





"""simple docstring"""





from typing import List, Optional, Union

import numpy as np

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


lowerCAmelCase__        =      logging.get_logger(__name__)





class 							_lowerCamelCase   (							_lowercase			):
						UpperCAmelCase_			=      ["input_values", "attention_mask"]


						def __init__(self ,				__a=1 ,				__a=1_60_00 ,				__a=0.0 ,				__a=False ,				__a=True ,				**__a ,				)			-> Optional[int]:
								super().__init__(feature_size=__a ,				sampling_rate=__a ,				padding_value=__a ,				**__a		)
								UpperCamelCase =						return_attention_mask
								UpperCamelCase =						do_normalize


						@staticmethod
						def      snake_case_ (__a ,				__a ,				__a = 0.0		)			-> List[np.ndarray]:
								if attention_mask is not None:
										UpperCamelCase =						np.array(__a ,				np.intaa		)
										UpperCamelCase =						[]

										for vector, length in zip(__a ,				attention_mask.sum(-1		)		):
												UpperCamelCase =						(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
												if length < normed_slice.shape[0]:
														UpperCamelCase =						padding_value

												normed_input_values.append(__a		)
								else:
										UpperCamelCase =						[(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

								return normed_input_values




						def __call__(self ,				__a ,				__a = False ,				__a = None ,				__a = False ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				**__a ,				)			-> BatchFeature:

								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."		)

								UpperCamelCase =						isinstance(__a ,				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}"		)
								UpperCamelCase =						is_batched_numpy or (
								    isinstance(__a ,				(list, tuple)		) and (isinstance(raw_speech[0] ,				(np.ndarray, tuple, list)		))
								)

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

								# convert into correct format for padding
								UpperCamelCase =						BatchFeature({"input_values": raw_speech}		)

								UpperCamelCase =						self.pad(
								    __a ,				padding=__a ,				max_length=__a ,				truncation=__a ,				pad_to_multiple_of=__a ,				return_attention_mask=__a ,				)

								# convert input values to correct format
								UpperCamelCase =						padded_inputs["input_values"]
								if not isinstance(input_values[0] ,				np.ndarray		):
										UpperCamelCase =						[np.asarray(__a ,				dtype=np.floataa		) for array in input_values]
								elif (
								    not isinstance(__a ,				np.ndarray		)
								    and isinstance(input_values[0] ,				np.ndarray		)
								    and input_values[0].dtype is np.dtype(np.floataa		)
								):
										UpperCamelCase =						[array.astype(np.floataa		) for array in input_values]
								elif isinstance(__a ,				np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
										UpperCamelCase =						input_values.astype(np.floataa		)

								# convert attention_mask to correct format
								UpperCamelCase =						padded_inputs.get("attention_mask"		)
								if attention_mask is not None:
										UpperCamelCase =						[np.asarray(__a ,				dtype=np.intaa		) for array in attention_mask]

								# zero-mean and unit-variance normalization
								if self.do_normalize:
										UpperCamelCase =						(
										    attention_mask
										    if self._get_padding_strategies(__a ,				max_length=__a		) is not PaddingStrategy.DO_NOT_PAD
										    else None
										)
										UpperCamelCase =						self.zero_mean_unit_var_norm(
										    padded_inputs["input_values"] ,				attention_mask=__a ,				padding_value=self.padding_value		)

								if return_tensors is not None:
										UpperCamelCase =						padded_inputs.convert_to_tensors(__a		)

								return padded_inputs













"""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    a__       (       _SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None     ):




		"""simple docstring"""


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





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



def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Tuple:
								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      snake_case_ (self		)			-> Optional[int]:
								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__       (       ):




		"""simple docstring"""


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


if __name__ == "__main__":
						main()






