



"""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__				:    int=None   ,       lowerCAmelCase__				:    List[str]=None						)						->       List[str]:

  """simple docstring"""




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







@dataclass
class 				UpperCamelCase__       :




      """simple docstring"""




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




def     UpperCamelCase_   (   lowerCAmelCase__				:    Tuple						)						->       Dict:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    Tuple						)						->       List[str]:

  """simple docstring"""




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







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    str								=			args
        lowerCAmelCase_       :    List[Any]								=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    int								=			plt.subplots()
        lowerCAmelCase_       :    int								=			'Time usage' if self.args.is_time else 'Memory usage'
        lowerCAmelCase_       :    str								=			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_       :    int								=			sorted(set(self.result_dict[model_name]['bsz']	)	)
          lowerCAmelCase_       :    Tuple								=			sorted(set(self.result_dict[model_name]['seq_len']	)	)
          lowerCAmelCase_       :    Union[str, Any]								=			self.result_dict[model_name]['result']

          (lowerCAmelCase_)       :    Dict								=			(
              (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
          )

          lowerCAmelCase_       :    Union[str, Any]								=			(
              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_       :    List[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:
              lowerCAmelCase_       :    Optional[Any]								=			np.asarray(
                  [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,      dtype=np.floataa   ,      )

            (lowerCAmelCase_)       :    Union[str, Any]								=			(
                ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
            )

            lowerCAmelCase_       :    List[Any]								=			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."

        lowerCAmelCase_       :    Dict								=			title_str[:-4]
        lowerCAmelCase_       :    List[str]								=			'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     UpperCamelCase_   (   )						->       str:

  """simple docstring"""




  lowerCAmelCase_       :    List[Any]								=			HfArgumentParser(lowerCAmelCase__						)
  lowerCAmelCase_       :    List[str]								=			parser.parse_args_into_dataclasses()[0]
  lowerCAmelCase_       :    Optional[Any]								=			Plot(args=lowerCAmelCase__						)
  plot.plot()


if __name__ == "__main__":
     main()










"""simple docstring"""







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


lowercase__		:    Optional[int]				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_, lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""maskformer-swin"""

      _SCREAMING_SNAKE_CASE      =					{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }



      def __init__(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2_2_4   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=4   ,      SCREAMING_SNAKE_CASE_    :				int=3   ,      SCREAMING_SNAKE_CASE_    :				Dict=9_6   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=[2, 2, 6, 2]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=[3, 6, 1_2, 2_4]   ,      SCREAMING_SNAKE_CASE_    :				List[str]=7   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=4.0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.0   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      SCREAMING_SNAKE_CASE_    :				int=0.1   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Dict=False   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				int=1E-5   ,      SCREAMING_SNAKE_CASE_    :				Dict=None   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Dict								=			image_size
        lowerCAmelCase_       :    Optional[Any]								=			patch_size
        lowerCAmelCase_       :    Optional[int]								=			num_channels
        lowerCAmelCase_       :    List[str]								=			embed_dim
        lowerCAmelCase_       :    Dict								=			depths
        lowerCAmelCase_       :    Optional[Any]								=			len(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			num_heads
        lowerCAmelCase_       :    List[str]								=			window_size
        lowerCAmelCase_       :    Any								=			mlp_ratio
        lowerCAmelCase_       :    Any								=			qkv_bias
        lowerCAmelCase_       :    List[Any]								=			hidden_dropout_prob
        lowerCAmelCase_       :    List[Any]								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Tuple								=			drop_path_rate
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    Any								=			use_absolute_embeddings
        lowerCAmelCase_       :    Optional[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			initializer_range
        # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        lowerCAmelCase_       :    List[str]								=			int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_	) - 1)	)
        lowerCAmelCase_       :    List[Any]								=			['stem'] + [F"stage{idx}" for idx in range(1   ,      len(SCREAMING_SNAKE_CASE_	) + 1	)]
        lowerCAmelCase_						,lowerCAmelCase_       :    Tuple								=			get_aligned_output_features_output_indices(
            out_features=SCREAMING_SNAKE_CASE_   ,      out_indices=SCREAMING_SNAKE_CASE_   ,      stage_names=self.stage_names	)





