"""simple docstring"""







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     lowercase__( __SCREAMING_SNAKE_CASE							:  Any=None  ,    __SCREAMING_SNAKE_CASE							:  int=None   ):
     return field(default_factory=lambda: default  ,    metadata=__SCREAMING_SNAKE_CASE   )







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






def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     try:
          int(__SCREAMING_SNAKE_CASE   )
          return True
     except ValueError:
          return False



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]   ):
     try:
          float(__SCREAMING_SNAKE_CASE   )
          return True
     except ValueError:
          return False







class  UpperCamelCase	:
   def __init__(    self  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       str									=			args
        lowercase_			:       Optional[int]									=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

        with open(self.args.csv_file  ,newline=''    ) as csv_file:
             lowercase_			:       List[Any]									=			csv.DictReader(__UpperCamelCase    )
             for row in reader:
                  lowercase_			:       str									=			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
                       lowercase_			:       Dict									=			int(row['result']    )
                  elif can_convert_to_float(row['result']    ):
                       # value is not None
                       lowercase_			:       Any									=			float(row['result']    )







   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       List[str]									=			plt.subplots()
        lowercase_			:       str									=			'Time usage' if self.args.is_time else 'Memory usage'
        lowercase_			:       Optional[Any]									=			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()    ):
             lowercase_			:       Tuple									=			sorted(set(self.result_dict[model_name]['bsz']    )    )
             lowercase_			:       Any									=			sorted(set(self.result_dict[model_name]['seq_len']    )    )
             lowercase_			:       List[str]									=			self.result_dict[model_name]['result']

             ((lowercase_)							,						(lowercase_))			:       Tuple									=			(
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )

             lowercase_			:       Tuple									=			(
                 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:
                       lowercase_			:       Optional[int]									=			np.asarray(
                           [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,dtype=__UpperCamelCase  ,)
                  else:
                       lowercase_			:       Optional[Any]									=			np.asarray(
                           [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,dtype=np.floataa  ,)

                  ((lowercase_)							,						(lowercase_))			:       Union[str, Any]									=			(
                      ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                  )

                  lowercase_			:       List[Any]									=			np.asarray(__UpperCamelCase  ,__UpperCamelCase    )[: len(__UpperCamelCase    )]
                  plt.scatter(
                      __UpperCamelCase  ,__UpperCamelCase  ,label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''    )
                  plt.plot(__UpperCamelCase  ,__UpperCamelCase  ,'--'    )

             title_str += f''' {label_model_name} vs.'''

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

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

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






def     lowercase__( ):
     lowercase_			:       str									=			HfArgumentParser(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			parser.parse_args_into_dataclasses()[0]
     lowercase_			:       str									=			Plot(args=__SCREAMING_SNAKE_CASE   )
     plot.plot()


if __name__ == "__main__":
       main()


"""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

