



'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowercase       :						str						=logging.get_logger(__name__)






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  ['input_values', 'attention_mask']



			def __init__(     self		:						str    ,			__UpperCamelCase		:						int=1    ,			__UpperCamelCase		:						Tuple=16000    ,			__UpperCamelCase		:						str=128    ,			__UpperCamelCase		:						Tuple=1024    ,			__UpperCamelCase		:						Union[str, Any]=0.0    ,			__UpperCamelCase		:						Optional[int]=True    ,			__UpperCamelCase		:						Optional[int]=-4.2677393    ,			__UpperCamelCase		:						int=4.5689974    ,			__UpperCamelCase		:						str=False    ,			**__UpperCamelCase		:						Any    ,			)      ->      List[str]:
							"""simple docstring"""





							super().__init__(feature_size=__UpperCamelCase    ,			sampling_rate=__UpperCamelCase    ,			padding_value=__UpperCamelCase    ,			**__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    num_mel_bins
							snake_case__      :   Dict      =    max_length
							snake_case__      :   List[str]      =    do_normalize
							snake_case__      :   List[Any]      =    mean
							snake_case__      :   Tuple      =    std
							snake_case__      :   Optional[int]      =    return_attention_mask



			def 				lowerCAmelCase (     self		:						int    ,			__UpperCamelCase		:						np.ndarray    ,			__UpperCamelCase		:						int    ,			)      ->      np.ndarray:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    torch.from_numpy(__UpperCamelCase			).unsqueeze(0			)
							snake_case__      :   Dict      =    ta_kaldi.fbank(
							    __UpperCamelCase    ,			htk_compat=__UpperCamelCase    ,			sample_frequency=self.sampling_rate    ,			use_energy=__UpperCamelCase    ,			window_type='''hanning'''    ,			num_mel_bins=self.num_mel_bins    ,			dither=0.0    ,			frame_shift=10    ,			)

							snake_case__      :   List[str]      =    fbank.shape[0]
							snake_case__      :   int      =    max_length - n_frames

							# pad or truncate, depending on difference
							if difference > 0:
											snake_case__      :   List[Any]      =    torch.nn.ZeroPadad((0, 0, 0, difference)			)
											snake_case__      :   Any      =    pad_module(__UpperCamelCase			)
							elif difference < 0:
											snake_case__      :   Dict      =    fbank[0:max_length, :]

							snake_case__      :   List[str]      =    fbank.numpy()

							return fbank



			def 				lowerCAmelCase (     self		:						int    ,			__UpperCamelCase		:						np.ndarray			)      ->      np.ndarray:
							"""simple docstring"""





							return (input_values - (self.mean)) / (self.std * 2)







			def __call__(     self		:						str    ,			__UpperCamelCase		:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,			__UpperCamelCase		:						Optional[int] = None    ,			__UpperCamelCase		:						Optional[Union[str, TensorType]] = None    ,			**__UpperCamelCase		:						Tuple    ,			)      ->      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} was trained using a sampling rate of'''
															    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
															    F''' {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.'''			)

							snake_case__      :   Optional[Any]      =    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}'''			)
							snake_case__      :   List[Any]      =    is_batched_numpy or (
							    isinstance(__UpperCamelCase    ,			(list, tuple)			) and (isinstance(raw_speech[0]    ,			(np.ndarray, tuple, list)			))
							)

							if is_batched:
											snake_case__      :   Union[str, Any]      =    [np.asarray(__UpperCamelCase    ,			dtype=np.floataa			) for speech in raw_speech]
							elif not is_batched and not isinstance(__UpperCamelCase    ,			np.ndarray			):
											snake_case__      :   Optional[int]      =    np.asarray(__UpperCamelCase    ,			dtype=np.floataa			)
							elif isinstance(__UpperCamelCase    ,			np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
											snake_case__      :   Union[str, Any]      =    raw_speech.astype(np.floataa			)

							# always return batch
							if not is_batched:
											snake_case__      :   int      =    [raw_speech]

							# extract fbank features and pad/truncate to max_length
							snake_case__      :   str      =    [self._extract_fbank_features(__UpperCamelCase    ,			max_length=self.max_length			) for waveform in raw_speech]

							# convert into BatchFeature
							snake_case__      :   int      =    BatchFeature({'''input_values''': features}			)

							# make sure list is in array format
							snake_case__      :   Dict      =    padded_inputs.get('''input_values'''			)
							if isinstance(input_values[0]    ,			__UpperCamelCase			):
											snake_case__      :   Optional[int]      =    [np.asarray(__UpperCamelCase    ,			dtype=np.floataa			) for feature in input_values]

							# normalization
							if self.do_normalize:
											snake_case__      :   int      =    [self.normalize(__UpperCamelCase			) for feature in input_values]

							if return_tensors is not None:
											snake_case__      :   Dict      =    padded_inputs.convert_to_tensors(__UpperCamelCase			)

							return padded_inputs






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  A			(   lowercase=None       ,			lowercase=None					)  ->    Tuple:
		'''simple docstring'''

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


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






def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

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


class 							lowercase   :



		def __init__(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	args
				UpperCamelCase            =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

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



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase            =	plt.subplots()
				UpperCamelCase            =	'Time usage' if self.args.is_time else 'Memory usage'
				UpperCamelCase            =	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()			):
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['bsz']			)			)
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['seq_len']			)			)
						UpperCamelCase            =	self.result_dict[model_name]['result']

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

						UpperCamelCase            =	(
						    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:
										UpperCamelCase            =	np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							, dtype=A_							, )
								else:
										UpperCamelCase            =	np.asarray(
										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							, dtype=np.floataa							, )

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

								UpperCamelCase            =	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.'''

				UpperCamelCase            =	title_str[:-4]
				UpperCamelCase            =	'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  A			(   )  ->    Dict:
		'''simple docstring'''

		UpperCamelCase            =	HfArgumentParser(lowercase					)
		UpperCamelCase            =	parser.parse_args_into_dataclasses()[0]
		UpperCamelCase            =	Plot(args=lowercase					)
		plot.plot()


if __name__ == "__main__":
					main()
