



'''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 A__    (     UpperCAmelCase_=None  ,  UpperCAmelCase_=None							):
						return field(default_factory=lambda: default  ,  metadata=UpperCAmelCase_							)






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






def A__    (     UpperCAmelCase_							):
						try:
												int(UpperCAmelCase_							)
												return True
						except ValueError:
												return False






def A__    (     UpperCAmelCase_							):
						try:
												float(UpperCAmelCase_							)
												return True
						except ValueError:
												return False






class 							lowercase__	:





	def __init__( self					:				List[Any] ,lowerCamelCase__					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   args
							_UpperCamelCase					:		Optional[Any] 				=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

							with open(self.args.csv_file ,newline='' ) as csv_file:
													_UpperCamelCase					:		List[Any] 				=   csv.DictReader(lowerCamelCase__ )
													for row in reader:
																			_UpperCamelCase					:		Any 				=   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					:		Dict 				=   float(row['result'] )





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase				,			_UpperCamelCase					:		Optional[int] 				=   plt.subplots()
							_UpperCamelCase					:		List[str] 				=   'Time usage' if self.args.is_time else 'Memory usage'
							_UpperCamelCase					:		List[Any] 				=   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					:		Dict 				=   sorted(set(self.result_dict[model_name]['bsz'] ) )
													_UpperCamelCase					:		Optional[int] 				=   sorted(set(self.result_dict[model_name]['seq_len'] ) )
													_UpperCamelCase					:		List[str] 				=   self.result_dict[model_name]['result']

													((_UpperCamelCase)				,			(_UpperCamelCase))					:		Tuple 				=   (
													    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
													)

													_UpperCamelCase					:		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:
																									_UpperCamelCase					:		Optional[Any] 				=   np.asarray(
																									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,dtype=lowerCamelCase__ ,)
																			else:
																									_UpperCamelCase					:		str 				=   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))					:		Tuple 				=   (
																			    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																			)

																			_UpperCamelCase					:		Dict 				=   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.'

							_UpperCamelCase					:		Optional[Any] 				=   title_str[:-4]
							_UpperCamelCase					:		str 				=   '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 A__    (     ):
						_UpperCamelCase					:		str 				=   HfArgumentParser(UpperCAmelCase_							)
						_UpperCamelCase					:		Dict 				=   parser.parse_args_into_dataclasses()[0]
						_UpperCamelCase					:		List[str] 				=   Plot(args=UpperCAmelCase_							)
						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


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Optional[Any]				=      '''focalnet'''




  def __init__(    self  ,__UpperCAmelCase=224  ,__UpperCAmelCase=4  ,__UpperCAmelCase=3  ,__UpperCAmelCase=96  ,__UpperCAmelCase=False  ,__UpperCAmelCase=[192, 384, 768, 768]  ,__UpperCAmelCase=[2, 2, 6, 2]  ,__UpperCAmelCase=[2, 2, 2, 2]  ,__UpperCAmelCase=[3, 3, 3, 3]  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=4.0  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=False  ,__UpperCAmelCase=1E-4  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-5  ,__UpperCAmelCase=32  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		Optional[Any]:
      super().__init__(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					image_size
      lowerCAmelCase__ :  int							      =					patch_size
      lowerCAmelCase__ :  str							      =					num_channels
      lowerCAmelCase__ :  Dict							      =					embed_dim
      lowerCAmelCase__ :  List[str]							      =					use_conv_embed
      lowerCAmelCase__ :  List[Any]							      =					hidden_sizes
      lowerCAmelCase__ :  Dict							      =					depths
      lowerCAmelCase__ :  List[str]							      =					focal_levels
      lowerCAmelCase__ :  List[str]							      =					focal_windows
      lowerCAmelCase__ :  Dict							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					mlp_ratio
      lowerCAmelCase__ :  Tuple							      =					hidden_dropout_prob
      lowerCAmelCase__ :  Tuple							      =					drop_path_rate
      lowerCAmelCase__ :  Dict							      =					use_layerscale
      lowerCAmelCase__ :  Optional[Any]							      =					layerscale_value
      lowerCAmelCase__ :  str							      =					use_post_layernorm
      lowerCAmelCase__ :  Union[str, Any]							      =					use_post_layernorm_in_modulation
      lowerCAmelCase__ :  int							      =					normalize_modulator
      lowerCAmelCase__ :  Optional[Any]							      =					initializer_range
      lowerCAmelCase__ :  List[str]							      =					layer_norm_eps
      lowerCAmelCase__ :  List[Any]							      =					encoder_stride
      lowerCAmelCase__ :  Dict							      =					["""stem"""] + [F"""stage{idx}""" for idx in range(1  ,len(self.depths      ) + 1      )]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase  ,out_indices=__UpperCAmelCase  ,stage_names=self.stage_names      )


