'''simple docstring'''




import os
from collections import deque

import torch
from torch.utils.data import Dataset






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase=""       ,    __lowerCAmelCase="train"):




					"""simple docstring"""


					assert os.path.isdir(__lowerCAmelCase)

					lowerCAmelCase   					=  []
					lowerCAmelCase   					=  os.listdir(__lowerCAmelCase)
					for story_filename in story_filenames_list:
							if "summary" in story_filename:
									continue
							lowerCAmelCase   					=  os.path.join(__lowerCAmelCase       ,    __lowerCAmelCase)
							if not os.path.isfile(__lowerCAmelCase):
									continue
							self.documents.append(__lowerCAmelCase)



			def __len__(   self):




					"""simple docstring"""


					return len(self.documents)







			def __getitem__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.documents[idx]
					lowerCAmelCase   					=  document_path.split("""/""")[-1]
					with open(__lowerCAmelCase       ,    encoding="""utf-8""") as source:
							lowerCAmelCase   					=  source.read()
							lowerCAmelCase					,     lowerCAmelCase   					=  process_story(__lowerCAmelCase)
					return document_name, story_lines, summary_lines







def 				snake_case__							(			_A:					Dict )    ->	Dict:

		'''simple docstring'''


		lowerCAmelCase   					=  list(filter(lambda _A : len(_A ) != 0      ,   [line.strip() for line in raw_story.split("""\n""" )] ) )

		# for some unknown reason some lines miss a period, add it
		lowerCAmelCase   					=  [_add_missing_period(_A ) for line in nonempty_lines]

		# gather article lines
		lowerCAmelCase   					=  []
		lowerCAmelCase   					=  deque(_A )
		while True:
				try:
						lowerCAmelCase   					=  lines.popleft()
						if element.startswith("""@highlight""" ):
								break
						story_lines.append(_A )
				except IndexError:
						# if "@highlight" is absent from the file we pop
						# all elements until there is None, raising an exception.
						return story_lines, []

    # gather summary lines
		lowerCAmelCase   					=  list(filter(lambda _A : not t.startswith("""@highlight""" )      ,   _A ) )

		return story_lines, summary_lines






def 				snake_case__							(			_A:					List[str] )    ->	Optional[int]:

		'''simple docstring'''


		lowerCAmelCase   					=  [""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
		if line.startswith("""@highlight""" ):
				return line
		if line[-1] in END_TOKENS:
				return line
		return line + "."






def 				snake_case__							(			_A:					Optional[int]      ,   _A:					Tuple      ,   _A:					int )    ->	Dict:

		'''simple docstring'''


		if len(_A ) > block_size:
				return sequence[:block_size]
		else:
				sequence.extend([pad_token_id] * (block_size - len(_A )) )
				return sequence






def 				snake_case__							(			_A:					Optional[int]      ,   _A:					Tuple )    ->	Optional[int]:

		'''simple docstring'''


		lowerCAmelCase   					=  torch.ones_like(_A )
		lowerCAmelCase   					=  sequence == pad_token_id
		lowerCAmelCase   					=  0
		return mask






def 				snake_case__							(			_A:					int      ,   _A:					Optional[int]      ,   _A:					Optional[int] )    ->	Dict:

		'''simple docstring'''


		lowerCAmelCase   					=  [tokenizer.encode(_A ) for line in story_lines]
		lowerCAmelCase   					=  [token for sentence in story_lines_token_ids for token in sentence]
		lowerCAmelCase   					=  [tokenizer.encode(_A ) for line in summary_lines]
		lowerCAmelCase   					=  [token for sentence in summary_lines_token_ids for token in sentence]

		return story_token_ids, summary_token_ids






def 				snake_case__							(			_A:					List[Any]      ,   _A:					List[Any] )    ->	Tuple:

		'''simple docstring'''


		lowerCAmelCase   					=  []
		for sequence in batch:
				lowerCAmelCase   					=  -1
				lowerCAmelCase   					=  []
				for s in sequence:
						if s == separator_token_id:
								sentence_num += 1
						embeddings.append(sentence_num % 2 )
				batch_embeddings.append(_A )
		return torch.tensor(_A )





'''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


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       ['''input_features''']



			def __init__(   self       ,    __lowerCAmelCase=80       ,    __lowerCAmelCase=16000       ,    __lowerCAmelCase=160       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=False       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(
					    feature_size=__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    padding_value=__lowerCAmelCase       ,    return_attention_mask=__lowerCAmelCase       ,    **__lowerCAmelCase       ,    )
					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=__lowerCAmelCase       ,    min_frequency=0.0       ,    max_frequency=8000.0       ,    sampling_rate=__lowerCAmelCase       ,    norm="""slaney"""       ,    mel_scale="""slaney"""       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  spectrogram(
					    __lowerCAmelCase       ,    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(__lowerCAmelCase       ,    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 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 0.0):




					"""simple docstring"""


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

							for vector, length in zip(__lowerCAmelCase       ,    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(__lowerCAmelCase)
					else:
							lowerCAmelCase   					=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

					return normed_input_values



			def __call__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = "max_length"       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""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.""")

					lowerCAmelCase   					=  isinstance(__lowerCAmelCase       ,    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(__lowerCAmelCase       ,    (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(__lowerCAmelCase       ,    np.ndarray):
							lowerCAmelCase   					=  np.asarray(__lowerCAmelCase       ,    dtype=np.floataa)
					elif isinstance(__lowerCAmelCase       ,    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(
					    __lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    max_length=max_length if max_length else self.n_samples       ,    truncation=__lowerCAmelCase       ,    pad_to_multiple_of=__lowerCAmelCase       ,    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(__lowerCAmelCase) for waveform in input_features[0]]

					if isinstance(input_features[0]       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase       ,    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(__lowerCAmelCase)

					return padded_inputs







			def 							a_       (   self):




					"""simple docstring"""


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




