





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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Optional[Any]		     =      ["audio_values", "audio_mask"]






     def __init__(							self						:   str						,lowerCamelCase__						:   Optional[int]=2048						,lowerCamelCase__						:   Dict=1						,lowerCamelCase__						:   Dict=[16, 16]						,lowerCamelCase__						:   Union[str, Any]=128						,lowerCamelCase__						:   int=44100						,lowerCamelCase__						:   Dict=86						,lowerCamelCase__						:   str=2048						,lowerCamelCase__						:   Tuple=0.0						,**lowerCamelCase__						:   str						,)					->		Any:




           '''simple docstring'''


           super().__init__(
               feature_size=lowerCamelCase__						,sampling_rate=lowerCamelCase__						,padding_value=lowerCamelCase__						,**lowerCamelCase__						,)

           SCREAMING_SNAKE_CASE		=						spectrogram_length
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						feature_size // self.patch_size[1]
           SCREAMING_SNAKE_CASE		=						n_fft
           SCREAMING_SNAKE_CASE		=						sampling_rate // hop_length_to_sampling_rate
           SCREAMING_SNAKE_CASE		=						sampling_rate
           SCREAMING_SNAKE_CASE		=						padding_value
           SCREAMING_SNAKE_CASE		=						mel_filter_bank(
               num_frequency_bins=1 + n_fft // 2						,num_mel_filters=lowerCamelCase__						,min_frequency=0.0						,max_frequency=22050.0						,sampling_rate=lowerCamelCase__						,norm="""slaney"""						,mel_scale="""slaney"""						,).T






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   np.array	)					->		np.ndarray:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						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.T						,log_mel="""dB"""						,db_range=80.0						,)
           SCREAMING_SNAKE_CASE		=						log_spec[:, :-1]
           SCREAMING_SNAKE_CASE		=						log_spec - 20.0
           SCREAMING_SNAKE_CASE		=						np.clip(log_spec / 40.0						,-2.0						,0.0	) + 1.0
           return log_spec






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




           '''simple docstring'''


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

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

           # Create audio attention mask
           SCREAMING_SNAKE_CASE		=						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		=						[
                     (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		=						np.array(lowerCamelCase__	).astype(np.floataa	)

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

           # return as BatchFeature
           if return_attention_mask:
                 SCREAMING_SNAKE_CASE		=						{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
           else:
                 SCREAMING_SNAKE_CASE		=						{"""audio_values""": padded_audio_features}

           SCREAMING_SNAKE_CASE		=						BatchFeature(data=lowerCamelCase__						,tensor_type=lowerCamelCase__	)
           return encoded_inputs









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						       =	{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


