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


UpperCamelCase  							=						logging.get_logger(__name__)



class   snake_case_       (		__A       ):
   __A	:		List[str]         =				["input_features"]






   def __init__(      self				:     List[str]	,						lowercase_				:     List[Any]=80	,						lowercase_				:     List[str]=1_60_00	,						lowercase_				:     Optional[int]=1_60	,						lowercase_				:     Tuple=30	,						lowercase_				:     Optional[Any]=4_00	,						lowercase_				:     str=0.0	,						lowercase_				:     Any=False	,						**lowercase_				:     Tuple	,						)					->						Union[str, Any]:
       super().__init__(
           feature_size=lowercase_	,						sampling_rate=lowercase_	,						padding_value=lowercase_	,						return_attention_mask=lowercase_	,						**lowercase_	,						)
       lowercase__ :   Tuple    					=				n_fft
       lowercase__ :   int    					=				hop_length
       lowercase__ :   Tuple    					=				chunk_length
       lowercase__ :   List[Any]    					=				chunk_length * sampling_rate
       lowercase__ :   Union[str, Any]    					=				self.n_samples // hop_length
       lowercase__ :   Tuple    					=				sampling_rate
       lowercase__ :   List[str]    					=				mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2	,						num_mel_filters=lowercase_	,						min_frequency=0.0	,						max_frequency=80_00.0	,						sampling_rate=lowercase_	,						norm="slaney"	,						mel_scale="slaney"	,						)






   def       __UpperCamelCase     (      self				:     List[str]	,						lowercase_				:     np.array  )					->						np.ndarray:
       lowercase__ :   Tuple    					=				spectrogram(
           lowercase_	,						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"	,						)
       lowercase__ :   int    					=				log_spec[:, :-1]
       lowercase__ :   int    					=				np.maximum(lowercase_	,						log_spec.max() - 8.0  )
       lowercase__ :   List[Any]    					=				(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     (      lowercase_				:     List[np.ndarray]	,						lowercase_				:     List[np.ndarray]	,						lowercase_				:     float = 0.0  )					->						List[np.ndarray]:
       if attention_mask is not None:
           lowercase__ :   int    					=				np.array(lowercase_	,						np.intaa  )
           lowercase__ :   List[Any]    					=				[]

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

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

       return normed_input_values






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

       lowercase__ :   List[Any]    					=				isinstance(lowercase_	,						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}'''  )
       lowercase__ :   Union[str, Any]    					=				is_batched_numpy or (
           isinstance(lowercase_	,						(list, tuple)  ) and (isinstance(raw_speech[0]	,						(np.ndarray, tuple, list)  ))
       )

       if is_batched:
           lowercase__ :   Any    					=				[np.asarray([speech]	,						dtype=np.floataa  ).T for speech in raw_speech]
       elif not is_batched and not isinstance(lowercase_	,						np.ndarray  ):
           lowercase__ :   int    					=				np.asarray(lowercase_	,						dtype=np.floataa  )
       elif isinstance(lowercase_	,						np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
           lowercase__ :   str    					=				raw_speech.astype(np.floataa  )

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

       lowercase__ :   Union[str, Any]    					=				BatchFeature({"input_features": raw_speech}  )

       # convert into correct format for padding

       lowercase__ :   Any    					=				self.pad(
           lowercase_	,						padding=lowercase_	,						max_length=max_length if max_length else self.n_samples	,						truncation=lowercase_	,						pad_to_multiple_of=lowercase_	,						return_attention_mask=return_attention_mask or do_normalize	,						)

       # zero-mean and unit-variance normalization
       if do_normalize:
           lowercase__ :   str    					=				self.zero_mean_unit_var_norm(
               padded_inputs["input_features"]	,						attention_mask=padded_inputs["attention_mask"]	,						padding_value=self.padding_value	,						)
           lowercase__ :   Dict    					=				np.stack(padded_inputs["input_features"]	,						axis=0  )

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

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

       if isinstance(input_features[0]	,						lowercase_  ):
           lowercase__ :   List[Any]    					=				[np.asarray(lowercase_	,						dtype=np.floataa  ) for feature in input_features]
       else:
           lowercase__ :   Tuple    					=				input_features

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

       if return_tensors is not None:
           lowercase__ :   Dict    					=				padded_inputs.convert_to_tensors(lowercase_  )

       return padded_inputs







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





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Union[str, Any]				       ="""Wav2Vec2FeatureExtractor"""
	A__    :   Any				       ="""AutoTokenizer"""
	def __init__(			self							:   List[Any]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   str							):
						super().__init__(UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False
	@classmethod
	def      A_				(			cls							:   Union[str, Any]       ,   UpperCAmelCase_							:   Any       ,   **UpperCAmelCase_							:   Optional[Any]							):
						try:
											return super().from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
						except OSError:
											warnings.warn(
											    F'Loading a tokenizer inside {cls.__name__} from a config that does not'
											    ' include a `tokenizer_class` attribute is deprecated and will be '
											    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
											    ' attribute to either your `config.json` or `tokenizer_config.json` '
											    'file to suppress this warning: '       ,   UpperCAmelCase_       ,   )

											SCREAMING_SNAKE_CASE__													= WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)

											return cls(feature_extractor=UpperCAmelCase_       ,   tokenizer=UpperCAmelCase_							)
	def __call__(			self							:   Any       ,   *UpperCAmelCase_							:   Dict       ,   **UpperCAmelCase_							:   Any							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'							)
											SCREAMING_SNAKE_CASE__													= kwargs.pop('raw_speech'							)
						else:
											SCREAMING_SNAKE_CASE__													= kwargs.pop('audio'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('sampling_rate'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('text'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= args[1:]

						if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'							)

						if audio is not None:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   sampling_rate=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if text is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if text is None:
											return inputs
						elif audio is None:
											return encodings
						else:
											SCREAMING_SNAKE_CASE__													= encodings['input_ids']
											return inputs
	def      A_				(			self							:   List[str]       ,   *UpperCAmelCase_							:   List[Any]       ,   **UpperCAmelCase_							:   Optional[int]							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor.pad(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= kwargs.pop('input_features'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('labels'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= args[1:]

						if input_features is not None:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor.pad(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if labels is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer.pad(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if labels is None:
											return input_features
						elif input_features is None:
											return labels
						else:
											SCREAMING_SNAKE_CASE__													= labels['input_ids']
											return input_features
	def      A_				(			self							:   List[Any]       ,   *UpperCAmelCase_							:   List[str]       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.batch_decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Tuple       ,   *UpperCAmelCase_							:   int       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)





	@contextmanager
	def      A_				(			self							:   str							):
						warnings.warn(
						    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
						    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
						    'your audio inputs, or in a separate call.'							)
						SCREAMING_SNAKE_CASE__													= True
						SCREAMING_SNAKE_CASE__													= self.tokenizer
						yield
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False





