'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A_     :						Tuple    							=logging.get_logger(__name__)

A_     :						Dict    							={
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class     __UpperCAmelCase       ( __a		):
  __A :				List[Any]											= 'deformable_detr'
  __A :				Union[str, Any]											= {
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }





  def __init__(	self    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     _lowerCamelCase=3    ,     _lowerCamelCase=300    ,     _lowerCamelCase=1024    ,     _lowerCamelCase=6    ,     _lowerCamelCase=1024    ,     _lowerCamelCase=8    ,     _lowerCamelCase=6    ,     _lowerCamelCase=1024    ,     _lowerCamelCase=8    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=True    ,     _lowerCamelCase="relu"    ,     _lowerCamelCase=256    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=0.02    ,     _lowerCamelCase=1.0    ,     _lowerCamelCase=True    ,     _lowerCamelCase=False    ,     _lowerCamelCase="sine"    ,     _lowerCamelCase="resnet50"    ,     _lowerCamelCase=True    ,     _lowerCamelCase=False    ,     _lowerCamelCase=4    ,     _lowerCamelCase=4    ,     _lowerCamelCase=4    ,     _lowerCamelCase=False    ,     _lowerCamelCase=300    ,     _lowerCamelCase=False    ,     _lowerCamelCase=1    ,     _lowerCamelCase=5    ,     _lowerCamelCase=2    ,     _lowerCamelCase=1    ,     _lowerCamelCase=1    ,     _lowerCamelCase=5    ,     _lowerCamelCase=2    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.25    ,     _lowerCamelCase=False    ,     **_lowerCamelCase    ,     ):
     if backbone_config is not None and use_timm_backbone:
        raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )

     if not use_timm_backbone:
        if backbone_config is None:
           logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
           lowerCAmelCase_			=      CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
        elif isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
           lowerCAmelCase_			=      backbone_config.get('''model_type''' )
           lowerCAmelCase_			=      CONFIG_MAPPING[backbone_model_type]
           lowerCAmelCase_			=      config_class.from_dict(_lowerCamelCase )
     lowerCAmelCase_			=      use_timm_backbone
     lowerCAmelCase_			=      backbone_config
     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      num_queries
     lowerCAmelCase_			=      max_position_embeddings
     lowerCAmelCase_			=      d_model
     lowerCAmelCase_			=      encoder_ffn_dim
     lowerCAmelCase_			=      encoder_layers
     lowerCAmelCase_			=      encoder_attention_heads
     lowerCAmelCase_			=      decoder_ffn_dim
     lowerCAmelCase_			=      decoder_layers
     lowerCAmelCase_			=      decoder_attention_heads
     lowerCAmelCase_			=      dropout
     lowerCAmelCase_			=      attention_dropout
     lowerCAmelCase_			=      activation_dropout
     lowerCAmelCase_			=      activation_function
     lowerCAmelCase_			=      init_std
     lowerCAmelCase_			=      init_xavier_std
     lowerCAmelCase_			=      encoder_layerdrop
     lowerCAmelCase_			=      auxiliary_loss
     lowerCAmelCase_			=      position_embedding_type
     lowerCAmelCase_			=      backbone
     lowerCAmelCase_			=      use_pretrained_backbone
     lowerCAmelCase_			=      dilation
     # deformable attributes
     lowerCAmelCase_			=      num_feature_levels
     lowerCAmelCase_			=      encoder_n_points
     lowerCAmelCase_			=      decoder_n_points
     lowerCAmelCase_			=      two_stage
     lowerCAmelCase_			=      two_stage_num_proposals
     lowerCAmelCase_			=      with_box_refine
     if two_stage is True and with_box_refine is False:
        raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
     # Hungarian matcher
     lowerCAmelCase_			=      class_cost
     lowerCAmelCase_			=      bbox_cost
     lowerCAmelCase_			=      giou_cost
     # Loss coefficients
     lowerCAmelCase_			=      mask_loss_coefficient
     lowerCAmelCase_			=      dice_loss_coefficient
     lowerCAmelCase_			=      bbox_loss_coefficient
     lowerCAmelCase_			=      giou_loss_coefficient
     lowerCAmelCase_			=      eos_coefficient
     lowerCAmelCase_			=      focal_alpha
     lowerCAmelCase_			=      disable_custom_kernels
     super().__init__(is_encoder_decoder=_lowerCamelCase    ,     **_lowerCamelCase )





  @property
  def 						UpperCAmelCase_	(	self ):
     return self.encoder_attention_heads





  @property
  def 						UpperCAmelCase_	(	self ):
     return self.d_model







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      copy.deepcopy(self.__dict__ )
     if self.backbone_config is not None:
        lowerCAmelCase_			=      self.backbone_config.to_dict()
     lowerCAmelCase_			=      self.__class__.model_type
     return output






'''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()





