          _a     						=					initializer_range
          _a     						=					relative_attention
          _a     						=					max_relative_positions
          _a     						=					pad_token_id
          _a     						=					position_biased_input

          # Backwards compatibility
          if type(__UpperCAmelCase				) == str:
               _a     						=					[x.strip() for x in pos_att_type.lower().split('''|'''				)]

          _a     						=					pos_att_type
          _a     						=					vocab_size
          _a     						=					layer_norm_eps

          _a     						=					kwargs.get('''pooler_hidden_size'''     ,					__UpperCAmelCase				)
          _a     						=					pooler_dropout
          _a     						=					pooler_hidden_act







class      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     @property
     def 				_UpperCAmelCase   (   self				)							->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a     						=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
               _a     						=					{0: '''batch''', 1: '''sequence'''}
          if self._config.type_vocab_size > 0:
               return OrderedDict(
                   [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]				)
          else:
               return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				int:
          return 12





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = None     ,					)							->				Mapping[str, Any]:
          _a     						=					super().generate_dummy_inputs(preprocessor=__UpperCAmelCase     ,					framework=__UpperCAmelCase				)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
          return dummy_inputs