



import copy

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


lowerCamelCase__     =	logging.get_logger(__name__)

lowerCamelCase__     =	{
    """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 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





							def __init__(				self						:						List[Any] ,			__lowercase						:						Optional[Any]=256 ,			__lowercase						:						Optional[int]=1024 ,			__lowercase						:						Any=16 ,			__lowercase						:						Optional[Any]=16 ,			__lowercase						:						Optional[Any]=4 ,			__lowercase						:						Optional[int]="gelu" ,			__lowercase						:						List[Any]=1E-6 ,			**__lowercase						:						str ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)
											__a					       =						hidden_size
											__a					       =						image_size
											__a					       =						patch_size
											__a					       =						image_size // patch_size
											__a					       =						mask_input_channels
											__a					       =						num_point_embeddings
											__a					       =						hidden_act
											__a					       =						layer_norm_eps






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





							def __init__(				self						:						int ,			__lowercase						:						Tuple=256 ,			__lowercase						:						List[Any]="relu" ,			__lowercase						:						Any=2048 ,			__lowercase						:						List[Any]=2 ,			__lowercase						:						List[str]=8 ,			__lowercase						:						Any=2 ,			__lowercase						:						List[Any]=3 ,			__lowercase						:						str=3 ,			__lowercase						:						str=256 ,			__lowercase						:						Optional[Any]=1E-6 ,			**__lowercase						:						Tuple ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)
											__a					       =						hidden_size
											__a					       =						hidden_act
											__a					       =						mlp_dim
											__a					       =						num_hidden_layers
											__a					       =						num_attention_heads
											__a					       =						attention_downsample_rate
											__a					       =						num_multimask_outputs
											__a					       =						iou_head_depth
											__a					       =						iou_head_hidden_dim
											__a					       =						layer_norm_eps






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





							def __init__(				self						:						List[Any] ,			__lowercase						:						List[Any]=768 ,			__lowercase						:						List[Any]=256 ,			__lowercase						:						Any=12 ,			__lowercase						:						Tuple=12 ,			__lowercase						:						Union[str, Any]=3 ,			__lowercase						:						Tuple=1024 ,			__lowercase						:						Any=16 ,			__lowercase						:						Dict="gelu" ,			__lowercase						:						List[Any]=1E-06 ,			__lowercase						:						List[str]=0.0 ,			__lowercase						:						Optional[Any]=1E-10 ,			__lowercase						:						Optional[Any]=True ,			__lowercase						:						str=4.0 ,			__lowercase						:						List[Any]=True ,			__lowercase						:						Dict=True ,			__lowercase						:						Optional[Any]=14 ,			__lowercase						:						Optional[int]=[2, 5, 8, 11] ,			__lowercase						:						List[str]=128 ,			__lowercase						:						Optional[int]=None ,			**__lowercase						:						List[Any] ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)

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






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):
							__lowerCamelCase		:		Dict      ='sam'
							__lowerCamelCase		:		Any      =True





							def __init__(				self						:						List[str] ,			__lowercase						:						List[str]=None ,			__lowercase						:						Optional[int]=None ,			__lowercase						:						Dict=None ,			__lowercase						:						Optional[int]=0.02 ,			**__lowercase						:						List[str] ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)
											__a					       =						vision_config if vision_config is not None else {}
											__a					       =						prompt_encoder_config if prompt_encoder_config is not None else {}
											__a					       =						mask_decoder_config if mask_decoder_config is not None else {}

											if isinstance(__lowercase ,			__lowercase		):
															__a					       =						vision_config.to_dict()
											if isinstance(__lowercase ,			__lowercase		):
															__a					       =						prompt_encoder_config.to_dict()
											if isinstance(__lowercase ,			__lowercase		):
															__a					       =						mask_decoder_config.to_dict()

											__a					       =						SamVisionConfig(**__lowercase		)
											__a					       =						SamPromptEncoderConfig(**__lowercase		)
											__a					       =						SamMaskDecoderConfig(**__lowercase		)
											__a					       =						initializer_range







							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											__a					       =						copy.deepcopy(self.__dict__		)
											__a					       =						self.vision_config.to_dict()
											__a					       =						self.prompt_encoder_config.to_dict()
											__a					       =						self.mask_decoder_config.to_dict()
											__a					       =						self.__class__.model_type
											return output





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    :     Dict=None			,       _SCREAMING_SNAKE_CASE    :     List[str]=None      ):
				"""simple docstring"""







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






@dataclass
class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		str      =field(
							    metadata={'help': 'The csv file to plot.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Disable logarithmic scale when plotting'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={
							        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
							    }     , )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}     , )
							__lowerCamelCase		:		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    :     Any      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







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






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						str		):

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

											with open(self.args.csv_file ,			newline=""""""		) as csv_file:
															__a					       =						csv.DictReader(__lowercase		)
															for row in reader:
																			__a					       =						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
																							__a					       =						int(row["""result"""]		)
																			elif can_convert_to_float(row["""result"""]		):
																							# value is not None
																							__a					       =						float(row["""result"""]		)







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a       ,   __a					       =						plt.subplots()
											__a					       =						"""Time usage""" if self.args.is_time else """Memory usage"""
											__a					       =						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()		):
															__a					       =						sorted(set(self.result_dict[model_name]["""bsz"""]		)		)
															__a					       =						sorted(set(self.result_dict[model_name]["""seq_len"""]		)		)
															__a					       =						self.result_dict[model_name]["""result"""]

															((__a)       ,   (__a))					       =						(
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															__a					       =						(
															    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:
																							__a					       =						np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,			dtype=__lowercase ,			)
																			else:
																							__a					       =						np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,			dtype=np.floataa ,			)

																			((__a)       ,   (__a))					       =						(
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			__a					       =						np.asarray(__lowercase ,			__lowercase		)[: len(__lowercase		)]
																			plt.scatter(
																			    __lowercase ,			__lowercase ,			label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
																			plt.plot(__lowercase ,			__lowercase ,			"""--"""		)

															title_str += F" {label_model_name} vs."

											__a					       =						title_str[:-4]
											__a					       =						"""Time in s""" if self.args.is_time else """Memory in MB"""

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

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



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						HfArgumentParser(_SCREAMING_SNAKE_CASE      )
				__a					       =						parser.parse_args_into_dataclasses()[0]
				__a					       =						Plot(args=_SCREAMING_SNAKE_CASE      )
				plot.plot()


if __name__ == "__main__":
						main()
