





'''simple docstring'''






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__    :       str              =logging.get_logger(__name__)

A__    :       List[Any]              ={
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class 			UpperCAmelCase					(      snake_case_       ):
						_lowercase:      Tuple											=       '''wavlm'''




						def __init__(      self      :			Dict      ,      __snake_case      :			Optional[Any]=32      ,      __snake_case      :			int=7_68      ,      __snake_case      :			Dict=12      ,      __snake_case      :			Union[str, Any]=12      ,      __snake_case      :			List[Any]=30_72      ,      __snake_case      :			Any="gelu"      ,      __snake_case      :			int=0.1      ,      __snake_case      :			Dict=0.1      ,      __snake_case      :			Optional[Any]=0.1      ,      __snake_case      :			List[str]=0.0      ,      __snake_case      :			Any=0.1      ,      __snake_case      :			List[Any]=0.1      ,      __snake_case      :			int=0.02      ,      __snake_case      :			Any=1E-5      ,      __snake_case      :			Optional[int]="group"      ,      __snake_case      :			int="gelu"      ,      __snake_case      :			int=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)      ,      __snake_case      :			Tuple=(5, 2, 2, 2, 2, 2, 2)      ,      __snake_case      :			Any=(10, 3, 3, 3, 3, 2, 2)      ,      __snake_case      :			Optional[int]=False      ,      __snake_case      :			int=1_28      ,      __snake_case      :			List[Any]=16      ,      __snake_case      :			List[str]=3_20      ,      __snake_case      :			int=8_00      ,      __snake_case      :			int=False      ,      __snake_case      :			List[str]=True      ,      __snake_case      :			Tuple=0.05      ,      __snake_case      :			Optional[Any]=10      ,      __snake_case      :			List[str]=2      ,      __snake_case      :			Union[str, Any]=0.0      ,      __snake_case      :			List[Any]=10      ,      __snake_case      :			Dict=3_20      ,      __snake_case      :			Optional[Any]=2      ,      __snake_case      :			List[Any]=0.1      ,      __snake_case      :			str=1_00      ,      __snake_case      :			Optional[int]=2_56      ,      __snake_case      :			Union[str, Any]=2_56      ,      __snake_case      :			str=0.1      ,      __snake_case      :			Optional[Any]="mean"      ,      __snake_case      :			Tuple=False      ,      __snake_case      :			List[Any]=False      ,      __snake_case      :			Optional[Any]=2_56      ,      __snake_case      :			str=(5_12, 5_12, 5_12, 5_12, 15_00)      ,      __snake_case      :			Any=(5, 3, 3, 1, 1)      ,      __snake_case      :			List[Any]=(1, 2, 3, 1, 1)      ,      __snake_case      :			Any=5_12      ,      __snake_case      :			List[str]=80      ,      __snake_case      :			str=0      ,      __snake_case      :			Optional[Any]=1      ,      __snake_case      :			Any=2      ,      __snake_case      :			int=False      ,      __snake_case      :			List[str]=3      ,      __snake_case      :			List[Any]=2      ,      __snake_case      :			int=3      ,      __snake_case      :			Optional[Any]=None      ,      **__snake_case      :			Tuple      ,      )    ->				Dict:
												super().__init__(**_SCREAMING_SNAKE_CASE      ,      pad_token_id=_SCREAMING_SNAKE_CASE      ,      bos_token_id=_SCREAMING_SNAKE_CASE      ,      eos_token_id=_SCREAMING_SNAKE_CASE  )
												_lowerCAmelCase												=							hidden_size
												_lowerCAmelCase												=							feat_extract_norm
												_lowerCAmelCase												=							feat_extract_activation
												_lowerCAmelCase												=							list(_SCREAMING_SNAKE_CASE  )
												_lowerCAmelCase												=							list(_SCREAMING_SNAKE_CASE  )
												_lowerCAmelCase												=							list(_SCREAMING_SNAKE_CASE  )
												_lowerCAmelCase												=							conv_bias
												_lowerCAmelCase												=							num_buckets
												_lowerCAmelCase												=							max_bucket_distance
												_lowerCAmelCase												=							num_conv_pos_embeddings
												_lowerCAmelCase												=							num_conv_pos_embedding_groups
												_lowerCAmelCase												=							len(self.conv_dim  )
												_lowerCAmelCase												=							num_hidden_layers
												_lowerCAmelCase												=							intermediate_size
												_lowerCAmelCase												=							hidden_act
												_lowerCAmelCase												=							num_attention_heads
												_lowerCAmelCase												=							hidden_dropout
												_lowerCAmelCase												=							attention_dropout
												_lowerCAmelCase												=							activation_dropout
												_lowerCAmelCase												=							feat_proj_dropout
												_lowerCAmelCase												=							final_dropout
												_lowerCAmelCase												=							layerdrop
												_lowerCAmelCase												=							layer_norm_eps
												_lowerCAmelCase												=							initializer_range
												_lowerCAmelCase												=							num_ctc_classes
												_lowerCAmelCase												=							vocab_size
												_lowerCAmelCase												=							do_stable_layer_norm
												_lowerCAmelCase												=							use_weighted_layer_sum
												_lowerCAmelCase												=							classifier_proj_size

												if (
												    (len(self.conv_stride  ) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
												    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
												):
																		raise ValueError(
																		    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																		    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																		    f" {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,"
																		    f" `len(config.conv_kernel) = {len(self.conv_kernel  )}`."  )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												_lowerCAmelCase												=							apply_spec_augment
												_lowerCAmelCase												=							mask_time_prob
												_lowerCAmelCase												=							mask_time_length
												_lowerCAmelCase												=							mask_time_min_masks
												_lowerCAmelCase												=							mask_feature_prob
												_lowerCAmelCase												=							mask_feature_length

												# parameters for pretraining with codevector quantized representations
												_lowerCAmelCase												=							num_codevectors_per_group
												_lowerCAmelCase												=							num_codevector_groups
												_lowerCAmelCase												=							contrastive_logits_temperature
												_lowerCAmelCase												=							num_negatives
												_lowerCAmelCase												=							codevector_dim
												_lowerCAmelCase												=							proj_codevector_dim
												_lowerCAmelCase												=							diversity_loss_weight

												# ctc loss
												_lowerCAmelCase												=							ctc_loss_reduction
												_lowerCAmelCase												=							ctc_zero_infinity

												# adapter
												_lowerCAmelCase												=							add_adapter
												_lowerCAmelCase												=							adapter_kernel_size
												_lowerCAmelCase												=							adapter_stride
												_lowerCAmelCase												=							num_adapter_layers
												_lowerCAmelCase												=							output_hidden_size or hidden_size

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												_lowerCAmelCase												=							classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												_lowerCAmelCase												=							list(_SCREAMING_SNAKE_CASE  )
												_lowerCAmelCase												=							list(_SCREAMING_SNAKE_CASE  )
												_lowerCAmelCase												=							list(_SCREAMING_SNAKE_CASE  )
												_lowerCAmelCase												=							xvector_output_dim




						@property
						def     lowercase__   (      self      :			Union[str, Any]  )    ->				Optional[int]:
												return functools.reduce(operator.mul      ,      self.conv_stride      ,      1  )













'''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()






