'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
A_     :						Dict    							=logging.get_logger(__name__)



def snake_case_      (					__snake_case  :							Any)			->					Tuple:
   # initialize config
   if "resnet-50" in model_name:
      lowerCAmelCase_			=      ResNetConfig.from_pretrained('''microsoft/resnet-50''')
   elif "resnet-101" in model_name:
      lowerCAmelCase_			=      ResNetConfig.from_pretrained('''microsoft/resnet-101''')
   else:
      raise ValueError('''Model name should include either resnet50 or resnet101''')

   lowerCAmelCase_			=      DetrConfig(use_timm_backbone=__snake_case	,   backbone_config=__snake_case)

   # set label attributes
   lowerCAmelCase_			=      '''panoptic''' in model_name
   if is_panoptic:
      lowerCAmelCase_			=      250
   else:
      lowerCAmelCase_			=      91
      lowerCAmelCase_			=      '''huggingface/label-files'''
      lowerCAmelCase_			=      '''coco-detection-id2label.json'''
      lowerCAmelCase_			=      json.load(open(hf_hub_download(__snake_case	,   __snake_case	,   repo_type='''dataset''')	,   '''r'''))
      lowerCAmelCase_			=      {int(__snake_case): v for k, v in idalabel.items()}
      lowerCAmelCase_			=      idalabel
      lowerCAmelCase_			=      {v: k for k, v in idalabel.items()}

   return config, is_panoptic



def snake_case_      (					__snake_case  :							Optional[Any])			->					Tuple:
   # here we list all keys to be renamed (original name on the left, our name on the right)
   lowerCAmelCase_			=      []

   # stem
   # fmt: off
   rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight'''))
   rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight'''))
   rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias'''))
   rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'''))
   rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var'''))
   # stages
   for stage_idx in range(len(config.backbone_config.depths)):
      for layer_idx in range(config.backbone_config.depths[stage_idx]):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
                ))
         # 3 convs
         for i in range(3):
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                ))
    # fmt: on

   for i in range(config.encoder_layers):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (
              F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
              F'''encoder.layers.{i}.self_attn.out_proj.weight''',
          ))
      rename_keys.append(
          (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
      rename_keys.append(
          (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight'''))
      rename_keys.append(
          (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
      rename_keys.append(
          (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (
              F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
              F'''decoder.layers.{i}.self_attn.out_proj.weight''',
          ))
      rename_keys.append(
          (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias'''))
      rename_keys.append(
          (
              F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
              F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
          ))
      rename_keys.append(
          (
              F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
              F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
          ))
      rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
      rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
      rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
      rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
      rename_keys.append(
          (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight'''))
      rename_keys.append(
          (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
      rename_keys.append(
          (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight'''))
      rename_keys.append(
          (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias'''))
      rename_keys.append(
          (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
      rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

   # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
   rename_keys.extend(
       [
           ('''input_proj.weight''', '''input_projection.weight'''),
           ('''input_proj.bias''', '''input_projection.bias'''),
           ('''query_embed.weight''', '''query_position_embeddings.weight'''),
           ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
           ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
           ('''class_embed.weight''', '''class_labels_classifier.weight'''),
           ('''class_embed.bias''', '''class_labels_classifier.bias'''),
           ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
           ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
           ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
           ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
           ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
           ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
       ])

   return rename_keys



def snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Tuple	,   __snake_case  :							Optional[Any])			->					List[str]:
   lowerCAmelCase_			=      state_dict.pop(__snake_case)
   lowerCAmelCase_			=      val



def snake_case_      (					__snake_case  :							List[Any]	,   __snake_case  :							Optional[int]=False)			->					str:
   lowerCAmelCase_			=      ''''''
   if is_panoptic:
      lowerCAmelCase_			=      '''detr.'''

   # first: transformer encoder
   for i in range(6):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''')
      # next, add query, keys and values (in that order) to the state dict
      lowerCAmelCase_			=      in_proj_weight[:256, :]
      lowerCAmelCase_			=      in_proj_bias[:256]
      lowerCAmelCase_			=      in_proj_weight[256:512, :]
      lowerCAmelCase_			=      in_proj_bias[256:512]
      lowerCAmelCase_			=      in_proj_weight[-256:, :]
      lowerCAmelCase_			=      in_proj_bias[-256:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6):
      # read in weights + bias of input projection layer of self-attention
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''')
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''')
      # next, add query, keys and values (in that order) to the state dict
      lowerCAmelCase_			=      in_proj_weight[:256, :]
      lowerCAmelCase_			=      in_proj_bias[:256]
      lowerCAmelCase_			=      in_proj_weight[256:512, :]
      lowerCAmelCase_			=      in_proj_bias[256:512]
      lowerCAmelCase_			=      in_proj_weight[-256:, :]
      lowerCAmelCase_			=      in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      lowerCAmelCase_			=      state_dict.pop(
          F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''')
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''')
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      lowerCAmelCase_			=      in_proj_weight_cross_attn[:256, :]
      lowerCAmelCase_			=      in_proj_bias_cross_attn[:256]
      lowerCAmelCase_			=      in_proj_weight_cross_attn[256:512, :]
      lowerCAmelCase_			=      in_proj_bias_cross_attn[256:512]
      lowerCAmelCase_			=      in_proj_weight_cross_attn[-256:, :]
      lowerCAmelCase_			=      in_proj_bias_cross_attn[-256:]



def snake_case_      (					)			->					str:
   lowerCAmelCase_			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCAmelCase_			=      Image.open(requests.get(__snake_case	,   stream=__snake_case).raw)

   return im



@torch.no_grad()
def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							List[Any]=None	,   __snake_case  :							Any=False)			->					Optional[Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      get_detr_config(__snake_case)

   # load original model from torch hub
   lowerCAmelCase_			=      {
       '''detr-resnet-50''': '''detr_resnet50''',
       '''detr-resnet-101''': '''detr_resnet101''',
   }
   logger.info(F'''Converting model {model_name}...''')
   lowerCAmelCase_			=      torch.hub.load('''facebookresearch/detr'''	,   model_name_to_original_name[model_name]	,   pretrained=__snake_case).eval()
   lowerCAmelCase_			=      detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(__snake_case):
      if is_panoptic:
         lowerCAmelCase_			=      '''detr.''' + src
      rename_key(__snake_case	,   __snake_case	,   __snake_case)
   # query, key and value matrices need special treatment
   read_in_q_k_v(__snake_case	,   is_panoptic=__snake_case)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   lowerCAmelCase_			=      '''detr.model.''' if is_panoptic else '''model.'''
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith('''detr''')
             and not key.startswith('''class_labels_classifier''')
             and not key.startswith('''bbox_predictor''')
         ):
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
         elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
            continue
         else:
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
      else:
         if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val

    # finally, create HuggingFace model and load state dict
   lowerCAmelCase_			=      DetrForSegmentation(__snake_case) if is_panoptic else DetrForObjectDetection(__snake_case)
   model.load_state_dict(__snake_case)
   model.eval()

   # verify our conversion on an image
   lowerCAmelCase_			=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
   lowerCAmelCase_			=      DetrImageProcessor(format=__snake_case)

   lowerCAmelCase_			=      processor(images=prepare_img()	,   return_tensors='''pt''')
   lowerCAmelCase_			=      encoding['''pixel_values''']

   lowerCAmelCase_			=      detr(__snake_case)
   lowerCAmelCase_			=      model(__snake_case)

   assert torch.allclose(outputs.logits	,   original_outputs['''pred_logits''']	,   atol=1E-3)
   assert torch.allclose(outputs.pred_boxes	,   original_outputs['''pred_boxes''']	,   atol=1E-3)
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks	,   original_outputs['''pred_masks''']	,   atol=1E-4)
   print('''Looks ok!''')

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
      Path(__snake_case).mkdir(exist_ok=__snake_case)
      model.save_pretrained(__snake_case)
      processor.save_pretrained(__snake_case)

   if push_to_hub:
      # Upload model and image processor to the hub
      logger.info('''Uploading PyTorch model and image processor to the hub...''')
      model.push_to_hub(F'''nielsr/{model_name}''')
      processor.push_to_hub(F'''nielsr/{model_name}''')


if __name__ == "__main__":
 A_     :						Any    							=argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''detr-resnet-50''',
     type=str,
     choices=['''detr-resnet-50''', '''detr-resnet-101'''],
     help='''Name of the DETR model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
 A_     :						Optional[int]    							=parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''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 snake_case_      (					__snake_case  :							Tuple=None	,   __snake_case  :							int=None)			->					int:
   return field(default_factory=lambda: default	,   metadata=__snake_case)


@dataclass
class     __UpperCAmelCase       :
  __A :				str											= field(
      metadata={'help': 'The csv file to plot.'}						,       )
  __A :				bool											= field(
      default=__a						,       metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}						,       )
  __A :				bool											= field(
      default=__a						,       metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}						,       )
  __A :				bool											= field(
      default=__a						,       metadata={'help': 'Disable logarithmic scale when plotting'}						,       )
  __A :				bool											= field(
      default=__a						,       metadata={
          'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
      }						,       )
  __A :				Optional[str]											= field(
      default=__a						,       metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}						,       )
  __A :				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 snake_case_      (					__snake_case  :							Optional[Any])			->					Dict:
   try:
      int(__snake_case)
      return True
   except ValueError:
      return False



def snake_case_      (					__snake_case  :							Dict)			->					int:
   try:
      float(__snake_case)
      return True
   except ValueError:
      return False


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      args
     lowerCAmelCase_			=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

     with open(self.args.csv_file    ,     newline='''''' ) as csv_file:
        lowerCAmelCase_			=      csv.DictReader(_lowerCamelCase )
        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 						UpperCAmelCase_	(	self ):
     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=_lowerCamelCase    ,     )
           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(_lowerCamelCase    ,     _lowerCamelCase )[: len(_lowerCamelCase )]
           plt.scatter(
               _lowerCamelCase    ,     _lowerCamelCase    ,     label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
           plt.plot(_lowerCamelCase    ,     _lowerCamelCase    ,     '''--''' )

        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(_lowerCamelCase )
     plt.xlabel(_lowerCamelCase )
     plt.ylabel(_lowerCamelCase )
     plt.legend()

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






def snake_case_      (					)			->					Tuple:
   lowerCAmelCase_			=      HfArgumentParser(__snake_case)
   lowerCAmelCase_			=      parser.parse_args_into_dataclasses()[0]
   lowerCAmelCase_			=      Plot(args=__snake_case)
   plot.plot()


if __name__ == "__main__":
 main()





