
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


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''input_features''']

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     Union[str, Any]=8_0   ,					__UpperCamelCase					:     str=1_6_0_0_0   ,					__UpperCamelCase					:     Optional[int]=1_6_0   ,					__UpperCamelCase					:     int=3_0   ,					__UpperCamelCase					:     List[str]=4_0_0   ,					__UpperCamelCase					:     Dict=0.0   ,					__UpperCamelCase					:     str=False   ,					**__UpperCamelCase					:     List[str]   ,					):
												super().__init__(
												    feature_size=__UpperCamelCase   ,					sampling_rate=__UpperCamelCase   ,					padding_value=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												lowerCamelCase_							  =					n_fft
												lowerCamelCase_							  =					hop_length
												lowerCamelCase_							  =					chunk_length
												lowerCamelCase_							  =					chunk_length * sampling_rate
												lowerCamelCase_							  =					self.n_samples // hop_length
												lowerCamelCase_							  =					sampling_rate
												lowerCamelCase_							  =					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    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     np.array   ):
												lowerCamelCase_							  =					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_							  =					log_spec[:, :-1]
												lowerCamelCase_							  =					np.maximum(__UpperCamelCase   ,					log_spec.max() - 8.0   )
												lowerCamelCase_							  =					(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    lowercase__      (	__UpperCamelCase					:     List[np.ndarray]   ,					__UpperCamelCase					:     List[np.ndarray]   ,					__UpperCamelCase					:     float = 0.0   ):
												if attention_mask is not None:
																			lowerCamelCase_							  =					np.array(__UpperCamelCase   ,					np.intaa   )
																			lowerCamelCase_							  =					[]

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

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

												return normed_input_values

					def __call__(	self					:     Dict   ,					__UpperCamelCase					:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     Optional[str] = "max_length"   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					**__UpperCamelCase					:     Any   ,					):

												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_							  =					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_							  =					is_batched_numpy or (
												    isinstance(__UpperCamelCase   ,					(list, tuple)   ) and (isinstance(raw_speech[0]   ,					(np.ndarray, tuple, list)   ))
												)

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

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

												lowerCamelCase_							  =					BatchFeature({"""input_features""": raw_speech}   )

												# convert into correct format for padding

												lowerCamelCase_							  =					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_							  =					self.zero_mean_unit_var_norm(
																			    padded_inputs["""input_features"""]   ,					attention_mask=padded_inputs["""attention_mask"""]   ,					padding_value=self.padding_value   ,					)
																			lowerCamelCase_							  =					np.stack(padded_inputs["""input_features"""]   ,					axis=0   )

												# make sure list is in array format
												lowerCamelCase_							  =					padded_inputs.get("""input_features"""   ).transpose(2   ,					0   ,					1   )

												lowerCamelCase_							  =					[self._np_extract_fbank_features(__UpperCamelCase   ) for waveform in input_features[0]]

												if isinstance(input_features[0]   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					[np.asarray(__UpperCamelCase   ,					dtype=np.floataa   ) for feature in input_features]
												else:
																			lowerCamelCase_							  =					input_features

												if return_attention_mask:
																			# rescale from sample (48000) to feature (3000)
																			lowerCamelCase_							  =					padded_inputs["""attention_mask"""][:, :: self.hop_length]

												if return_tensors is not None:
																			lowerCamelCase_							  =					padded_inputs.convert_to_tensors(__UpperCamelCase   )

												return padded_inputs



					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					copy.deepcopy(self.__dict__   )
												lowerCamelCase_							  =					self.__class__.__name__
												if "mel_filters" in output:
																			del output["mel_filters"]
												return output







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''facebook/bart-large-mnli'''
					SCREAMING_SNAKE_CASE			    =    (
					    '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
					    '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
					    '''It returns the most likely label in the list of provided `labels` for the input text.'''
					)
					SCREAMING_SNAKE_CASE			    =    '''text_classifier'''
					SCREAMING_SNAKE_CASE			    =    AutoTokenizer
					SCREAMING_SNAKE_CASE			    =    AutoModelForSequenceClassification

					SCREAMING_SNAKE_CASE			    =    ['''text''', ['''text''']]
					SCREAMING_SNAKE_CASE			    =    ['''text''']

					def    lowercase__      (	self					:     Union[str, Any]   ):
												super().setup()
												lowerCamelCase_							  =					self.model.config
												lowerCamelCase_							  =					-1
												for idx, label in config.idalabel.items():
																			if label.lower().startswith("""entail"""   ):
																										lowerCamelCase_							  =					int(__UpperCamelCase   )
												if self.entailment_id == -1:
																			raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					labels
												return self.pre_processor(
												    [text] * len(__UpperCamelCase   )   ,					[F'''This example is {label}''' for label in labels]   ,					return_tensors="""pt"""   ,					padding="""max_length"""   ,					)



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					outputs.logits
												lowerCamelCase_							  =					torch.argmax(logits[:, 2]   ).item()
												return self._labels[label_id]





