




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowercase__							:	Union[str, Any]											=		logging.get_logger(__name__)

lowercase__							:	List[str]											=		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase__							:	List[Any]											=		{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

lowercase__							:	Dict											=		{
    "facebook/blenderbot_small-90M": 512,
}
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  VOCAB_FILES_NAMES
						lowerCAmelCase_							=  PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_							=  BlenderbotSmallTokenizer






						def __init__(		self				:	Optional[int]			,      __lowercase				:	List[Any]=None			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	Tuple="<|endoftext|>"			,      __lowercase				:	List[str]="<|endoftext|>"			,      __lowercase				:	List[str]="<|endoftext|>"			,      __lowercase				:	Union[str, Any]=False			,      __lowercase				:	Tuple=True			,      **__lowercase				:	Tuple			,      ):






							"""simple docstring"""






							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=__lowercase			,      merges=__lowercase			,      add_prefix_space=__lowercase			,      trim_offsets=__lowercase			,      )			,      bos_token=__lowercase			,      eos_token=__lowercase			,      unk_token=__lowercase			,      **__lowercase			,      )
							snake_case_						       =	add_prefix_space






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	List[str]=None		):






							"""simple docstring"""






							snake_case_						       =	[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 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	List[int]			,      __lowercase				:	Optional[List[int]] = None		):






							"""simple docstring"""






							snake_case_						       =	[self.sep_token_id]
							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 + sep + token_ids_a + sep		) * [0]






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

import numpy as np

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


lowercase__							:	Tuple											=		logging.get_logger(__name__)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''input_values''', '''attention_mask''']






						def __init__(		self				:	List[Any]			,      __lowercase				:	int = 1			,      __lowercase				:	int = 1_60_00			,      __lowercase				:	float = 0.0			,      __lowercase				:	bool = False			,      __lowercase				:	int = 80			,      __lowercase				:	int = 16			,      __lowercase				:	int = 64			,      __lowercase				:	str = "hann_window"			,      __lowercase				:	float = 1.0			,      __lowercase				:	float = 80			,      __lowercase				:	float = 76_00			,      __lowercase				:	float = 1E-10			,      __lowercase				:	int = 2			,      __lowercase				:	bool = True			,      **__lowercase				:	Union[str, Any]			,      ):






							"""simple docstring"""






							super().__init__(feature_size=__lowercase			,      sampling_rate=__lowercase			,      padding_value=__lowercase			,      **__lowercase		)
							snake_case_						       =	do_normalize
							snake_case_						       =	return_attention_mask

							snake_case_						       =	num_mel_bins
							snake_case_						       =	hop_length
							snake_case_						       =	win_length
							snake_case_						       =	win_function
							snake_case_						       =	frame_signal_scale
							snake_case_						       =	fmin
							snake_case_						       =	fmax
							snake_case_						       =	mel_floor
							snake_case_						       =	reduction_factor

							snake_case_						       =	win_length * sampling_rate // 10_00
							snake_case_						       =	hop_length * sampling_rate // 10_00
							snake_case_						       =	optimal_fft_length(self.sample_size		)
							snake_case_						       =	(self.n_fft // 2) + 1

							snake_case_						       =	window_function(window_length=self.sample_size			,      name=self.win_function			,      periodic=__lowercase		)

							snake_case_						       =	mel_filter_bank(
							    num_frequency_bins=self.n_freqs			,      num_mel_filters=self.num_mel_bins			,      min_frequency=self.fmin			,      max_frequency=self.fmax			,      sampling_rate=self.sampling_rate			,      norm="slaney"			,      mel_scale="slaney"			,      )

							if frame_signal_scale != 1.0:
								warnings.warn(
								    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"			,      __lowercase			,      )
							if reduction_factor != 2.0:
								warnings.warn(
								    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"			,      __lowercase			,      )






						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def 			snake_case__		(		__lowercase				:	List[np.ndarray]			,      __lowercase				:	List[np.ndarray]			,      __lowercase				:	float = 0.0		):






							"""simple docstring"""






							if attention_mask is not None:
								snake_case_						       =	np.array(__lowercase			,      np.intaa		)
								snake_case_						       =	[]

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

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

							return normed_input_values






						def 			snake_case__		(		self				:	str			,      __lowercase				:	np.ndarray			,      ):






							"""simple docstring"""






							snake_case_						       =	spectrogram(
							    __lowercase			,      window=self.window			,      frame_length=self.sample_size			,      hop_length=self.sample_stride			,      fft_length=self.n_fft			,      mel_filters=self.mel_filters			,      mel_floor=self.mel_floor			,      log_mel="log10"			,      )
							return log_mel_spec.T






						def __call__(		self				:	Optional[int]			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      __lowercase				:	Optional[int] = None			,      **__lowercase				:	List[str]			,      ):






							"""simple docstring"""






							if audio is None and audio_target is None:
								raise ValueError("You must provide either `audio` or `audio_target` values."		)

							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 audio 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."		)

							if audio is not None:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )
							else:
								snake_case_						       =	None

							if audio_target is not None:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )

								if inputs is None:
									return inputs_target
								else:
									snake_case_						       =	inputs_target["input_values"]
									snake_case_						       =	inputs_target.get("attention_mask"		)
									if decoder_attention_mask is not None:
										snake_case_						       =	decoder_attention_mask

							return inputs






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,      __lowercase				:	bool = False			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      **__lowercase				:	Optional[Any]			,      ):






							"""simple docstring"""






							snake_case_						       =	isinstance(__lowercase			,      np.ndarray		) and len(speech.shape		) > 1
							if is_batched_numpy and len(speech.shape		) > 2:
								raise ValueError(f"Only mono-channel audio is supported for input to {self}"		)
							snake_case_						       =	is_batched_numpy or (
							    isinstance(__lowercase			,      (list, tuple)		) and (isinstance(speech[0]			,      (np.ndarray, tuple, list)		))
							)

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

							# always return batch
							if not is_batched:
								snake_case_						       =	[speech]

							# needed to make pad() work on spectrogram inputs
							snake_case_						       =	self.feature_size

							# convert into correct format for padding
							if is_target:
								snake_case_						       =	[self._extract_mel_features(__lowercase		) for waveform in speech]
								snake_case_						       =	BatchFeature({"input_values": features}		)
								snake_case_						       =	self.num_mel_bins
							else:
								snake_case_						       =	BatchFeature({"input_values": speech}		)

							snake_case_						       =	self.pad(
							    __lowercase			,      padding=__lowercase			,      max_length=__lowercase			,      truncation=__lowercase			,      pad_to_multiple_of=__lowercase			,      return_attention_mask=__lowercase			,      **__lowercase			,      )

							snake_case_						       =	feature_size_hack

							# convert input values to correct format
							snake_case_						       =	padded_inputs["input_values"]
							if not isinstance(input_values[0]			,      np.ndarray		):
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.floataa		) for array in input_values]
							elif (
							    not isinstance(__lowercase			,      np.ndarray		)
							    and isinstance(input_values[0]			,      np.ndarray		)
							    and input_values[0].dtype is np.dtype(np.floataa		)
							):
								snake_case_						       =	[array.astype(np.floataa		) for array in input_values]
							elif isinstance(__lowercase			,      np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
								snake_case_						       =	input_values.astype(np.floataa		)

							# convert attention_mask to correct format
							snake_case_						       =	padded_inputs.get("attention_mask"		)
							if attention_mask is not None:
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.intaa		) for array in attention_mask]

							# zero-mean and unit-variance normalization
							if not is_target and self.do_normalize:
								snake_case_						       =	(
								    attention_mask
								    if self._get_padding_strategies(__lowercase			,      max_length=__lowercase		) is not PaddingStrategy.DO_NOT_PAD
								    else None
								)
								snake_case_						       =	self.zero_mean_unit_var_norm(
								    padded_inputs["input_values"]			,      attention_mask=__lowercase			,      padding_value=self.padding_value		)

							if return_tensors is not None:
								snake_case_						       =	padded_inputs.convert_to_tensors(__lowercase		)

							return padded_inputs






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							snake_case_						       =	["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
							for name in names:
								if name in output:
									del output[name]

							return output
