





'''simple docstring'''






import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor

A__    :       List[Any]              =random.Random()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase=1.0							,     lowerCAmelCase=None							,     lowerCAmelCase=None  ):





						"""simple docstring"""


						if rng is None:
												_lowerCAmelCase												=							global_rng

						_lowerCAmelCase												=							[]
						for batch_idx in range(shape[0]  ):
												values.append([]  )
												for _ in range(shape[1]  ):
																		values[-1].append(rng.random() * scale  )

						return values




@require_torch
@require_torchaudio
class 			UpperCAmelCase					(      unittest.TestCase       ):




						def __init__(      self      :			List[str]      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			List[str]=7      ,      __snake_case      :			Optional[Any]=4_00      ,      __snake_case      :			Any=20_00      ,      __snake_case      :			Optional[Any]=24      ,      __snake_case      :			Union[str, Any]=24      ,      __snake_case      :			Union[str, Any]=0.0      ,      __snake_case      :			List[Any]=1_60_00      ,      __snake_case      :			int=True      ,      __snake_case      :			Tuple=True      ,      )    ->				Optional[int]:
												_lowerCAmelCase												=							parent
												_lowerCAmelCase												=							batch_size
												_lowerCAmelCase												=							min_seq_length
												_lowerCAmelCase												=							max_seq_length
												_lowerCAmelCase												=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												_lowerCAmelCase												=							feature_size
												_lowerCAmelCase												=							num_mel_bins
												_lowerCAmelCase												=							padding_value
												_lowerCAmelCase												=							sampling_rate
												_lowerCAmelCase												=							return_attention_mask
												_lowerCAmelCase												=							do_normalize




						def     lowercase__   (      self      :			Optional[Any]  )    ->				Any:
												return {
												    "feature_size": self.feature_size,
												    "num_mel_bins": self.num_mel_bins,
												    "padding_value": self.padding_value,
												    "sampling_rate": self.sampling_rate,
												    "return_attention_mask": self.return_attention_mask,
												    "do_normalize": self.do_normalize,
												}




						def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			Any=False      ,      __snake_case      :			Any=False  )    ->				Union[str, Any]:
												def _flatten(__snake_case      :			Tuple  ):
																		return list(itertools.chain(*__snake_case  )  )

												if equal_length:
																		_lowerCAmelCase												=							[floats_list((self.max_seq_length, self.feature_size)  ) for _ in range(self.batch_size  )]
												else:
																		# make sure that inputs increase in size
																		_lowerCAmelCase												=							[
																		    floats_list((x, self.feature_size)  )
																		    for x in range(self.min_seq_length      ,      self.max_seq_length      ,      self.seq_length_diff  )
																		]
												if numpify:
																		_lowerCAmelCase												=							[np.asarray(__snake_case  ) for x in speech_inputs]
												return speech_inputs




@require_torch
@require_torchaudio
class 			UpperCAmelCase					(      snake_case_							,		unittest.TestCase       ):
						_lowercase:      Any											=       SpeechaTextFeatureExtractor if is_speech_available() else None




						def     lowercase__   (      self      :			Any  )    ->				int:
												_lowerCAmelCase												=							SpeechaTextFeatureExtractionTester(self  )




						def     lowercase__   (      self      :			str      ,      __snake_case      :			Optional[Any]  )    ->				Dict:
												self.assertTrue(np.all(np.mean(__snake_case      ,      axis=0  ) < 1E-3  )  )
												self.assertTrue(np.all(np.abs(np.var(__snake_case      ,      axis=0  ) - 1  ) < 1E-3  )  )




						def     lowercase__   (      self      :			Tuple  )    ->				List[str]:
												# Tests that all call wrap to encode_plus and batch_encode_plus
												_lowerCAmelCase												=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
												# create three inputs of length 800, 1000, and 1200
												_lowerCAmelCase												=							[floats_list((1, x)  )[0] for x in range(8_00      ,      14_00      ,      2_00  )]
												_lowerCAmelCase												=							[np.asarray(__snake_case  ) for speech_input in speech_inputs]

												# Test feature size
												_lowerCAmelCase												=							feature_extractor(__snake_case      ,      padding=__snake_case      ,      return_tensors="""np"""  ).input_features
												self.assertTrue(input_features.ndim == 3  )
												self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size  )

												# Test not batched input
												_lowerCAmelCase												=							feature_extractor(speech_inputs[0]      ,      return_tensors="""np"""  ).input_features
												_lowerCAmelCase												=							feature_extractor(np_speech_inputs[0]      ,      return_tensors="""np"""  ).input_features
												self.assertTrue(np.allclose(__snake_case      ,      __snake_case      ,      atol=1E-3  )  )

												# Test batched
												_lowerCAmelCase												=							feature_extractor(__snake_case      ,      return_tensors="""np"""  ).input_features
												_lowerCAmelCase												=							feature_extractor(__snake_case      ,      return_tensors="""np"""  ).input_features
												for enc_seq_a, enc_seq_a in zip(__snake_case      ,      __snake_case  ):
																		self.assertTrue(np.allclose(__snake_case      ,      __snake_case      ,      atol=1E-3  )  )

												# Test 2-D numpy arrays are batched.
												_lowerCAmelCase												=							[floats_list((1, x)  )[0] for x in (8_00, 8_00, 8_00)]
												_lowerCAmelCase												=							np.asarray(__snake_case  )
												_lowerCAmelCase												=							feature_extractor(__snake_case      ,      return_tensors="""np"""  ).input_features
												_lowerCAmelCase												=							feature_extractor(__snake_case      ,      return_tensors="""np"""  ).input_features
												for enc_seq_a, enc_seq_a in zip(__snake_case      ,      __snake_case  ):
																		self.assertTrue(np.allclose(__snake_case      ,      __snake_case      ,      atol=1E-3  )  )




						def     lowercase__   (      self      :			List[Any]  )    ->				List[Any]:
												_lowerCAmelCase												=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
												_lowerCAmelCase												=							[floats_list((1, x)  )[0] for x in range(8_00      ,      14_00      ,      2_00  )]

												_lowerCAmelCase												=							["""longest""", """max_length""", """do_not_pad"""]
												_lowerCAmelCase												=							[None, 16, None]
												for max_length, padding in zip(__snake_case      ,      __snake_case  ):
																		_lowerCAmelCase												=							feature_extractor(
																		    __snake_case      ,      padding=__snake_case      ,      max_length=__snake_case      ,      return_attention_mask=__snake_case  )
																		_lowerCAmelCase												=							inputs.input_features
																		_lowerCAmelCase												=							inputs.attention_mask
																		_lowerCAmelCase												=							[np.sum(__snake_case  ) for x in attention_mask]

																		self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]  )
																		self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]  )
																		self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]  )




						def     lowercase__   (      self      :			Union[str, Any]  )    ->				Tuple:
												_lowerCAmelCase												=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
												_lowerCAmelCase												=							[floats_list((1, x)  )[0] for x in range(8_00      ,      14_00      ,      2_00  )]

												_lowerCAmelCase												=							["""longest""", """max_length""", """do_not_pad"""]
												_lowerCAmelCase												=							[None, 16, None]
												for max_length, padding in zip(__snake_case      ,      __snake_case  ):
																		_lowerCAmelCase												=							feature_extractor(
																		    __snake_case      ,      max_length=__snake_case      ,      padding=__snake_case      ,      return_tensors="""np"""      ,      return_attention_mask=__snake_case  )
																		_lowerCAmelCase												=							inputs.input_features
																		_lowerCAmelCase												=							inputs.attention_mask
																		_lowerCAmelCase												=							[np.sum(__snake_case  ) for x in attention_mask]

																		self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]  )
																		self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6  )
																		self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]  )
																		self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6  )
																		self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]  )




						def     lowercase__   (      self      :			int  )    ->				Tuple:
												_lowerCAmelCase												=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
												_lowerCAmelCase												=							[floats_list((1, x)  )[0] for x in range(8_00      ,      14_00      ,      2_00  )]
												_lowerCAmelCase												=							feature_extractor(
												    __snake_case      ,      padding="""max_length"""      ,      max_length=4      ,      truncation=__snake_case      ,      return_tensors="""np"""      ,      return_attention_mask=__snake_case      ,      )
												_lowerCAmelCase												=							inputs.input_features
												_lowerCAmelCase												=							inputs.attention_mask
												_lowerCAmelCase												=							np.sum(attention_mask == 1      ,      axis=1  )

												self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]  )
												self._check_zero_mean_unit_variance(input_features[1]  )
												self._check_zero_mean_unit_variance(input_features[2]  )




						def     lowercase__   (      self      :			Tuple  )    ->				Optional[int]:
												_lowerCAmelCase												=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
												_lowerCAmelCase												=							[floats_list((1, x)  )[0] for x in range(8_00      ,      14_00      ,      2_00  )]
												_lowerCAmelCase												=							feature_extractor(
												    __snake_case      ,      padding="""longest"""      ,      max_length=4      ,      truncation=__snake_case      ,      return_tensors="""np"""      ,      return_attention_mask=__snake_case      ,      )
												_lowerCAmelCase												=							inputs.input_features
												_lowerCAmelCase												=							inputs.attention_mask
												_lowerCAmelCase												=							np.sum(attention_mask == 1      ,      axis=1  )

												self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]  )
												self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]  )
												self._check_zero_mean_unit_variance(input_features[2]  )

												# make sure that if max_length < longest -> then pad to max_length
												self.assertEqual(input_features.shape      ,      (3, 4, 24)  )

												_lowerCAmelCase												=							[floats_list((1, x)  )[0] for x in range(8_00      ,      14_00      ,      2_00  )]
												_lowerCAmelCase												=							feature_extractor(
												    __snake_case      ,      padding="""longest"""      ,      max_length=16      ,      truncation=__snake_case      ,      return_tensors="""np"""      ,      return_attention_mask=__snake_case      ,      )
												_lowerCAmelCase												=							inputs.input_features
												_lowerCAmelCase												=							inputs.attention_mask
												_lowerCAmelCase												=							np.sum(attention_mask == 1      ,      axis=1  )

												self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]  )
												self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]  )
												self._check_zero_mean_unit_variance(input_features[2]  )

												# make sure that if max_length < longest -> then pad to max_length
												self.assertEqual(input_features.shape      ,      (3, 6, 24)  )




						def     lowercase__   (      self      :			int  )    ->				List[str]:
												import torch

												_lowerCAmelCase												=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
												_lowerCAmelCase												=							np.random.rand(1_00      ,      32  ).astype(np.floataa  )
												_lowerCAmelCase												=							np_speech_inputs.tolist()

												for inputs in [py_speech_inputs, np_speech_inputs]:
																		_lowerCAmelCase												=							feature_extractor.pad([{"""input_features""": inputs}]      ,      return_tensors="""np"""  )
																		self.assertTrue(np_processed.input_features.dtype == np.floataa  )
																		_lowerCAmelCase												=							feature_extractor.pad([{"""input_features""": inputs}]      ,      return_tensors="""pt"""  )
																		self.assertTrue(pt_processed.input_features.dtype == torch.floataa  )




						def     lowercase__   (      self      :			Any      ,      __snake_case      :			Union[str, Any]  )    ->				Any:
												from datasets import load_dataset

												_lowerCAmelCase												=							load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,      """clean"""      ,      split="""validation"""  )
												# automatic decoding with librispeech
												_lowerCAmelCase												=							ds.sort("""id"""  ).select(range(__snake_case  )  )[:num_samples]["""audio"""]

												return [x["array"] for x in speech_samples]




						def     lowercase__   (      self      :			Tuple  )    ->				Optional[Any]:
												# fmt: off
												_lowerCAmelCase												=							np.array([
												    -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
												    -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
												    -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
												]  )
												# fmt: on

												_lowerCAmelCase												=							self._load_datasamples(1  )
												_lowerCAmelCase												=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
												_lowerCAmelCase												=							feature_extractor(__snake_case      ,      return_tensors="""pt"""  ).input_features
												self.assertEquals(input_features.shape      ,      (1, 5_84, 24)  )
												self.assertTrue(np.allclose(input_features[0, 0, :30]      ,      __snake_case      ,      atol=1E-4  )  )













'''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        UpperCamelCase__				(		lowerCAmelCase=None							,     lowerCAmelCase=None  ):





      """simple docstring"""


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




@dataclass
class 			UpperCAmelCase					:
      _lowercase:      str											=       field(
          metadata={'''help''': '''The csv file to plot.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Disable logarithmic scale when plotting'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={
              '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
          }							,		)
      _lowercase:      Optional[str]											=       field(
          default=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=snake_case_							,		metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}       )




def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      try:
            int(lowerCAmelCase  )
            return True
      except ValueError:
            return False
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


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




class 			UpperCAmelCase					:




      def __init__(      self      :			List[str]      ,      __snake_case      :			Union[str, Any]  )    ->				int:
            _lowerCAmelCase												=							args
            _lowerCAmelCase												=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

            with open(self.args.csv_file      ,      newline=""""""  ) as csv_file:
                  _lowerCAmelCase												=							csv.DictReader(__snake_case  )
                  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     lowercase__   (      self      :			Dict  )    ->				str:
            _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=__snake_case      ,      )
                        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(__snake_case      ,      __snake_case  )[: len(__snake_case  )]
                        plt.scatter(
                            __snake_case      ,      __snake_case      ,      label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"  )
                        plt.plot(__snake_case      ,      __snake_case      ,      """--"""  )

                  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(__snake_case  )
            plt.xlabel(__snake_case  )
            plt.ylabel(__snake_case  )
            plt.legend()

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




def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							HfArgumentParser(lowerCAmelCase  )
      _lowerCAmelCase												=							parser.parse_args_into_dataclasses()[0]
      _lowerCAmelCase												=							Plot(args=lowerCAmelCase  )
      plot.plot()


if __name__ == "__main__":
      main()






