



"""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 	snake_case__  (						__lowerCamelCase							:       Optional[int]=None				,						__lowerCamelCase							:       Union[str, Any]=None      ):
			"""simple docstring"""
			return field(default_factory=lambda: default				,						metadata=__lowerCamelCase      )

@dataclass
class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





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






def 	snake_case__  (						__lowerCamelCase							:       Any      ):
			"""simple docstring"""
			try:
						int(__lowerCamelCase      )
						return True
			except ValueError:
						return False






def 	snake_case__  (						__lowerCamelCase							:       List[str]      ):
			"""simple docstring"""
			try:
						float(__lowerCamelCase      )
						return True
			except ValueError:
						return False

class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    List[Any]	)->   Union[str, Any]:
								lowerCamelCase__						:						List[str]						      =args
								lowerCamelCase__						:						str						      =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

								with open(self.args.csv_file,    newline=''''''	) as csv_file:
											lowerCamelCase__						:						Dict						      =csv.DictReader(lowerCamelCase	)
											for row in reader:
														lowerCamelCase__						:						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
																	lowerCamelCase__						:						Union[str, Any]						      =int(row['''result''']	)
														elif can_convert_to_float(row['''result''']	):
																	# value is not None
																	lowerCamelCase__						:						Tuple						      =float(row['''result''']	)




					def 						snake_case			(			self				:    Optional[int]	)->   Union[str, Any]:
								lowerCamelCase__      ,  lowerCamelCase__						:						Any						      =plt.subplots()
								lowerCamelCase__						:						Any						      ='''Time usage''' if self.args.is_time else '''Memory usage'''
								lowerCamelCase__						:						Tuple						      =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()	):
											lowerCamelCase__						:						Optional[Any]						      =sorted(set(self.result_dict[model_name]['''bsz''']	)	)
											lowerCamelCase__						:						List[str]						      =sorted(set(self.result_dict[model_name]['''seq_len''']	)	)
											lowerCamelCase__						:						List[str]						      =self.result_dict[model_name]['''result''']

											((lowerCamelCase__)      ,  (lowerCamelCase__))						:						str						      =(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											lowerCamelCase__						:						Optional[int]						      =(
											    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:
																	lowerCamelCase__						:						List[str]						      =np.asarray(
																	    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],    dtype=lowerCamelCase,    )
														else:
																	lowerCamelCase__						:						int						      =np.asarray(
																	    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results],    dtype=np.floataa,    )

														((lowerCamelCase__)      ,  (lowerCamelCase__))						:						Optional[Any]						      =(
														    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
														)

														lowerCamelCase__						:						Any						      =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.'''

								lowerCamelCase__						:						Dict						      =title_str[:-4]
								lowerCamelCase__						:						List[Any]						      ='''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 	snake_case__  (						):
			"""simple docstring"""
			lowerCamelCase__						:						int						      =HfArgumentParser(__lowerCamelCase      )
			lowerCamelCase__						:						int						      =parser.parse_args_into_dataclasses()[0]
			lowerCamelCase__						:						Optional[int]						      =Plot(args=__lowerCamelCase      )
			plot.plot()


if __name__ == "__main__":
		main()









"""simple docstring"""






import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
		import torch

		from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
		import pyanvml.pyanvml as nvml


_lowercase					:					Optional[Any] 		=		logging.get_logger(__name__)

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				42
					_a       =				42
					_a       =				"PyTorch"



					@property
					def 						snake_case			(			self				:    Dict	)->   List[str]:
								return torch.__version__



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   float:
								lowerCamelCase__						:						List[Any]						      =self._prepare_inference_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_speed(_inference	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   [Memory, Optional[MemorySummary]]:
								lowerCamelCase__						:						List[Any]						      =self._prepare_inference_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_memory(_inference	)



					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   float:
								lowerCamelCase__						:						int						      =self._prepare_train_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_speed(_train	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   [Memory, Optional[MemorySummary]]:
								lowerCamelCase__						:						str						      =self._prepare_train_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_memory(_train	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   Callable[[], None]:
								lowerCamelCase__						:						List[str]						      =self.config_dict[model_name]

								if self.args.torchscript:
											lowerCamelCase__						:						int						      =True

								lowerCamelCase__						:						Optional[Any]						      =(
								    hasattr(lowerCamelCase,    '''architectures'''	)
								    and isinstance(config.architectures,    lowerCamelCase	)
								    and len(config.architectures	) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														lowerCamelCase__						:						int						      =config.architectures[0]
														lowerCamelCase__						:						Dict						      =__import__('''transformers''',    fromlist=[model_class]	)
														lowerCamelCase__						:						str						      =getattr(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Tuple						      =model_cls(lowerCamelCase	)
											except ImportError:
														raise ImportError(
														    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
														    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
								else:
											lowerCamelCase__						:						List[str]						      =MODEL_MAPPING[config.__class__](lowerCamelCase	)

								model.eval()
								model.to(self.args.device	)

								# encoder-decoder has vocab size saved differently
								lowerCamelCase__						:						Optional[Any]						      =config.vocab_size if hasattr(lowerCamelCase,    '''vocab_size'''	) else config.encoder.vocab_size
								lowerCamelCase__						:						int						      =torch.randint(lowerCamelCase,    (batch_size, sequence_length),    dtype=torch.long,    device=self.args.device	)

								if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''	)
											if not self.args.is_gpu:
														raise ValueError('''Mixed precision is possible only for GPU.'''	)
											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

								if self.args.torchscript:
											with torch.no_grad():
														lowerCamelCase__						:						Optional[Any]						      =torch.jit.trace(lowerCamelCase,    lowerCamelCase	)
								else:
											lowerCamelCase__						:						Tuple						      =model

								def encoder_decoder_forward():
											with torch.no_grad():
														lowerCamelCase__						:						Optional[Any]						      =inference_model(lowerCamelCase,    decoder_input_ids=lowerCamelCase	)
											return outputs

								def encoder_forward():
											with torch.no_grad():
														lowerCamelCase__						:						Any						      =inference_model(lowerCamelCase	)
											return outputs

								lowerCamelCase__						:						Optional[int]						      =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
								return _forward



					def 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   Callable[[], None]:
								lowerCamelCase__						:						Any						      =self.config_dict[model_name]

								lowerCamelCase__						:						Tuple						      =(
								    hasattr(lowerCamelCase,    '''architectures'''	)
								    and isinstance(config.architectures,    lowerCamelCase	)
								    and len(config.architectures	) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														lowerCamelCase__						:						List[Any]						      =config.architectures[0]
														lowerCamelCase__						:						int						      =__import__('''transformers''',    fromlist=[model_class]	)
														lowerCamelCase__						:						int						      =getattr(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Dict						      =model_cls(lowerCamelCase	)
											except ImportError:
														raise ImportError(
														    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
														    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
								else:
											lowerCamelCase__						:						List[str]						      =MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase	)

								if self.args.torchscript:
											raise NotImplementedError('''Training for torchscript is currently not implemented'''	)
								else:
											lowerCamelCase__						:						str						      =model

								model.train()
								model.to(self.args.device	)

								# encoder-decoder has vocab size saved differently
								lowerCamelCase__						:						Dict						      =config.vocab_size if hasattr(lowerCamelCase,    '''vocab_size'''	) else config.encoder.vocab_size
								lowerCamelCase__						:						int						      =torch.randint(lowerCamelCase,    (batch_size, sequence_length),    dtype=torch.long,    device=self.args.device	)

								if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''	)
											if not self.args.is_gpu:
														raise ValueError('''Mixed precision is possible only for GPU.'''	)

											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

								def compute_loss_and_backprob_encoder():
											lowerCamelCase__						:						Optional[int]						      =train_model(lowerCamelCase,    labels=lowerCamelCase	)[0]
											loss.backward()
											return loss

								def compute_loss_and_backprob_encoder_decoder():
											lowerCamelCase__						:						Dict						      =train_model(lowerCamelCase,    decoder_input_ids=lowerCamelCase,    labels=lowerCamelCase	)[0]
											loss.backward()
											return loss

								lowerCamelCase__						:						Optional[Any]						      =(
								    compute_loss_and_backprob_encoder_decoder
								    if config.is_encoder_decoder
								    else compute_loss_and_backprob_encoder
								)
								return _train



					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Optional[int]	)->   float:
								try:
											if self.args.is_tpu or self.args.torchscript:
														# run additional 10 times to stabilize compilation for tpu and torchscript
														logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''	)
														timeit.repeat(
														    lowerCamelCase,    repeat=1,    number=5,    )

											# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
											lowerCamelCase__						:						List[str]						      =timeit.repeat(
											    lowerCamelCase,    repeat=self.args.repeat,    number=10,    )

											if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
														import torch_xla.debug.metrics as met

														self.print_fn(met.metrics_report()	)

											return min(lowerCamelCase	) / 10.0
								except RuntimeError as e:
											self.print_fn(F'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A"




					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Callable[[], None]	)->   [Memory, MemorySummary]:
								try:
											if self.args.trace_memory_line_by_line:
														lowerCamelCase__						:						Optional[int]						      =start_memory_tracing('''transformers'''	)

											if self.args.is_tpu:
														# tpu
														raise NotImplementedError(
														    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
														    ''' `--no-memory` or `args.memory=False`'''	)
											elif self.args.is_gpu:
														if not is_pyanvml_available():
																	logger.warning(
																	    '''py3nvml not installed, we won\'t log GPU memory usage. '''
																	    '''Install py3nvml (pip install py3nvml) to log information about GPU.'''	)
																	lowerCamelCase__						:						str						      ='''N/A'''
														else:
																	logger.info(
																	    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
																	    ''' on the same GPU.'''	)
																	# init nvml
																	nvml.nvmlInit()
																	func()
																	lowerCamelCase__						:						int						      =nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
																	lowerCamelCase__						:						Optional[Any]						      =nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase	)
																	lowerCamelCase__						:						str						      =meminfo.used
																	lowerCamelCase__						:						List[str]						      =Memory(lowerCamelCase	)
																	# shutdown nvml
																	nvml.nvmlShutdown()
											else:
														# cpu
														lowerCamelCase__						:						List[Any]						      =measure_peak_memory_cpu(lowerCamelCase	)
														lowerCamelCase__						:						List[Any]						      =Memory(lowerCamelCase	) if isinstance(lowerCamelCase,    lowerCamelCase	) else memory_bytes

											if self.args.trace_memory_line_by_line:
														lowerCamelCase__						:						List[str]						      =stop_memory_tracing(lowerCamelCase	)
											else:
														lowerCamelCase__						:						Optional[int]						      =None

											return memory, summary
								except RuntimeError as e:
											self.print_fn(F'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A", None




