


from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


snake_case			:      int 							=   logging.get_logger(__name__)



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					str        =						["input_values", "attention_mask"]





							def __init__(    self				, A_=1				, A_=16000				, A_=128				, A_=1024				, A_=0.0				, A_=True				, A_=-4.267_7393				, A_=4.568_9974				, A_=False				, **A_				, )->	Optional[int]:
											super().__init__(feature_size=A_				, sampling_rate=A_				, padding_value=A_				, **A_	)
											_SCREAMING_SNAKE_CASE      = num_mel_bins
											_SCREAMING_SNAKE_CASE      = max_length
											_SCREAMING_SNAKE_CASE      = do_normalize
											_SCREAMING_SNAKE_CASE      = mean
											_SCREAMING_SNAKE_CASE      = std
											_SCREAMING_SNAKE_CASE      = return_attention_mask





							def 		__magic_name__			(    self				, A_				, A_				, )->	int:
											_SCREAMING_SNAKE_CASE      = torch.from_numpy(A_	).unsqueeze(0	)
											_SCREAMING_SNAKE_CASE      = ta_kaldi.fbank(
											    A_				, htk_compat=A_				, sample_frequency=self.sampling_rate				, use_energy=A_				, window_type='hanning'				, num_mel_bins=self.num_mel_bins				, dither=0.0				, frame_shift=10				, )

											_SCREAMING_SNAKE_CASE      = fbank.shape[0]
											_SCREAMING_SNAKE_CASE      = max_length - n_frames

											# pad or truncate, depending on difference
											if difference > 0:
															_SCREAMING_SNAKE_CASE      = torch.nn.ZeroPadad((0, 0, 0, difference)	)
															_SCREAMING_SNAKE_CASE      = pad_module(A_	)
											elif difference < 0:
															_SCREAMING_SNAKE_CASE      = fbank[0:max_length, :]

											_SCREAMING_SNAKE_CASE      = fbank.numpy()

											return fbank





							def 		__magic_name__			(    self				, A_	)->	List[str]:
											return (input_values - (self.mean)) / (self.std * 2)





							def __call__(    self				, A_				, A_ = None				, A_ = None				, **A_				, )->	Optional[Any]:

											if sampling_rate is not None:
															if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																			    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
																			    F''' {self.sampling_rate} and not {sampling_rate}.'''	)
											else:
															logger.warning(
															    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
															    'Failing to do so can result in silent errors that might be hard to debug.'	)

											_SCREAMING_SNAKE_CASE      = isinstance(A_				, np.ndarray	) and len(raw_speech.shape	) > 1
											if is_batched_numpy and len(raw_speech.shape	) > 2:
															raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''	)
											_SCREAMING_SNAKE_CASE      = is_batched_numpy or (
											    isinstance(A_				, (list, tuple)	) and (isinstance(raw_speech[0]				, (np.ndarray, tuple, list)	))
											)

											if is_batched:
															_SCREAMING_SNAKE_CASE      = [np.asarray(A_				, dtype=np.floataa	) for speech in raw_speech]
											elif not is_batched and not isinstance(A_				, np.ndarray	):
															_SCREAMING_SNAKE_CASE      = np.asarray(A_				, dtype=np.floataa	)
											elif isinstance(A_				, np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
															_SCREAMING_SNAKE_CASE      = raw_speech.astype(np.floataa	)

											# always return batch
											if not is_batched:
															_SCREAMING_SNAKE_CASE      = [raw_speech]

											# extract fbank features and pad/truncate to max_length
											_SCREAMING_SNAKE_CASE      = [self._extract_fbank_features(A_				, max_length=self.max_length	) for waveform in raw_speech]

											# convert into BatchFeature
											_SCREAMING_SNAKE_CASE      = BatchFeature({'input_values': features}	)

											# make sure list is in array format
											_SCREAMING_SNAKE_CASE      = padded_inputs.get('input_values'	)
											if isinstance(input_values[0]				, A_	):
															_SCREAMING_SNAKE_CASE      = [np.asarray(A_				, dtype=np.floataa	) for feature in input_values]

											# normalization
											if self.do_normalize:
															_SCREAMING_SNAKE_CASE      = [self.normalize(A_	) for feature in input_values]

											if return_tensors is not None:
															_SCREAMING_SNAKE_CASE      = padded_inputs.convert_to_tensors(A_	)

											return padded_inputs





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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


snake_case			:      Dict 							=   logging.get_logger(__name__)

snake_case			:      List[str] 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case			:      Any 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Optional[int] 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Union[str, Any] 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[str]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						["input_ids", "attention_mask", "token_type_ids"]
							SCREAMING_SNAKE_CASE   :					Any        =						DebertaTokenizer





							def __init__(    self				, A_=None				, A_=None				, A_=None				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, **A_				, )->	List[str]:
											super().__init__(
											    A_				, A_				, tokenizer_file=A_				, errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_bos_token'				, A_	)

											_SCREAMING_SNAKE_CASE      = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
											if pre_tok_state.get('add_prefix_space'				, A_	) != add_prefix_space:
															_SCREAMING_SNAKE_CASE      = getattr(A_				, pre_tok_state.pop('type'	)	)
															_SCREAMING_SNAKE_CASE      = add_prefix_space
															_SCREAMING_SNAKE_CASE      = pre_tok_class(**A_	)

											_SCREAMING_SNAKE_CASE      = add_prefix_space





							@property
							def 		__magic_name__			(    self	)->	str:
											if self._mask_token is None:
															if self.verbose:
																			logger.error('Using mask_token, but it is not set yet.'	)
															return None
											return str(self._mask_token	)





							@mask_token.setter
							def 		__magic_name__			(    self				, A_	)->	int:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else value
											_SCREAMING_SNAKE_CASE      = value





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]

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





							def 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)
											assert self.add_prefix_space or not is_split_into_words, (
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    "to use it with pretokenized inputs."
											)

											return super()._batch_encode_plus(*A_				, **A_	)





							def 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)

											assert self.add_prefix_space or not is_split_into_words, (
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    "to use it with pretokenized inputs."
											)

											return super()._encode_plus(*A_				, **A_	)





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											_SCREAMING_SNAKE_CASE      = self._tokenizer.model.save(A_				, name=A_	)
											return tuple(A_	)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)

											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
