





'''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'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_  :							Any            =	{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							str            =	[
					    "DPRContextEncoderTokenizerFast",
					    "DPRQuestionEncoderTokenizerFast",
					    "DPRReaderTokenizerFast",
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Optional[int]            =	[
					    "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPRContextEncoder",
					    "DPRPretrainedContextEncoder",
					    "DPRPreTrainedModel",
					    "DPRPretrainedQuestionEncoder",
					    "DPRPretrainedReader",
					    "DPRQuestionEncoder",
					    "DPRReader",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Dict            =	[
					    "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFDPRContextEncoder",
					    "TFDPRPretrainedContextEncoder",
					    "TFDPRPretrainedQuestionEncoder",
					    "TFDPRPretrainedReader",
					    "TFDPRQuestionEncoder",
					    "TFDPRReader",
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

					snake_case_  :							List[str]            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





