"""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   a_						( lowerCamelCase=None ,      lowerCamelCase=None	):
				return field(default_factory=lambda: default ,      metadata=lowerCamelCase	)
@dataclass
class     snake_case	:




					"""simple docstring"""

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






def   a_						( lowerCamelCase	):
				try:
								int(lowerCamelCase	)
								return True
				except ValueError:
								return False



def   a_						( lowerCamelCase	):
				try:
								float(lowerCamelCase	)
								return True
				except ValueError:
								return False
class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     List[str]   ,lowerCamelCase__       :     str      ):
									UpperCAmelCase__					=							args
									UpperCAmelCase__					=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

									with open(self.args.csv_file   ,newline=''      ) as csv_file:
													UpperCAmelCase__					=							csv.DictReader(lowerCamelCase__      )
													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 		__lowerCAmelCase						(    self       :     Any      ):
									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=lowerCamelCase__   ,)
																	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(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.'''

									UpperCAmelCase__					=							title_str[:-4]
									UpperCAmelCase__					=							'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   a_						( ):
				UpperCAmelCase__					=							HfArgumentParser(lowerCamelCase	)
				UpperCAmelCase__					=							parser.parse_args_into_dataclasses()[0]
				UpperCAmelCase__					=							Plot(args=lowerCamelCase	)
				plot.plot()


if __name__ == "__main__":
						main()



"""simple docstring"""






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


lowerCAmelCase__  :  Optional[Any]											=     logging.get_logger(__name__)
class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     Optional[Any]   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Optional[Any]=21   ,lowerCamelCase__       :     str=[]   ,lowerCamelCase__       :     Any=None      ):
									UpperCAmelCase__					=							model
									UpperCAmelCase__					=							trace_batch_nums
									UpperCAmelCase__					=							abort_after_batch_num

									# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
									UpperCAmelCase__					=							collections.deque([]   ,lowerCamelCase__      )
									UpperCAmelCase__					=							[]
									UpperCAmelCase__					=							0
									UpperCAmelCase__					=							0
									UpperCAmelCase__					=							False
									UpperCAmelCase__					=							'                 '

									self.analyse_model()

									self.register_forward_hook()







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]   ,lowerCamelCase__       :     Tuple=None      ):
									if frame is not None:
													self.expand_frame(lowerCamelCase__      )
									self.frames.append('\n'.join(self.frame      )      )
									UpperCAmelCase__					=							[]  # start a new frame







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     int      ):
									self.frame.append(lowerCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									print('\n'.join(self.frames      )      )
									UpperCAmelCase__					=							[]







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							[]







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''      )
									print(f'''Last {len(self.frames      )} forward frames:'''      )
									print(f'''{"abs min":8} {"abs max":8} metadata'''      )
									print('\n'.join(self.frames      )      )
									print('\n\n'      )
									UpperCAmelCase__					=							[]







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									# extract the fully qualified module names, to be able to report at run time. e.g.:
									# encoder.block.2.layer.0.SelfAttention.o
									#
									# for shared weights only the first shared module name will be registered
									UpperCAmelCase__					=							{m: name for name, m in self.model.named_modules()}
									# self.longest_module_name = max(len(v) for v in self.module_names.values())







					def 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     int   ,lowerCamelCase__       :     List[Any]      ):
									if torch.is_tensor(lowerCamelCase__      ):
													self.expand_frame(get_abs_min_max(lowerCamelCase__   ,lowerCamelCase__      )      )
													if detect_overflow(lowerCamelCase__   ,lowerCamelCase__      ):
																	UpperCAmelCase__					=							True
									elif var is None:
													self.expand_frame(f'''{"None":>17} {ctx}'''      )
									else:
													self.expand_frame(f'''{"not a tensor":>17} {ctx}'''      )







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''      )
									self.expand_frame(f'''{"abs min":8} {"abs max":8} metadata'''      )







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''      )







					def 		__lowerCAmelCase						(    self       :     List[str]   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     Optional[Any]      ):
									self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''      )

									# params
									for name, p in module.named_parameters(recurse=lowerCamelCase__      ):
													self.analyse_variable(lowerCamelCase__   ,lowerCamelCase__      )

									# inputs
									if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
													for i, x in enumerate(lowerCamelCase__      ):
																	self.analyse_variable(lowerCamelCase__   ,f'''input[{i}]'''      )
									else:
													self.analyse_variable(lowerCamelCase__   ,'input'      )

									# outputs
									if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
													for i, x in enumerate(lowerCamelCase__      ):
																	# possibly a tuple of tuples
																	if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
																					for j, y in enumerate(lowerCamelCase__      ):
																									self.analyse_variable(lowerCamelCase__   ,f'''output[{i}][{j}]'''      )
																	else:
																					self.analyse_variable(lowerCamelCase__   ,f'''output[{i}]'''      )
									else:
													self.analyse_variable(lowerCamelCase__   ,'output'      )

									self.save_frame()







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									self.model.apply(self._register_forward_hook      )







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     str      ):
									module.register_forward_hook(self.forward_hook      )







					def 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     Union[str, Any]      ):
									# - input is a tuple of packed inputs (could be non-Tensors)
									# - output could be a Tensor or a tuple of Tensors and non-Tensors

									UpperCAmelCase__					=							False

									UpperCAmelCase__					=							True if self.batch_number in self.trace_batch_nums else False
									if trace_mode:
													self.reset_saved_frames()

									if self.total_calls == 0:
													self.batch_start_frame()
									self.total_calls += 1

									# count batch numbers - the very first forward hook of the batch will be called when the
									# batch completes - i.e. it gets called very last - we know this batch has finished
									if module == self.model:
													self.batch_number += 1
													UpperCAmelCase__					=							True

									self.create_frame(lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__      )

									# if last_frame_of_batch:
									#     self.batch_end_frame()

									if trace_mode:
													self.trace_frames()

									if last_frame_of_batch:
													self.batch_start_frame()

									if self.detected_overflow and not trace_mode:
													self.dump_saved_frames()

													# now we can abort, as it's pointless to continue running
													raise ValueError(
													    'DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '
													    'Please scroll up above this traceback to see the activation values prior to this event.'      )

									# abort after certain batch if requested to do so
									if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
													raise ValueError(
													    f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
													    f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''      )






def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							var.abs()
				return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							False
				if torch.isnan(lowerCamelCase	).any().item():
								UpperCAmelCase__					=							True
								print(f'''{ctx} has nans'''	)
				if torch.isinf(lowerCamelCase	).any().item():
								UpperCAmelCase__					=							True
								print(f'''{ctx} has infs'''	)

				# if needed to monitor large elements can enable the following
				if 0:  # and detected:
								UpperCAmelCase__					=							var[torch.ge(var.abs() ,      1_0_0	)]
								if naaa.numel() > 0:
												print(f'''{ctx}:  n100={naaa.numel()}'''	)
								UpperCAmelCase__					=							var[torch.ge(var.abs() ,      1_0_0_0	)]
								if naaaa.numel() > 0:
												print(f'''{ctx}: n1000={naaaa.numel()}'''	)
								UpperCAmelCase__					=							var[torch.ge(var.abs() ,      1_0_0_0_0	)]
								if naaaaa.numel() > 0:
												print(f'''{ctx}: n10000={naaaaa.numel()}'''	)

				if 0:
								print(f'''min={var.min():9.2e} max={var.max():9.2e}'''	)

				if 0:
								print(f'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''	)

				return detected







class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"underflow_overflow"
					snake_case__       =			"tpu_metrics_debug"



