"""simple docstring"""







import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={"vocab_file": "vocab.txt"}

__SCREAMING_SNAKE_CASE								={
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

__SCREAMING_SNAKE_CASE								={
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str   ):
     with open(__SCREAMING_SNAKE_CASE  ,    'r'   ) as f:
          lowercase_			:       List[str]									=			f.read().splitlines()
          return [l.strip() for l in lines]







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						VOCAB_FILES_NAMES
   lowercase							=						PRETRAINED_VOCAB_FILES_MAP
   lowercase							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=						['input_ids', 'attention_mask']
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase="<unk>"  ,__UpperCamelCase="<cls>"  ,__UpperCamelCase="<pad>"  ,__UpperCamelCase="<mask>"  ,__UpperCamelCase="<eos>"  ,**__UpperCamelCase  ,)			->   int:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			load_vocab_file(__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			dict(enumerate(self.all_tokens    )    )
        lowercase_			:       Any									=			{tok: ind for ind, tok in enumerate(self.all_tokens    )}
        lowercase_			:       Tuple									=			unk_token
        lowercase_			:       int									=			cls_token
        lowercase_			:       int									=			pad_token
        lowercase_			:       Union[str, Any]									=			mask_token
        lowercase_			:       int									=			eos_token
        lowercase_			:       Any									=			self.all_tokens
        self._create_trie(self.unique_no_split_tokens    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        return self._id_to_token.get(__UpperCamelCase  ,self.unk_token    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self._token_to_id.get(__UpperCamelCase  ,self._token_to_id.get(self.unk_token    )    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        return text.split()
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=False    )			->   Tuple:


        '''simple docstring'''


        return len(self._id_to_token    )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        return {token: i for i, token in enumerate(self.all_tokens    )}
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self._token_to_id.get(__UpperCamelCase  ,self._token_to_id.get(self.unk_token    )    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        return self._id_to_token.get(__UpperCamelCase  ,self.unk_token    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   List[int]:


        '''simple docstring'''


        lowercase_			:       Any									=			[self.cls_token_id]
        lowercase_			:       List[str]									=			[self.eos_token_id]  # No sep token in ESM vocabulary
        if token_ids_a is None:
             if self.eos_token_id is None:
                  return cls + token_ids_a
             else:
                  return cls + token_ids_a + sep
        elif self.eos_token_id is None:
             raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'    )
        return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = False    )			->   List[int]:


        '''simple docstring'''


        if already_has_special_tokens:
             if token_ids_a is not None:
                  raise ValueError(
                      'You should not supply a second sequence if the provided sequence of '
                      'ids is already formatted with special tokens for the model.'    )

             return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
        lowercase_			:       Dict									=			[1] + ([0] * len(__UpperCamelCase    )) + [1]
        if token_ids_a is not None:
             mask += [0] * len(__UpperCamelCase    ) + [1]
        return mask
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       int									=			os.path.join(__UpperCamelCase  ,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'    )
        with open(__UpperCamelCase  ,'w'    ) as f:
             f.write('\n'.join(self.all_tokens    )    )
        return (vocab_file,)
   @property
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return self.get_vocab_size(with_added_tokens=__UpperCamelCase    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = False    )			->   int:


        '''simple docstring'''


        return super()._add_tokens(__UpperCamelCase  ,special_tokens=__UpperCamelCase    )


"""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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						['input_features']
   def __init__(    self  ,__UpperCamelCase=80  ,__UpperCamelCase=1_6000  ,__UpperCamelCase=160  ,__UpperCamelCase=30  ,__UpperCamelCase=400  ,__UpperCamelCase=0.0  ,__UpperCamelCase=False  ,**__UpperCamelCase  ,)			->   Any:


        '''simple docstring'''


        super().__init__(
            feature_size=__UpperCamelCase  ,sampling_rate=__UpperCamelCase  ,padding_value=__UpperCamelCase  ,return_attention_mask=__UpperCamelCase  ,**__UpperCamelCase  ,)
        lowercase_			:       Dict									=			n_fft
        lowercase_			:       Optional[Any]									=			hop_length
        lowercase_			:       Optional[int]									=			chunk_length
        lowercase_			:       Optional[Any]									=			chunk_length * sampling_rate
        lowercase_			:       Tuple									=			self.n_samples // hop_length
        lowercase_			:       int									=			sampling_rate
        lowercase_			:       int									=			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:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			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'  ,)
        lowercase_			:       Optional[Any]									=			log_spec[:, :-1]
        lowercase_			:       int									=			np.maximum(__UpperCamelCase  ,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     (    __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = 0.0    )			->   List[np.ndarray]:


        '''simple docstring'''


        if attention_mask is not None:
             lowercase_			:       Optional[int]									=			np.array(__UpperCamelCase  ,np.intaa    )
             lowercase_			:       Optional[Any]									=			[]

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

                  normed_input_values.append(__UpperCamelCase    )
        else:
             lowercase_			:       Optional[int]									=			[(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:


        '''simple docstring'''


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

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

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

        lowercase_			:       str									=			BatchFeature({'input_features': raw_speech}    )

        # convert into correct format for padding

        lowercase_			:       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:
             lowercase_			:       int									=			self.zero_mean_unit_var_norm(
                 padded_inputs['input_features']  ,attention_mask=padded_inputs['attention_mask']  ,padding_value=self.padding_value  ,)
             lowercase_			:       Tuple									=			np.stack(padded_inputs['input_features']  ,axis=0    )

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

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

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

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

        if return_tensors is not None:
             lowercase_			:       Union[str, Any]									=			padded_inputs.convert_to_tensors(__UpperCamelCase    )

        return padded_inputs







   def     _UpperCAmelCase     (    self    )			->   Dict[str, Any]:


        '''simple docstring'''


        lowercase_			:       Dict									=			copy.deepcopy(self.__dict__    )
        lowercase_			:       Union[str, Any]									=			self.__class__.__name__
        if "mel_filters" in output:
             del output["mel_filters"]
        return output

