
'''simple docstring'''



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__A          =    get_logger(__name__)





def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__ = None		,		):
						# Model states
						for i, state in enumerate(lowercase__	):
												lowercase__							 =       f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
												lowercase__							 =       os.path.join(lowercase__		,		lowercase__	)
												save(lowercase__		,		lowercase__	)
												logger.info(f'''Model weights saved in {output_model_file}'''	)
						# Optimizer states
						for i, opt in enumerate(lowercase__	):
												lowercase__							 =       opt.state_dict()
												lowercase__							 =       f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
												lowercase__							 =       os.path.join(lowercase__		,		lowercase__	)
												save(lowercase__		,		lowercase__	)
												logger.info(f'''Optimizer state saved in {output_optimizer_file}'''	)
						# Scheduler states
						for i, scheduler in enumerate(lowercase__	):
												lowercase__							 =       scheduler.state_dict()
												lowercase__							 =       f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
												lowercase__							 =       os.path.join(lowercase__		,		lowercase__	)
												save(lowercase__		,		lowercase__	)
												logger.info(f'''Scheduler state saved in {output_scheduler_file}'''	)
						# GradScaler state
						if scaler is not None:
												lowercase__							 =       scaler.state_dict()
												lowercase__							 =       os.path.join(lowercase__		,		lowercase__	)
												torch.save(lowercase__		,		lowercase__	)
												logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''	)
						# Random number generator states
						lowercase__							 =       {}
						lowercase__							 =       f'''{RNG_STATE_NAME}_{process_index}.pkl'''
						lowercase__							 =       random.getstate()
						lowercase__							 =       np.random.get_state()
						lowercase__							 =       torch.get_rng_state()
						if is_xpu_available():
												lowercase__							 =       torch.xpu.get_rng_state_all()
						else:
												lowercase__							 =       torch.cuda.get_rng_state_all()
						if is_tpu_available():
												lowercase__							 =       xm.get_rng_state()
						lowercase__							 =       os.path.join(lowercase__		,		lowercase__	)
						torch.save(lowercase__		,		lowercase__	)
						logger.info(f'''Random states saved in {output_states_file}'''	)
						return output_dir





def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=None		,		lowercase__=None		,		**lowercase__		,		):
						if map_location not in [None, "cpu", "on_device"]:
												raise TypeError(
												    """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""	)
						if map_location is None:
												lowercase__							 =       """cpu"""
						elif map_location == "on_device":
												lowercase__							 =       PartialState().device
						# Model states
						for i, model in enumerate(lowercase__	):
												lowercase__							 =       f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
												lowercase__							 =       os.path.join(lowercase__		,		lowercase__	)
												models[i].load_state_dict(torch.load(lowercase__		,		map_location=lowercase__	)		,		**lowercase__	)
						logger.info("""All model weights loaded successfully"""	)

						# Optimizer states
						for i, opt in enumerate(lowercase__	):
												lowercase__							 =       f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
												lowercase__							 =       os.path.join(lowercase__		,		lowercase__	)
												lowercase__							 =       torch.load(lowercase__		,		map_location=lowercase__	)
												optimizers[i].load_state_dict(lowercase__	)
						logger.info("""All optimizer states loaded successfully"""	)

						# Scheduler states
						for i, scheduler in enumerate(lowercase__	):
												lowercase__							 =       f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
												lowercase__							 =       os.path.join(lowercase__		,		lowercase__	)
												scheduler.load_state_dict(torch.load(lowercase__	)	)
						logger.info("""All scheduler states loaded successfully"""	)

						# GradScaler state
						if scaler is not None:
												lowercase__							 =       os.path.join(lowercase__		,		lowercase__	)
												scaler.load_state_dict(torch.load(lowercase__	)	)
												logger.info("""GradScaler state loaded successfully"""	)

						# Random states
						try:
												lowercase__							 =       torch.load(os.path.join(lowercase__		,		f'''{RNG_STATE_NAME}_{process_index}.pkl'''	)	)
												random.setstate(states["""random_state"""]	)
												np.random.set_state(states["""numpy_random_seed"""]	)
												torch.set_rng_state(states["""torch_manual_seed"""]	)
												if is_xpu_available():
																		torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]	)
												else:
																		torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]	)
												if is_tpu_available():
																		xm.set_rng_state(states["""xm_seed"""]	)
												logger.info("""All random states loaded successfully"""	)
						except Exception:
												logger.info("""Could not load random states"""	)





def  _A		(							lowercase__		,		lowercase__		,		lowercase__ = 0	):
						lowercase__							 =       Path(lowercase__	) / f'''custom_checkpoint_{index}.pkl'''
						logger.info(f'''Saving the state of {get_pretty_name(lowercase__	)} to {save_location}'''	)
						torch.save(obj.state_dict()		,		lowercase__	)







def  _A		(							lowercase__		,		lowercase__		,		lowercase__ = 0	):
						lowercase__							 =       f'''{path}/custom_checkpoint_{index}.pkl'''
						logger.info(f'''Loading the state of {get_pretty_name(lowercase__	)} from {load_location}'''	)
						obj.load_state_dict(torch.load(lowercase__		,		map_location="""cpu"""	)	)








'''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		(							lowercase__=None		,		lowercase__=None	):
						return field(default_factory=lambda: default		,		metadata=lowercase__	)





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





def  _A		(							lowercase__	):
						try:
												int(lowercase__	)
												return True
						except ValueError:
												return False





def  _A		(							lowercase__	):
						try:
												float(lowercase__	)
												return True
						except ValueError:
												return False





class 			A							:



			def __init__(					self       ,     lowerCamelCase__       )			->		int:




									'''simple docstring'''



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

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



			def A__ (					self       )			->		Tuple:




									'''simple docstring'''



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

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

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

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

																					lowercase__							 =       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.'''

									lowercase__							 =       title_str[:-4]
									lowercase__							 =       """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		(							):
						lowercase__							 =       HfArgumentParser(lowercase__	)
						lowercase__							 =       parser.parse_args_into_dataclasses()[0]
						lowercase__							 =       Plot(args=lowercase__	)
						plot.plot()


if __name__ == "__main__":
				main()






