



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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     List[dict]			,       _SCREAMING_SNAKE_CASE    :     list			,       _SCREAMING_SNAKE_CASE    :     list			,       _SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     GradScaler = None			,       ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     List[Any]			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]=None			,       _SCREAMING_SNAKE_CASE    :     Union[str, Any]=None			,       **_SCREAMING_SNAKE_CASE    :     Optional[Any]			,       ):
				"""simple docstring"""







				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:
								__a					       =						"""cpu"""
				elif map_location == "on_device":
								__a					       =						PartialState().device
				# Model states
				for i, model in enumerate(_SCREAMING_SNAKE_CASE      ):
								__a					       =						f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
								__a					       =						os.path.join(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )
								models[i].load_state_dict(torch.load(_SCREAMING_SNAKE_CASE			,       map_location=_SCREAMING_SNAKE_CASE      )			,       **_SCREAMING_SNAKE_CASE      )
				logger.info("""All model weights loaded successfully"""      )

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

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

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

				# Random states
				try:
								__a					       =						torch.load(os.path.join(_SCREAMING_SNAKE_CASE			,       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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[Any]			,       _SCREAMING_SNAKE_CASE    :     Dict			,       _SCREAMING_SNAKE_CASE    :     int = 0      ):
				"""simple docstring"""







				__a					       =						Path(_SCREAMING_SNAKE_CASE      ) / f"custom_checkpoint_{index}.pkl"
				logger.info(f"Saving the state of {get_pretty_name(_SCREAMING_SNAKE_CASE      )} to {save_location}"      )
				torch.save(obj.state_dict()			,       _SCREAMING_SNAKE_CASE      )

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Dict			,       _SCREAMING_SNAKE_CASE    :     int = 0      ):
				"""simple docstring"""







				__a					       =						f"{path}/custom_checkpoint_{index}.pkl"
				logger.info(f"Loading the state of {get_pretty_name(_SCREAMING_SNAKE_CASE      )} from {load_location}"      )
				obj.load_state_dict(torch.load(_SCREAMING_SNAKE_CASE			,       map_location="""cpu"""      )      )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase__     =	{"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """HubertForCTC""",
						    """HubertForSequenceClassification""",
						    """HubertModel""",
						    """HubertPreTrainedModel""",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFHubertForCTC""",
						    """TFHubertModel""",
						    """TFHubertPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_hubert import (
												    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    HubertForCTC,
												    HubertForSequenceClassification,
												    HubertModel,
												    HubertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_hubert import (
												    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFHubertForCTC,
												    TFHubertModel,
												    TFHubertPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
