






import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any=None      ,    UpperCamelCase_    :   Tuple=None						)					->				List[Any]:

      """simple docstring"""



      return field(default_factory=lambda: default      ,    metadata=__snake_case						)


@dataclass
class       __SCREAMING_SNAKE_CASE   :
      _SCREAMING_SNAKE_CASE		:    Dict						=   field(
          metadata={'''help''': '''The csv file to plot.'''}						,   )
      _SCREAMING_SNAKE_CASE		:    int						=   field(
          default=__lowercase						,   metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}						,   )
      _SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   field(
          default=__lowercase						,   metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}						,   )
      _SCREAMING_SNAKE_CASE		:    Optional[int]						=   field(
          default=__lowercase						,   metadata={'''help''': '''Disable logarithmic scale when plotting'''}						,   )
      _SCREAMING_SNAKE_CASE		:    List[str]						=   field(
          default=__lowercase						,   metadata={
              '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
          }						,   )
      _SCREAMING_SNAKE_CASE		:    Tuple						=   field(
          default=__lowercase						,   metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}						,   )
      _SCREAMING_SNAKE_CASE		:    Optional[Any]						=   list_field(
          default=__lowercase						,   metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''})







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]						)					->				Union[str, Any]:

      """simple docstring"""



      try:
            int(__snake_case						)
            return True
      except ValueError:
            return False







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				int:

      """simple docstring"""



      try:
            float(__snake_case						)
            return True
      except ValueError:
            return False


class       __SCREAMING_SNAKE_CASE   :






      def __init__(		self       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  args
            lowerCAmelCase__        =  defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

            with open(self.args.csv_file       ,  newline=''				) as csv_file:
                  lowerCAmelCase__        =  csv.DictReader(__a				)
                  for row in reader:
                        lowerCAmelCase__        =  row['model']
                        self.result_dict[model_name]["bsz"].append(int(row['batch_size']				)				)
                        self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']				)				)
                        if can_convert_to_int(row['result']				):
                              # value is not None
                              lowerCAmelCase__        =  int(row['result']				)
                        elif can_convert_to_float(row['result']				):
                              # value is not None
                              lowerCAmelCase__        =  float(row['result']				)







      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__  ,    lowerCAmelCase__        =  plt.subplots()
            lowerCAmelCase__        =  'Time usage' if self.args.is_time else 'Memory usage'
            lowerCAmelCase__        =  title_str + ' for training' if self.args.is_train else title_str + ' for inference'

            if not self.args.no_log_scale:
                  # set logarithm scales
                  ax.set_xscale('log'				)
                  ax.set_yscale('log'				)

            for axis in [ax.xaxis, ax.yaxis]:
                  axis.set_major_formatter(ScalarFormatter()				)

            for model_name_idx, model_name in enumerate(self.result_dict.keys()				):
                  lowerCAmelCase__        =  sorted(set(self.result_dict[model_name]['bsz']				)				)
                  lowerCAmelCase__        =  sorted(set(self.result_dict[model_name]['seq_len']				)				)
                  lowerCAmelCase__        =  self.result_dict[model_name]['result']

                  ((lowerCAmelCase__)  ,    (lowerCAmelCase__))        =  (
                      (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                  )

                  lowerCAmelCase__        =  (
                      model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
                  )

                  for inner_loop_value in inner_loop_array:
                        if self.args.plot_along_batch:
                              lowerCAmelCase__        =  np.asarray(
                                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,  dtype=__a       ,  )
                        else:
                              lowerCAmelCase__        =  np.asarray(
                                  [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,  dtype=np.floataa       ,  )

                        ((lowerCAmelCase__)  ,    (lowerCAmelCase__))        =  (
                            ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                        )

                        lowerCAmelCase__        =  np.asarray(__a       ,  __a				)[: len(__a				)]
                        plt.scatter(
                            __a       ,  __a       ,  label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"				)
                        plt.plot(__a       ,  __a       ,  '--'				)

                  title_str += F" {label_model_name} vs."

            lowerCAmelCase__        =  title_str[:-4]
            lowerCAmelCase__        =  'Time in s' if self.args.is_time else 'Memory in MB'

            # plot
            plt.title(__a				)
            plt.xlabel(__a				)
            plt.ylabel(__a				)
            plt.legend()

            if self.args.figure_png_file is not None:
                  plt.savefig(self.args.figure_png_file				)
            else:
                  plt.show()







def 		_UpperCamelCase				(      )					->				List[str]:

      """simple docstring"""



      lowerCAmelCase__        =  HfArgumentParser(__snake_case						)
      lowerCAmelCase__        =  parser.parse_args_into_dataclasses()[0]
      lowerCAmelCase__        =  Plot(args=__snake_case						)
      plot.plot()


if __name__ == "__main__":
     main()













import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


__snake_case      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Tuple						=   ['''input_features''', '''is_longer''']






						def __init__(		self       ,  _UpperCamelCase=64       ,  _UpperCamelCase=4_80_00       ,  _UpperCamelCase=4_80       ,  _UpperCamelCase=10       ,  _UpperCamelCase=10_24       ,  _UpperCamelCase=0.0       ,  _UpperCamelCase=False       ,  _UpperCamelCase = 0       ,  _UpperCamelCase = 1_40_00       ,  _UpperCamelCase = None       ,  _UpperCamelCase = "fusion"       ,  _UpperCamelCase = "repeatpad"       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__(
												    feature_size=_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  padding_value=_UpperCamelCase       ,  return_attention_mask=_UpperCamelCase       ,  **_UpperCamelCase       ,  )
												lowerCAmelCase__        =  top_db
												lowerCAmelCase__        =  truncation
												lowerCAmelCase__        =  padding
												lowerCAmelCase__        =  fft_window_size
												lowerCAmelCase__        =  (fft_window_size >> 1) + 1
												lowerCAmelCase__        =  hop_length
												lowerCAmelCase__        =  max_length_s
												lowerCAmelCase__        =  max_length_s * sampling_rate
												lowerCAmelCase__        =  sampling_rate
												lowerCAmelCase__        =  frequency_min
												lowerCAmelCase__        =  frequency_max
												lowerCAmelCase__        =  mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins       ,  num_mel_filters=_UpperCamelCase       ,  min_frequency=_UpperCamelCase       ,  max_frequency=_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  norm=_UpperCamelCase       ,  mel_scale='htk'       ,  )
												lowerCAmelCase__        =  mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins       ,  num_mel_filters=_UpperCamelCase       ,  min_frequency=_UpperCamelCase       ,  max_frequency=_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  norm='slaney'       ,  mel_scale='slaney'       ,  )






						def  UpperCamelCase__ (		self				):






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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												lowerCAmelCase__        =  spectrogram(
												    _UpperCamelCase       ,  window_function(self.fft_window_size       ,  'hann'				)       ,  frame_length=self.fft_window_size       ,  hop_length=self.hop_length       ,  power=2.0       ,  mel_filters=_UpperCamelCase       ,  log_mel='dB'       ,  )
												return log_mel_spectrogram.T






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  np.array_split(list(range(0       ,  total_frames - chunk_frames + 1				)				)       ,  3				)
												if len(ranges[1]				) == 0:
																		# if the audio is too short, we just use the first chunk
																		lowerCAmelCase__        =  [0]
												if len(ranges[2]				) == 0:
																		# if the audio is too short, we just use the first chunk
																		lowerCAmelCase__        =  [0]
												# randomly choose index for each part
												lowerCAmelCase__        =  np.random.choice(ranges[0]				)
												lowerCAmelCase__        =  np.random.choice(ranges[1]				)
												lowerCAmelCase__        =  np.random.choice(ranges[2]				)

												lowerCAmelCase__        =  mel[idx_front : idx_front + chunk_frames, :]
												lowerCAmelCase__        =  mel[idx_middle : idx_middle + chunk_frames, :]
												lowerCAmelCase__        =  mel[idx_back : idx_back + chunk_frames, :]

												lowerCAmelCase__        =  torch.tensor(mel[None, None, :]				)
												lowerCAmelCase__        =  torch.nn.functional.interpolate(
												    _UpperCamelCase       ,  size=[chunk_frames, 64]       ,  mode='bilinear'       ,  align_corners=_UpperCamelCase				)
												lowerCAmelCase__        =  mel_shrink[0][0].numpy()
												lowerCAmelCase__        =  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,  axis=0				)
												return mel_fusion






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												if waveform.shape[0] > max_length:
																		if truncation == "rand_trunc":
																								lowerCAmelCase__        =  True
																								# random crop to max_length (for compatibility) -> this should be handled by self.pad
																								lowerCAmelCase__        =  len(_UpperCamelCase				) - max_length
																								lowerCAmelCase__        =  np.random.randint(0       ,  overflow + 1				)
																								lowerCAmelCase__        =  waveform[idx : idx + max_length]
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters_slaney				)[None, :]
																		elif truncation == "fusion":
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters				)
																								lowerCAmelCase__        =  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																								lowerCAmelCase__        =  mel.shape[0]
																								if chunk_frames == total_frames:
																														# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																														# In this case, we just use the whole audio.
																														lowerCAmelCase__        =  np.stack([mel, mel, mel, mel]       ,  axis=0				)
																														lowerCAmelCase__        =  False
																								else:
																														lowerCAmelCase__        =  self._random_mel_fusion(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)
																														lowerCAmelCase__        =  True
																		else:
																								raise NotImplementedError(F"data_truncating {truncation} not implemented"				)

												else:
																		lowerCAmelCase__        =  False
																		# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																		if waveform.shape[0] < max_length:
																								if padding == "repeat":
																														lowerCAmelCase__        =  int(max_length / len(_UpperCamelCase				)				)
																														lowerCAmelCase__        =  np.stack(np.tile(_UpperCamelCase       ,  n_repeat + 1				)				)[:max_length]
																								if padding == "repeatpad":
																														lowerCAmelCase__        =  int(max_length / len(_UpperCamelCase				)				)
																														lowerCAmelCase__        =  np.stack(np.tile(_UpperCamelCase       ,  _UpperCamelCase				)				)
																								lowerCAmelCase__        =  np.pad(_UpperCamelCase       ,  (0, max_length - waveform.shape[0])       ,  mode='constant'       ,  constant_values=0				)

																		if truncation == "fusion":
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters				)
																								lowerCAmelCase__        =  np.stack([input_mel, input_mel, input_mel, input_mel]       ,  axis=0				)
																		else:
																								lowerCAmelCase__        =  self._np_extract_fbank_features(_UpperCamelCase       ,  self.mel_filters_slaney				)[None, :]

												return input_mel, longer







						def __call__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  truncation if truncation is not None else self.truncation
												lowerCAmelCase__        =  padding if padding else self.padding

												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(_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}"				)
												lowerCAmelCase__        =  is_batched_numpy or (
												    isinstance(_UpperCamelCase       ,  (list, tuple)				) and (isinstance(raw_speech[0]       ,  (np.ndarray, tuple, list)				))
												)

												if is_batched:
																		lowerCAmelCase__        =  [np.asarray(_UpperCamelCase       ,  dtype=np.floataa				) for speech in raw_speech]
												elif not is_batched and not isinstance(_UpperCamelCase       ,  np.ndarray				):
																		lowerCAmelCase__        =  np.asarray(_UpperCamelCase       ,  dtype=np.floataa				)
												elif isinstance(_UpperCamelCase       ,  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(_UpperCamelCase				)]

												# convert to mel spectrogram, truncate and pad if needed.
												lowerCAmelCase__        =  [
												    self._get_input_mel(_UpperCamelCase       ,  max_length if max_length else self.nb_max_samples       ,  _UpperCamelCase       ,  _UpperCamelCase				)
												    for waveform in raw_speech
												]

												lowerCAmelCase__        =  []
												lowerCAmelCase__        =  []
												for mel, longer in padded_inputs:
																		input_mel.append(_UpperCamelCase				)
																		is_longer.append(_UpperCamelCase				)

												if truncation == "fusion" and sum(_UpperCamelCase				) == 0:
																		# if no audio is longer than 10s, then randomly select one audio to be longer
																		lowerCAmelCase__        =  np.random.randint(0       ,  len(_UpperCamelCase				)				)
																		lowerCAmelCase__        =  True

												if isinstance(input_mel[0]       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  [np.asarray(_UpperCamelCase       ,  dtype=np.floataa				) for feature in input_mel]

												# is_longer is a list of bool
												lowerCAmelCase__        =  [[longer] for longer in is_longer]

												lowerCAmelCase__        =  {'input_features': input_mel, 'is_longer': is_longer}
												lowerCAmelCase__        =  BatchFeature(_UpperCamelCase				)

												if return_tensors is not None:
																		lowerCAmelCase__        =  input_features.convert_to_tensors(_UpperCamelCase				)

												return input_features





