
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_			(				snake_case				: Optional[Any]=None			,   snake_case				: Optional[Any]=None			)   ->     Optional[Any]:



						'''simple docstring'''

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






@dataclass
class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






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





def 						A_			(				snake_case				: List[Any]			)   ->     str:



						'''simple docstring'''

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




def 						A_			(				snake_case				: List[str]			)   ->     int:



						'''simple docstring'''

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






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      args
													__UpperCamelCase  	=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

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



							def      A__		(	self      )-> int:





													'''simple docstring'''






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

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

																			__UpperCamelCase  	=      (
																			    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  	=      np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,						dtype=SCREAMING_SNAKE_CASE_  ,						)
																									else:
																															__UpperCamelCase  	=      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))  	=      (
																									    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																									)

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

																			title_str += F" {label_model_name} vs."

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

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

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





def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      HfArgumentParser(snake_case			)
						__UpperCamelCase  	=      parser.parse_args_into_dataclasses()[0]
						__UpperCamelCase  	=      Plot(args=snake_case			)
						plot.plot()


if __name__ == "__main__":
				main()










'''simple docstring'''







import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union





class 	lowerCamelCase_				(			enum.Enum				):




					'''simple docstring'''






					lowercase__       =					"""timm"""
					lowercase__       =					"""transformers"""






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if stage_names is None:
				raise ValueError("Stage_names must be set for transformers backbones"    )

		if out_features is not None:
				if not isinstance(lowercase_		,				(list,)    ):
						raise ValueError(f'''out_features must be a list {type(lowercase_    )}'''    )
				if any(feat not in stage_names for feat in out_features    ):
						raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''    )

		if out_indices is not None:
				if not isinstance(lowercase_		,				(list, tuple)    ):
						raise ValueError(f'''out_indices must be a list or tuple, got {type(lowercase_    )}'''    )
				if any(idx >= len(lowercase_    ) for idx in out_indices    ):
						raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"    )

		if out_features is not None and out_indices is not None:
				if len(lowercase_    ) != len(lowercase_    ):
						raise ValueError("out_features and out_indices should have the same length if both are set"    )
				if out_features != [stage_names[idx] for idx in out_indices]:
						raise ValueError("out_features and out_indices should correspond to the same stages if both are set"    )






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				):



		'''simple docstring'''







		if out_indices is None and out_features is None:
				_snake_case      			=			[len(lowercase_    ) - 1]
				_snake_case      			=			[stage_names[-1]]
		elif out_indices is None and out_features is not None:
				_snake_case      			=			[stage_names.index(lowercase_    ) for layer in out_features]
		elif out_features is None and out_indices is not None:
				_snake_case      			=			[stage_names[idx] for idx in out_indices]
		return out_features, out_indices






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				):



		'''simple docstring'''







		verify_out_features_out_indices(out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		_snake_case			,       _snake_case      			=			_align_output_features_output_indices(
		    out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		# Verify that the aligned out_features and out_indices are valid
		verify_out_features_out_indices(out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		return output_features, output_indices





class 	lowerCamelCase_				:




					'''simple docstring'''






					lowercase__       =					None






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							if getattr(self,    "_backbone",    __UpperCamelCase					) is None:
									raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

							# These will diagree with the defaults for the transformers models e.g. for resnet50
							# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
							# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
							_snake_case      			=			[stage["module"] for stage in self._backbone.feature_info.info]
							_snake_case      			=			[stage["num_chs"] for stage in self._backbone.feature_info.info]
							_snake_case      			=			self._backbone.feature_info.out_indices
							_snake_case      			=			self._backbone.feature_info.module_name()

							# We verify the out indices and out features are valid
							verify_out_features_out_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)
							_snake_case			,       _snake_case      			=			out_features, out_indices






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			getattr(__UpperCamelCase,    "stage_names"					)
							_snake_case      			=			getattr(__UpperCamelCase,    "out_features",    __UpperCamelCase					)
							_snake_case      			=			getattr(__UpperCamelCase,    "out_indices",    __UpperCamelCase					)

							_snake_case      			=			stage_names
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=__UpperCamelCase					)
							# Number of channels for each stage. This is set in the transformer backbone model init
							_snake_case      			=			None






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			config

							_snake_case      			=			getattr(__UpperCamelCase,    "use_timm_backbone",    __UpperCamelCase					)
							_snake_case      			=			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

							if self.backbone_type == BackboneType.TIMM:
									self._init_timm_backbone(__UpperCamelCase					)
							elif self.backbone_type == BackboneType.TRANSFORMERS:
									self._init_transformers_backbone(__UpperCamelCase					)
							else:
									raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_features






					@out_features.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_indices






					@out_indices.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# the current backbones will output the number of channels for each stage
							# even if that stage is not in the out_features list.
							return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return [self.out_feature_channels[name] for name in self.out_features]






					def 							__SCREAMING_SNAKE_CASE      (       self,    *__UpperCamelCase,    **__UpperCamelCase					):
							_snake_case      			=			dict(inspect.signature(self.forward					).parameters					)
							_snake_case      			=			{k: v for k, v in kwargs.items() if k in signature}
							return self(*__UpperCamelCase,    **__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None,    __UpperCamelCase = None,    __UpperCamelCase = None,    ):
							raise NotImplementedError("This method should be implemented by the derived class."					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			super().to_dict()
							_snake_case      			=			output.pop("_out_features"					)
							_snake_case      			=			output.pop("_out_indices"					)
							return output





class 	lowerCamelCase_				:




					'''simple docstring'''






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_features






					@out_features.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_indices






					@out_indices.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			super().to_dict()
							_snake_case      			=			output.pop("_out_features"					)
							_snake_case      			=			output.pop("_out_indices"					)
							return output





