





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

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


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


lowerCAmelCase							:       str					 =	{
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

lowerCAmelCase							:       Optional[Any]					 =	{
    """vocab_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
        ),
    },
    """tokenizer_config_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
        ),
    },
    """merges_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
        ),
    },
}

lowerCAmelCase							:       Optional[Any]					 =	"""</w>"""
lowerCAmelCase							:       Any					 =	"""@@ """






def 		lowerCAmelCase  (	UpperCamelCase__   :   Any					)      ->    str:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   set()
						__SCREAMING_SNAKE_CASE:				List[Any]           =   word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)					)
												__SCREAMING_SNAKE_CASE:				Tuple           =   char
						return pairs


# Speech2Text2 has no max input length
lowerCAmelCase							:       List[str]					 =	{"""facebook/s2t-wav2vec2-large-en-de""": 1024}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__     :   str		      =     PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__     :   Dict		      =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__     :   Any		      =     ['''input_ids''', '''attention_mask''']






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase="<s>"			, _lowerCAmelCase="<pad>"			, _lowerCAmelCase="</s>"			, _lowerCAmelCase="<unk>"			, _lowerCAmelCase=False			, _lowerCAmelCase=None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(
										    unk_token=_lowerCAmelCase			, bos_token=_lowerCAmelCase			, eos_token=_lowerCAmelCase			, pad_token=_lowerCAmelCase			, do_lower_case=_lowerCAmelCase			, **_lowerCAmelCase			, )

										__SCREAMING_SNAKE_CASE:				int           =   do_lower_case

										with open(_lowerCAmelCase			, encoding='''utf-8'''					) as vocab_handle:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   json.load(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   {v: k for k, v in self.encoder.items()}

										if merges_file is None:
																logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""					)

																__SCREAMING_SNAKE_CASE:				Dict           =   None
																__SCREAMING_SNAKE_CASE:				Tuple           =   None
										else:
																with open(_lowerCAmelCase			, encoding='''utf-8'''					) as merges_handle:
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   merges_handle.read().split('''\n'''					)[:-1]

																__SCREAMING_SNAKE_CASE:				List[Any]           =   [tuple(merge.split()[:2]					) for merge in merges]
																__SCREAMING_SNAKE_CASE:				Tuple           =   dict(zip(_lowerCAmelCase			, range(len(_lowerCAmelCase					)					)					)					)
																__SCREAMING_SNAKE_CASE:				str           =   {}






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return len(self.decoder					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return dict(self.encoder			, **self.added_tokens_encoder					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   tuple(token[:-1]					) + (token[-1] + BPE_TOKEN_MERGES,)
										if token in self.cache:
																return self.cache[token]
										__SCREAMING_SNAKE_CASE:				Any           =   get_pairs(_lowerCAmelCase					)

										if not pairs:
																return token

										while True:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   min(_lowerCAmelCase			, key=lambda _lowerCAmelCase					: self.bpe_ranks.get(_lowerCAmelCase			, float('''inf'''					)					)					)
																if bigram not in self.bpe_ranks:
																						break
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[int]           =   bigram
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   []
																__SCREAMING_SNAKE_CASE:				List[str]           =   0
																while i < len(_lowerCAmelCase					):
																						try:
																												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   word.index(_lowerCAmelCase			, _lowerCAmelCase					)
																						except ValueError:
																												new_word.extend(word[i:]					)
																												break
																						else:
																												new_word.extend(word[i:j]					)
																												__SCREAMING_SNAKE_CASE:				Any           =   j

																						if word[i] == first and i < len(_lowerCAmelCase					) - 1 and word[i + 1] == second:
																												new_word.append(first + second					)
																												i += 2
																						else:
																												new_word.append(word[i]					)
																												i += 1
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   tuple(_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				int           =   new_word
																if len(_lowerCAmelCase					) == 1:
																						break
																else:
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   get_pairs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   ''' '''.join(_lowerCAmelCase					)
										if word == "\n  " + BPE_TOKEN_MERGES:
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   '''\n''' + BPE_TOKEN_MERGES

										if word.endswith(_lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				Dict           =   word.replace(_lowerCAmelCase			, ''''''					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   word.replace(''' '''			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   word
										return word






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										if self.bpe_ranks is None:
																raise ValueError(
																    '''This tokenizer was instantiated without a `merges.txt` file, so'''
																    ''' that it can only be used for decoding, not for encoding.'''
																    '''Make sure to provide `merges.txt` file at instantiation to enable '''
																    '''encoding.'''					)

										if self.do_lower_case:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   text.lower()

										__SCREAMING_SNAKE_CASE:				Dict           =   text.split()

										__SCREAMING_SNAKE_CASE:				Tuple           =   []
										for token in text:
																if token:
																						split_tokens.extend(list(self.bpe(_lowerCAmelCase					).split(''' '''					)					)					)

										return split_tokens






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										return self.encoder.get(_lowerCAmelCase			, self.encoder.get(self.unk_token					)					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.decoder.get(_lowerCAmelCase			, self.unk_token					)
										return result






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   ''' '''.join(_lowerCAmelCase					)

										# make sure @@ tokens are concatenated
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   ''''''.join(string.split(_lowerCAmelCase					)					)

										return string






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None					):

										"""simple docstring"""




										if not os.path.isdir(_lowerCAmelCase					):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
																return
										__SCREAMING_SNAKE_CASE:				Tuple           =   os.path.join(
										    _lowerCAmelCase			, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
										__SCREAMING_SNAKE_CASE:				int           =   os.path.join(
										    _lowerCAmelCase			, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']					)

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

										__SCREAMING_SNAKE_CASE:				str           =   0
										if self.bpe_ranks is None:
																return (vocab_file,)

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

										return (vocab_file, merges_file)










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


lowerCAmelCase							:       Tuple					 =	logging.get_logger(__name__)

lowerCAmelCase							:       List[Any]					 =	{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     '''deberta-v2'''






				def __init__(  self			, _lowerCAmelCase=128100			, _lowerCAmelCase=1536			, _lowerCAmelCase=24			, _lowerCAmelCase=24			, _lowerCAmelCase=6144			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=512			, _lowerCAmelCase=0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1e-7			, _lowerCAmelCase=False			, _lowerCAmelCase=-1			, _lowerCAmelCase=0			, _lowerCAmelCase=True			, _lowerCAmelCase=None			, _lowerCAmelCase=0			, _lowerCAmelCase="gelu"			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				str           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				List[Any]           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				str           =   hidden_act
										__SCREAMING_SNAKE_CASE:				Dict           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Dict           =   max_position_embeddings
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   type_vocab_size
										__SCREAMING_SNAKE_CASE:				str           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Any           =   relative_attention
										__SCREAMING_SNAKE_CASE:				Tuple           =   max_relative_positions
										__SCREAMING_SNAKE_CASE:				List[str]           =   pad_token_id
										__SCREAMING_SNAKE_CASE:				List[str]           =   position_biased_input

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

										__SCREAMING_SNAKE_CASE:				int           =   pos_att_type
										__SCREAMING_SNAKE_CASE:				List[Any]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   layer_norm_eps

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   kwargs.get('''pooler_hidden_size'''			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   pooler_dropout
										__SCREAMING_SNAKE_CASE:				int           =   pooler_hidden_act





class 				a			(      __lowercase			):






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.task == "multiple-choice":
																__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																__SCREAMING_SNAKE_CASE:				Any           =   {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 			snake_case_    (  self					):

										"""simple docstring"""




										return 12






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = 3			, _lowerCAmelCase = 40			, _lowerCAmelCase = 40			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   super().generate_dummy_inputs(preprocessor=_lowerCAmelCase			, framework=_lowerCAmelCase					)
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
										return dummy_inputs



