



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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_xglm import XGLMTokenizer
else:
						__a           =							None


__a           =							logging.get_logger(__name__)

__a           =							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__a           =							{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

__a           =							{
    "facebook/xglm-564M": 20_48,
}
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     int		=	VOCAB_FILES_NAMES
					_A						:     Optional[int]		=	PRETRAINED_VOCAB_FILES_MAP
					_A						:     Optional[Any]		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A						:     Union[str, Any]		=	["""input_ids""", """attention_mask"""]
					_A						:     Optional[Any]		=	XGLMTokenizer






					def __init__(			self:				str		,	snake_case:				Optional[Any]=None		,	snake_case:				List[str]=None		,	snake_case:				Dict="<s>"		,	snake_case:				Dict="</s>"		,	snake_case:				Optional[Any]="</s>"		,	snake_case:				Optional[int]="<s>"		,	snake_case:				Optional[Any]="<unk>"		,	snake_case:				List[str]="<pad>"		,	**snake_case:				Optional[Any]		,	)  ->      Optional[Any]:
											# Compatibility with the original tokenizer
											snake_case_					:List[str]		=     7
											snake_case_					:int		=     [f"""<madeupword{i}>""" for i in range(self.num_madeup_words		)]

											snake_case_					:List[Any]		=     kwargs.get("""additional_special_tokens"""		,	[]		)
											kwargs["additional_special_tokens"] += [
											    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
											]

											super().__init__(
											    snake_case		,	tokenizer_file=snake_case		,	bos_token=snake_case		,	eos_token=snake_case		,	sep_token=snake_case		,	cls_token=snake_case		,	unk_token=snake_case		,	pad_token=snake_case		,	**snake_case		,	)

											snake_case_					:str		=     vocab_file
											snake_case_					:Union[str, Any]		=     False if not self.vocab_file else True






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

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






					def   lowerCAmelCase_							(			self:				Tuple		,	snake_case:				List[int]		,	snake_case:				Optional[List[int]] = None		)  ->      List[int]:
											snake_case_					:Tuple		=     [self.sep_token_id]

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






					def   lowerCAmelCase_							(			self:				Tuple		,	snake_case:				str		,	snake_case:				Optional[str] = None		)  ->      Tuple[str]:
											if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																	    """tokenizer."""		)

											if not os.path.isdir(snake_case		):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""		)
																	return
											snake_case_					:Any		=     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		):
																	copyfile(self.vocab_file		,	snake_case		)

											return (out_vocab_file,)




"""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