





'''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 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any]=None,      SCREAMING_SNAKE_CASE__   :      List[str]=None )			->						Optional[int]:
						return field(default_factory=lambda: default,      metadata=SCREAMING_SNAKE_CASE__ )


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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Optional[Any]:
						try:
												int(SCREAMING_SNAKE_CASE__ )
												return True
						except ValueError:
												return False





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						int:
						try:
												float(SCREAMING_SNAKE_CASE__ )
												return True
						except ValueError:
												return False


class        __a   :

							def __init__(				self				:  Optional[int]							,  __magic_name__				:  int )       ->							Optional[int]:







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

													with open(self.args.csv_file							,  newline='''''' ) as csv_file:
																			UpperCAmelCase_      :   Optional[int]      =	csv.DictReader(__magic_name__ )
																			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_      :   int      =	int(row['''result'''] )
																									elif can_convert_to_float(row['''result'''] ):
																															# value is not None
																															UpperCAmelCase_      :   Any      =	float(row['''result'''] )



							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							Tuple:







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

																			((UpperCAmelCase_)    ,							(UpperCAmelCase_))      :   int      =	(
																			    (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_      :   str      =	np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,  dtype=__magic_name__							,  )
																									else:
																															UpperCAmelCase_      :   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							,  )

																									((UpperCAmelCase_)    ,							(UpperCAmelCase_))      :   Any      =	(
																									    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																									)

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

																			title_str += F""" {label_model_name} vs."""

													UpperCAmelCase_      :   int      =	title_str[:-4]
													UpperCAmelCase_      :   List[str]      =	'''Time in s''' if self.args.is_time else '''Memory in MB'''

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

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





def 							lowerCamelCase_   (	)			->						str:
						UpperCAmelCase_      :   int      =	HfArgumentParser(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Dict      =	parser.parse_args_into_dataclasses()[0]
						UpperCAmelCase_      :   List[Any]      =	Plot(args=SCREAMING_SNAKE_CASE__ )
						plot.plot()


if __name__ == "__main__":
					main()













"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase           =							logging.get_logger(__name__)

lowercase           =							{
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}

class     A_				(   snake_case_	):







				def __init__(				self				:    List[Any]    ,     __lowerCamelCase				:    str=2_5_6    ,     __lowerCamelCase				:    str=1_0_2_4    ,     __lowerCamelCase				:    Optional[Any]=1_6    ,     __lowerCamelCase				:    Union[str, Any]=1_6    ,     __lowerCamelCase				:    str=4    ,     __lowerCamelCase				:    Union[str, Any]="gelu"    ,     __lowerCamelCase				:    Optional[int]=1e-6    ,     **__lowerCamelCase				:    str    ,     )					->       List[Any]:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  hidden_size
										__magic_name__      =  image_size
										__magic_name__      =  patch_size
										__magic_name__      =  image_size // patch_size
										__magic_name__      =  mask_input_channels
										__magic_name__      =  num_point_embeddings
										__magic_name__      =  hidden_act
										__magic_name__      =  layer_norm_eps





class     A_				(   snake_case_	):







				def __init__(				self				:    List[str]    ,     __lowerCamelCase				:    Optional[Any]=2_5_6    ,     __lowerCamelCase				:    int="relu"    ,     __lowerCamelCase				:    List[str]=2_0_4_8    ,     __lowerCamelCase				:    Optional[Any]=2    ,     __lowerCamelCase				:    Optional[Any]=8    ,     __lowerCamelCase				:    Optional[Any]=2    ,     __lowerCamelCase				:    Dict=3    ,     __lowerCamelCase				:    Optional[int]=3    ,     __lowerCamelCase				:    Tuple=2_5_6    ,     __lowerCamelCase				:    List[str]=1e-6    ,     **__lowerCamelCase				:    Any    ,     )					->       str:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  hidden_size
										__magic_name__      =  hidden_act
										__magic_name__      =  mlp_dim
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  attention_downsample_rate
										__magic_name__      =  num_multimask_outputs
										__magic_name__      =  iou_head_depth
										__magic_name__      =  iou_head_hidden_dim
										__magic_name__      =  layer_norm_eps





class     A_				(   snake_case_	):







				def __init__(				self				:    Union[str, Any]    ,     __lowerCamelCase				:    str=7_6_8    ,     __lowerCamelCase				:    Optional[int]=2_5_6    ,     __lowerCamelCase				:    Tuple=1_2    ,     __lowerCamelCase				:    int=1_2    ,     __lowerCamelCase				:    int=3    ,     __lowerCamelCase				:    int=1_0_2_4    ,     __lowerCamelCase				:    Any=1_6    ,     __lowerCamelCase				:    Tuple="gelu"    ,     __lowerCamelCase				:    Dict=1e-06    ,     __lowerCamelCase				:    List[Any]=0.0    ,     __lowerCamelCase				:    Optional[Any]=1e-10    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    List[str]=4.0    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    Optional[Any]=1_4    ,     __lowerCamelCase				:    str=[2, 5, 8, 1_1]    ,     __lowerCamelCase				:    List[Any]=1_2_8    ,     __lowerCamelCase				:    Dict=None    ,     **__lowerCamelCase				:    List[str]    ,     )					->       Optional[int]:
										super().__init__(**__lowerCamelCase						)

										__magic_name__      =  hidden_size
										__magic_name__      =  output_channels
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  num_channels
										__magic_name__      =  image_size
										__magic_name__      =  patch_size
										__magic_name__      =  hidden_act
										__magic_name__      =  layer_norm_eps
										__magic_name__      =  attention_dropout
										__magic_name__      =  initializer_range
										__magic_name__      =  qkv_bias
										__magic_name__      =  mlp_ratio
										__magic_name__      =  use_abs_pos
										__magic_name__      =  use_rel_pos
										__magic_name__      =  window_size
										__magic_name__      =  global_attn_indexes
										__magic_name__      =  num_pos_feats
										__magic_name__      =  int(hidden_size * mlp_ratio						) if mlp_dim is None else mlp_dim





class     A_				(   snake_case_	):
				UpperCAmelCase__										=					'''sam'''
				UpperCAmelCase__										=					True







				def __init__(				self				:    Optional[Any]    ,     __lowerCamelCase				:    Dict=None    ,     __lowerCamelCase				:    Dict=None    ,     __lowerCamelCase				:    Optional[Any]=None    ,     __lowerCamelCase				:    int=0.02    ,     **__lowerCamelCase				:    List[Any]    ,     )					->       List[str]:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  vision_config if vision_config is not None else {}
										__magic_name__      =  prompt_encoder_config if prompt_encoder_config is not None else {}
										__magic_name__      =  mask_decoder_config if mask_decoder_config is not None else {}

										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  vision_config.to_dict()
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  prompt_encoder_config.to_dict()
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  mask_decoder_config.to_dict()

										__magic_name__      =  SamVisionConfig(**__lowerCamelCase						)
										__magic_name__      =  SamPromptEncoderConfig(**__lowerCamelCase						)
										__magic_name__      =  SamMaskDecoderConfig(**__lowerCamelCase						)
										__magic_name__      =  initializer_range







				def      _snake_case       (				self				:    Optional[int]						)					->       str:
										__magic_name__      =  copy.deepcopy(self.__dict__						)
										__magic_name__      =  self.vision_config.to_dict()
										__magic_name__      =  self.prompt_encoder_config.to_dict()
										__magic_name__      =  self.mask_decoder_config.to_dict()
										__magic_name__      =  self.__class__.model_type
										return output



