





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


_lowerCamelCase							=     get_logger(__name__)






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






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							int   ,				UpperCamelCase__:							Union[str, Any]   ,				UpperCamelCase__:							Optional[Any]   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							Union[str, Any]=None   ,				UpperCamelCase__:							List[Any]=None   ,				**UpperCamelCase__:							str   ,				):
		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:
				SCREAMING_SNAKE_CASE__								= """cpu"""
		elif map_location == "on_device":
				SCREAMING_SNAKE_CASE__								= PartialState().device
		# Model states
		for i, model in enumerate(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
				SCREAMING_SNAKE_CASE__								= os.path.join(UpperCamelCase__   ,				UpperCamelCase__      )
				models[i].load_state_dict(torch.load(UpperCamelCase__   ,				map_location=UpperCamelCase__      )   ,				**UpperCamelCase__      )
		logger.info("""All model weights loaded successfully"""      )

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

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

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

		# Random states
		try:
				SCREAMING_SNAKE_CASE__								= torch.load(os.path.join(UpperCamelCase__   ,				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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Dict   ,				UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							int = 0      ):
		SCREAMING_SNAKE_CASE__								= Path(UpperCamelCase__      ) / f'''custom_checkpoint_{index}.pkl'''
		logger.info(f'''Saving the state of {get_pretty_name(UpperCamelCase__      )} to {save_location}'''      )
		torch.save(obj.state_dict()   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Dict   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							int = 0      ):
		SCREAMING_SNAKE_CASE__								= f'''{path}/custom_checkpoint_{index}.pkl'''
		logger.info(f'''Loading the state of {get_pretty_name(UpperCamelCase__      )} from {load_location}'''      )
		obj.load_state_dict(torch.load(UpperCamelCase__   ,				map_location="""cpu"""      )      )






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase							=     500000

_lowerCamelCase			,    _lowerCamelCase							=     os.path.split(__file__)
_lowerCamelCase							=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.select(range(0   ,				len(UpperCamelCase__      )   ,				2      )      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.sort("""numbers"""      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.shuffle()






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.train_test_split(0.1      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset   ,				UpperCamelCase__:							Tuple=10      ):
		for shard_id in range(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= dataset.shard(UpperCamelCase__   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= {"""num examples""": SPEED_TEST_N_EXAMPLES}
		SCREAMING_SNAKE_CASE__								= (select, sort, shuffle, train_test_split, shard)
		with tempfile.TemporaryDirectory() as tmp_dir:
				print("""generating dataset"""      )
				SCREAMING_SNAKE_CASE__								= datasets.Features({"""text""": datasets.Value("""string"""      ), """numbers""": datasets.Value("""float32"""      )}      )
				SCREAMING_SNAKE_CASE__								= generate_example_dataset(
				    os.path.join(UpperCamelCase__   ,				"""dataset.arrow"""      )   ,				UpperCamelCase__   ,				num_examples=UpperCamelCase__      )
				print("""Functions"""      )
				for func in functions:
						print(func.__name__      )
						SCREAMING_SNAKE_CASE__								= func(UpperCamelCase__      )

		with open(UpperCamelCase__   ,				"""wb"""      ) as f:
				f.write(json.dumps(UpperCamelCase__      ).encode("""utf-8"""      )      )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_indices_mapping()