
"""simple docstring"""

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['input_features']




	def __init__(			self		,			UpperCAmelCase_=80		,			UpperCAmelCase_=16000		,			UpperCAmelCase_=160		,			UpperCAmelCase_=30		,			UpperCAmelCase_=400		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=False		,			**UpperCAmelCase_		,			)		->							str:
			super().__init__(
			    feature_size=UpperCAmelCase_		,			sampling_rate=UpperCAmelCase_		,			padding_value=UpperCAmelCase_		,			return_attention_mask=UpperCAmelCase_		,			**UpperCAmelCase_		,			)
			lowerCamelCase					:      Optional[int]          =   n_fft
			lowerCamelCase					:      List[Any]          =   hop_length
			lowerCamelCase					:      Dict          =   chunk_length
			lowerCamelCase					:      int          =   chunk_length * sampling_rate
			lowerCamelCase					:      str          =   self.n_samples // hop_length
			lowerCamelCase					:      Optional[Any]          =   sampling_rate
			lowerCamelCase					:      List[str]          =   mel_filter_bank(
			    num_frequency_bins=1 + n_fft // 2		,			num_mel_filters=UpperCAmelCase_		,			min_frequency=0.0		,			max_frequency=8000.0		,			sampling_rate=UpperCAmelCase_		,			norm='slaney'		,			mel_scale='slaney'		,			)




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							np.ndarray:
			lowerCamelCase					:      int          =   spectrogram(
			    UpperCAmelCase_		,			window_function(self.n_fft		,			'hann' )		,			frame_length=self.n_fft		,			hop_length=self.hop_length		,			power=2.0		,			mel_filters=self.mel_filters		,			log_mel='log10'		,			)
			lowerCamelCase					:      List[Any]          =   log_spec[:, :-1]
			lowerCamelCase					:      int          =   np.maximum(UpperCAmelCase_		,			log_spec.max() - 8.0 )
			lowerCamelCase					:      Optional[int]          =   (log_spec + 4.0) / 4.0
			return log_spec




	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def       _UpperCamelCase   (			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = 0.0 )		->							List[np.ndarray]:
			if attention_mask is not None:
					lowerCamelCase					:      Dict          =   np.array(UpperCAmelCase_		,			np.intaa )
					lowerCamelCase					:      List[Any]          =   []

					for vector, length in zip(UpperCAmelCase_		,			attention_mask.sum(-1 ) ):
							lowerCamelCase					:      Any          =   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
							if length < normed_slice.shape[0]:
									lowerCamelCase					:      Dict          =   padding_value

							normed_input_values.append(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      str          =   [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

			return normed_input_values




	def __call__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = "max_length"		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							BatchFeature:

			if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
							raise ValueError(
							    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
							    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
							    F""" was sampled with {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.' )

			lowerCamelCase					:      Any          =   isinstance(UpperCAmelCase_		,			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}""" )
			lowerCamelCase					:      int          =   is_batched_numpy or (
			    isinstance(UpperCAmelCase_		,			(list, tuple) ) and (isinstance(raw_speech[0]		,			(np.ndarray, tuple, list) ))
			)

			if is_batched:
					lowerCamelCase					:      List[Any]          =   [np.asarray([speech]		,			dtype=np.floataa ).T for speech in raw_speech]
			elif not is_batched and not isinstance(UpperCAmelCase_		,			np.ndarray ):
					lowerCamelCase					:      Any          =   np.asarray(UpperCAmelCase_		,			dtype=np.floataa )
			elif isinstance(UpperCAmelCase_		,			np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
					lowerCamelCase					:      List[Any]          =   raw_speech.astype(np.floataa )

			# always return batch
			if not is_batched:
					lowerCamelCase					:      List[str]          =   [np.asarray([raw_speech] ).T]

			lowerCamelCase					:      int          =   BatchFeature({'input_features': raw_speech} )

			# convert into correct format for padding

			lowerCamelCase					:      List[str]          =   self.pad(
			    UpperCAmelCase_		,			padding=UpperCAmelCase_		,			max_length=max_length if max_length else self.n_samples		,			truncation=UpperCAmelCase_		,			pad_to_multiple_of=UpperCAmelCase_		,			return_attention_mask=return_attention_mask or do_normalize		,			)

			# zero-mean and unit-variance normalization
			if do_normalize:
					lowerCamelCase					:      int          =   self.zero_mean_unit_var_norm(
					    padded_inputs['input_features']		,			attention_mask=padded_inputs['attention_mask']		,			padding_value=self.padding_value		,			)
					lowerCamelCase					:      Optional[int]          =   np.stack(padded_inputs['input_features']		,			axis=0 )

			# make sure list is in array format
			lowerCamelCase					:      Dict          =   padded_inputs.get('input_features' ).transpose(2		,			0		,			1 )

			lowerCamelCase					:      List[str]          =   [self._np_extract_fbank_features(UpperCAmelCase_ ) for waveform in input_features[0]]

			if isinstance(input_features[0]		,			UpperCAmelCase_ ):
					lowerCamelCase					:      Optional[Any]          =   [np.asarray(UpperCAmelCase_		,			dtype=np.floataa ) for feature in input_features]
			else:
					lowerCamelCase					:      Dict          =   input_features

			if return_attention_mask:
					# rescale from sample (48000) to feature (3000)
					lowerCamelCase					:      List[Any]          =   padded_inputs['attention_mask'][:, :: self.hop_length]

			if return_tensors is not None:
					lowerCamelCase					:      Any          =   padded_inputs.convert_to_tensors(UpperCAmelCase_ )

			return padded_inputs




	def       _UpperCamelCase   (			self )		->							Dict[str, Any]:
			lowerCamelCase					:      List[Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Optional[Any]          =   self.__class__.__name__
			if "mel_filters" in output:
					del output["mel_filters"]
			return output








"""simple docstring"""

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_A						=			logging.get_logger(__name__)


_A						=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
_A						=			{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_A						=			{
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     VOCAB_FILES_NAMES
	lowercase_				=     PRETRAINED_VOCAB_FILES_MAP
	lowercase_				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_				=     ['input_ids', 'attention_mask']
	lowercase_				=     BartTokenizer




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_="replace"		,			UpperCAmelCase_="<s>"		,			UpperCAmelCase_="</s>"		,			UpperCAmelCase_="</s>"		,			UpperCAmelCase_="<s>"		,			UpperCAmelCase_="<unk>"		,			UpperCAmelCase_="<pad>"		,			UpperCAmelCase_="<mask>"		,			UpperCAmelCase_=False		,			UpperCAmelCase_=True		,			**UpperCAmelCase_		,			)		->							Union[str, Any]:
			super().__init__(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			tokenizer_file=UpperCAmelCase_		,			errors=UpperCAmelCase_		,			bos_token=UpperCAmelCase_		,			eos_token=UpperCAmelCase_		,			sep_token=UpperCAmelCase_		,			cls_token=UpperCAmelCase_		,			unk_token=UpperCAmelCase_		,			pad_token=UpperCAmelCase_		,			mask_token=UpperCAmelCase_		,			add_prefix_space=UpperCAmelCase_		,			trim_offsets=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

			lowerCamelCase					:      List[Any]          =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
			if pre_tok_state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
					lowerCamelCase					:      Tuple          =   getattr(UpperCAmelCase_		,			pre_tok_state.pop('type' ) )
					lowerCamelCase					:      Optional[Any]          =   add_prefix_space
					lowerCamelCase					:      str          =   pre_tok_class(**UpperCAmelCase_ )

			lowerCamelCase					:      Optional[Any]          =   add_prefix_space

			# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
			lowerCamelCase					:      Dict          =   'post_processor'
			lowerCamelCase					:      str          =   getattr(self.backend_tokenizer		,			UpperCAmelCase_		,			UpperCAmelCase_ )
			if tokenizer_component_instance:
					lowerCamelCase					:      Optional[Any]          =   json.loads(tokenizer_component_instance.__getstate__() )

					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
					if "sep" in state:
							lowerCamelCase					:      int          =   tuple(state['sep'] )
					if "cls" in state:
							lowerCamelCase					:      str          =   tuple(state['cls'] )

					lowerCamelCase					:      Optional[Any]          =   False

					if state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
							lowerCamelCase					:      Dict          =   add_prefix_space
							lowerCamelCase					:      Tuple          =   True

					if state.get('trim_offsets'		,			UpperCAmelCase_ ) != trim_offsets:
							lowerCamelCase					:      Tuple          =   trim_offsets
							lowerCamelCase					:      Dict          =   True

					if changes_to_apply:
							lowerCamelCase					:      Optional[int]          =   getattr(UpperCAmelCase_		,			state.pop('type' ) )
							lowerCamelCase					:      Any          =   component_class(**UpperCAmelCase_ )
							setattr(self.backend_tokenizer		,			UpperCAmelCase_		,			UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			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       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Optional[int]          =   AddedToken(UpperCAmelCase_		,			lstrip=UpperCAmelCase_		,			rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) else value
			lowerCamelCase					:      int          =   value




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      str          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    'to use it with pretokenized inputs.' )

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




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      Optional[Any]          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    'to use it with pretokenized inputs.' )

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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							Tuple[str]:
			lowerCamelCase					:      Any          =   self._tokenizer.model.save(UpperCAmelCase_		,			name=UpperCAmelCase_ )
			return tuple(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							List[Any]:
			lowerCamelCase					:      Union[str, Any]          =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
			if token_ids_a is None:
					return output

			return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							List[int]:
			lowerCamelCase					:      List[Any]          =   [self.sep_token_id]
			lowerCamelCase					:      str          =   [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 + sep + token_ids_a + sep ) * [0]






