






from math import ceil
from typing import List, Optional, Union

import numpy as np

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


snake_case    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[Any]			=	['''audio_values''', '''audio_mask''']


     def __init__(				self       :	int					, UpperCAmelCase_       :	Tuple=2048					, UpperCAmelCase_       :	Any=1					, UpperCAmelCase_       :	int=[16, 16]					, UpperCAmelCase_       :	List[Any]=128					, UpperCAmelCase_       :	List[str]=4_4100					, UpperCAmelCase_       :	Optional[Any]=86					, UpperCAmelCase_       :	Tuple=2048					, UpperCAmelCase_       :	Optional[int]=0.0					, **UpperCAmelCase_       :	List[Any]					, ):
       super().__init__(
           feature_size=UpperCAmelCase_					, sampling_rate=UpperCAmelCase_					, padding_value=UpperCAmelCase_					, **UpperCAmelCase_					, )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    spectrogram_length
       SCREAMING_SNAKE_CASE       :      Dict							=    num_channels
       SCREAMING_SNAKE_CASE       :      Tuple							=    patch_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    feature_size // self.patch_size[1]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    n_fft
       SCREAMING_SNAKE_CASE       :      str							=    sampling_rate // hop_length_to_sampling_rate
       SCREAMING_SNAKE_CASE       :      str							=    sampling_rate
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    padding_value
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2					, num_mel_filters=UpperCAmelCase_					, min_frequency=0.0					, max_frequency=22_050.0					, sampling_rate=UpperCAmelCase_					, norm="slaney"					, mel_scale="slaney"					, ).T


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	np.array      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    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.T					, log_mel="dB"					, db_range=80.0					, )
       SCREAMING_SNAKE_CASE       :      str							=    log_spec[:, :-1]
       SCREAMING_SNAKE_CASE       :      str							=    log_spec - 20.0
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.clip(log_spec / 40.0					, -2.0					, 0.0      ) + 1.0
       return log_spec






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

       if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
           raise ValueError(
               "This feature extractor is set to support sampling rate"
               f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
               f''' 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."      )

       SCREAMING_SNAKE_CASE       :      Dict							=    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}'''      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    is_batched_numpy or (
           isinstance(UpperCAmelCase_					, (list, tuple)      ) and (isinstance(raw_speech[0]					, (np.ndarray, tuple, list)      ))
       )
       if is_batched:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [np.asarray([speech]					, dtype=np.floataa      ).T for speech in raw_speech]
       elif not is_batched and not isinstance(UpperCAmelCase_					, np.ndarray      ):
         SCREAMING_SNAKE_CASE       :      List[str]							=    np.asarray(UpperCAmelCase_					, dtype=np.floataa      )
       elif isinstance(UpperCAmelCase_					, np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
         SCREAMING_SNAKE_CASE       :      Any							=    raw_speech.astype(np.floataa      )
       # always return batch
       if not is_batched:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    [np.asarray([raw_speech]      ).T]

       # Convert audio signals to log mel spectrograms, truncate by time axis
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [
           self._np_extract_fbank_features(waveform.squeeze()      ).T[: self.spectrogram_length] for waveform in raw_speech
       ]
       if isinstance(audio_features[0]					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Dict							=    [np.asarray(UpperCAmelCase_					, dtype=np.floataa      ) for feature in audio_features]

       # Create audio attention mask
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    max(
           [ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len for feature in audio_features]      )  # The maximum number of audio patches in a batch
       if return_attention_mask:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    [
             (ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [1]
             + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [0]
             for feature in audio_features
         ]
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    np.array(UpperCAmelCase_      ).astype(np.floataa      )

       # convert into correct format for padding
       SCREAMING_SNAKE_CASE       :      str							=    max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    np.ones([len(UpperCAmelCase_      ), 1, max_time_len, self.feature_size]      ).astype(np.floataa      )
       SCREAMING_SNAKE_CASE       :      str							=    padded_audio_features * self.padding_value
       for i in range(len(UpperCAmelCase_      )      ):
         SCREAMING_SNAKE_CASE       :      int							=    audio_features[i]
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    feature

       # return as BatchFeature
       if return_attention_mask:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    {"audio_values": padded_audio_features, "audio_mask": audio_mask}
       else:
         SCREAMING_SNAKE_CASE       :      Tuple							=    {"audio_values": padded_audio_features}

       SCREAMING_SNAKE_CASE       :      Any							=    BatchFeature(data=UpperCAmelCase_					, tensor_type=UpperCAmelCase_      )
       return encoded_inputs














import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


snake_case    				=		logging.getLogger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Union[str, Any]=-1      ):
       # in NER datasets, the last column is usually reserved for NER label
       SCREAMING_SNAKE_CASE       :      int							=    label_idx


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Union[Split, str]      ):
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    mode.value
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    os.path.join(UpperCAmelCase_					, f'''{mode}.txt'''      )
       SCREAMING_SNAKE_CASE       :      int							=    1
       SCREAMING_SNAKE_CASE       :      Any							=    []
       with open(UpperCAmelCase_					, encoding="utf-8"      ) as f:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
         SCREAMING_SNAKE_CASE       :      List[str]							=    []
         for line in f:
           if line.startswith("-DOCSTART-"      ) or line == "" or line == "\n":
             if words:
               examples.append(InputExample(guid=f'''{mode}-{guid_index}'''					, words=UpperCAmelCase_					, labels=UpperCAmelCase_      )      )
               guid_index += 1
               SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
               SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
           else:
             SCREAMING_SNAKE_CASE       :      str							=    line.split(" "      )
             words.append(splits[0]      )
             if len(UpperCAmelCase_      ) > 1:
               labels.append(splits[self.label_idx].replace("\n"					, ""      )      )
             else:
               # Examples could have no label for mode = "test"
               labels.append("O"      )
         if words:
           examples.append(InputExample(guid=f'''{mode}-{guid_index}'''					, words=UpperCAmelCase_					, labels=UpperCAmelCase_      )      )
       return examples


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	List      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    0
       for line in test_input_reader:
         if line.startswith("-DOCSTART-"      ) or line == "" or line == "\n":
           writer.write(UpperCAmelCase_      )
           if not preds_list[example_id]:
             example_id += 1
         elif preds_list[example_id]:
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    line.split()[0] + " " + preds_list[example_id].pop(0      ) + "\n"
           writer.write(UpperCAmelCase_      )
         else:
           logger.warning("Maximum sequence length exceeded: No prediction for '%s'."					, line.split()[0]      )






     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "r"      ) as f:
           SCREAMING_SNAKE_CASE       :      List[Any]							=    f.read().splitlines()
         if "O" not in labels:
           SCREAMING_SNAKE_CASE       :      Tuple							=    ["O"] + labels
         return labels
       else:
         return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	Union[str, Any]      ):
       # in CONLL2003 dataset chunk column is second-to-last
       super().__init__(label_idx=-2      )






     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "r"      ) as f:
           SCREAMING_SNAKE_CASE       :      Dict							=    f.read().splitlines()
         if "O" not in labels:
           SCREAMING_SNAKE_CASE       :      str							=    ["O"] + labels
         return labels
       else:
         return [
             "O",
             "B-ADVP",
             "B-INTJ",
             "B-LST",
             "B-PRT",
             "B-NP",
             "B-SBAR",
             "B-VP",
             "B-ADJP",
             "B-CONJP",
             "B-PP",
             "I-ADVP",
             "I-INTJ",
             "I-LST",
             "I-PRT",
             "I-NP",
             "I-SBAR",
             "I-VP",
             "I-ADJP",
             "I-CONJP",
             "I-PP",
         ]





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Union[Split, str]      ):
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      str							=    mode.value
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    os.path.join(UpperCAmelCase_					, f'''{mode}.txt'''      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    1
       SCREAMING_SNAKE_CASE       :      str							=    []

       with open(UpperCAmelCase_					, encoding="utf-8"      ) as f:
         for sentence in parse_incr(UpperCAmelCase_      ):
           SCREAMING_SNAKE_CASE       :      List[Any]							=    []
           SCREAMING_SNAKE_CASE       :      List[str]							=    []
           for token in sentence:
             words.append(token["form"]      )
             labels.append(token["upos"]      )
           assert len(UpperCAmelCase_      ) == len(UpperCAmelCase_      )
           if words:
             examples.append(InputExample(guid=f'''{mode}-{guid_index}'''					, words=UpperCAmelCase_					, labels=UpperCAmelCase_      )      )
             guid_index += 1
       return examples


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	List      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    0
       for sentence in parse_incr(UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    preds_list[example_id]
         SCREAMING_SNAKE_CASE       :      Any							=    ""
         for token in sentence:
           out += f'''{token['form']} ({token['upos']}|{s_p.pop(0      )}) '''
         out += "\n"
         writer.write(UpperCAmelCase_      )
         example_id += 1






     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "r"      ) as f:
           return f.read().splitlines()
       else:
         return [
             "ADJ",
             "ADP",
             "ADV",
             "AUX",
             "CCONJ",
             "DET",
             "INTJ",
             "NOUN",
             "NUM",
             "PART",
             "PRON",
             "PROPN",
             "PUNCT",
             "SCONJ",
             "SYM",
             "VERB",
             "X",
         ]






