






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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase=None ,   __lowerCAmelCase=None      )			->     int:
     return field(default_factory=lambda: default ,   metadata=__lowerCAmelCase      )





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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Tuple:
     try:
          int(__lowerCAmelCase      )
          return True
     except ValueError:
          return False







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Any:
     try:
          float(__lowerCAmelCase      )
          return True
     except ValueError:
          return False





class       __a  :







 def __init__(		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  args
      UpperCamelCase__      :       Union[str, Any]	    =  defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

      with open(self.args.csv_file							,       newline=""						) as csv_file:
           UpperCamelCase__      :       str	    =  csv.DictReader(SCREAMING_SNAKE_CASE						)
           for row in reader:
                UpperCamelCase__      :       Tuple	    =  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__      :       Optional[int]	    =  int(row["result"]						)
                elif can_convert_to_float(row["result"]						):
                     # value is not None
                     UpperCamelCase__      :       str	    =  float(row["result"]						)







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  plt.subplots()
      UpperCamelCase__      :       str	    =  "Time usage" if self.args.is_time else "Memory usage"
      UpperCamelCase__      :       int	    =  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__      :       Tuple	    =  sorted(set(self.result_dict[model_name]["bsz"]						)						)
           UpperCamelCase__      :       Any	    =  sorted(set(self.result_dict[model_name]["seq_len"]						)						)
           UpperCamelCase__      :       Any	    =  self.result_dict[model_name]["result"]

           ((UpperCamelCase__)    ,					(UpperCamelCase__))      :       Optional[Any]	    =  (
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           UpperCamelCase__      :       Optional[int]	    =  (
               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__      :       str	    =  np.asarray(
                         [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,       dtype=SCREAMING_SNAKE_CASE							,       )
                else:
                     UpperCamelCase__      :       List[Any]	    =  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__))      :       str	    =  (
                    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                )

                UpperCamelCase__      :       Dict	    =  np.asarray(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)[: len(SCREAMING_SNAKE_CASE						)]
                plt.scatter(
                    SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       label=F'{label_model_name} - {inner_loop_label}: {inner_loop_value}'						)
                plt.plot(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       "--"						)

           title_str += F' {label_model_name} vs.'

      UpperCamelCase__      :       Union[str, Any]	    =  title_str[:-4]
      UpperCamelCase__      :       Any	    =  "Time in s" if self.args.is_time else "Memory in MB"

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

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







def 		SCREAMING_SNAKE_CASE							( )			->     str:
     UpperCamelCase__      :       Tuple	    =  HfArgumentParser(__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[Any]	    =  parser.parse_args_into_dataclasses()[0]
     UpperCamelCase__      :       Dict	    =  Plot(args=__lowerCAmelCase      )
     plot.plot()


if __name__ == "__main__":
 main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase     :							List[Any]    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}





class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Union[str, Any]				=			'''bit'''
 _lowerCAmelCase	:	List[str]				=			['''preactivation''', '''bottleneck''']
 _lowerCAmelCase	:	Any				=			['''SAME''', '''VALID''']







 def __init__(		self :       str							,       SCREAMING_SNAKE_CASE :       Tuple=3							,       SCREAMING_SNAKE_CASE :       str=64							,       SCREAMING_SNAKE_CASE :       List[Any]=[2_56, 5_12, 10_24, 20_48]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=[3, 4, 6, 3]							,       SCREAMING_SNAKE_CASE :       str="preactivation"							,       SCREAMING_SNAKE_CASE :       Any="relu"							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=32							,       SCREAMING_SNAKE_CASE :       Tuple=0.0							,       SCREAMING_SNAKE_CASE :       Dict=False							,       SCREAMING_SNAKE_CASE :       int=32							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       List[str]=None							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      if layer_type not in self.layer_types:
           raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
      if global_padding is not None:
           if global_padding.upper() in self.supported_padding:
                UpperCamelCase__      :       Any	    =  global_padding.upper()
           else:
                raise ValueError(F'Padding strategy {global_padding} not supported'						)
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       Dict	    =  embedding_size
      UpperCamelCase__      :       Tuple	    =  hidden_sizes
      UpperCamelCase__      :       Any	    =  depths
      UpperCamelCase__      :       Optional[int]	    =  layer_type
      UpperCamelCase__      :       int	    =  hidden_act
      UpperCamelCase__      :       str	    =  global_padding
      UpperCamelCase__      :       Any	    =  num_groups
      UpperCamelCase__      :       str	    =  drop_path_rate
      UpperCamelCase__      :       Optional[Any]	    =  embedding_dynamic_padding
      UpperCamelCase__      :       Tuple	    =  output_stride
      UpperCamelCase__      :       List[str]	    =  width_factor

      UpperCamelCase__      :       Any	    =  ["stem"] + [F'stage{idx}' for idx in range(1							,       len(SCREAMING_SNAKE_CASE						) + 1						)]
      UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,       out_indices=SCREAMING_SNAKE_CASE							,       stage_names=self.stage_names						)