





"""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


_lowerCamelCase       =	get_logger(__name__)




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							List[dict] ,      lowercase_   :							list ,      lowercase_   :							list ,      lowercase_   :							int ,      lowercase_   :							GradScaler = None ,      ):

 '''simple docstring'''






 for i, state in enumerate(lowercase_		):
  __SCREAMING_SNAKE_CASE :			Dict												=       F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
  __SCREAMING_SNAKE_CASE :			Any												=       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_		):
  __SCREAMING_SNAKE_CASE :			List[Any]												=       opt.state_dict()
  __SCREAMING_SNAKE_CASE :			List[Any]												=       F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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_		):
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       scheduler.state_dict()
  __SCREAMING_SNAKE_CASE :			List[Any]												=       F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
  __SCREAMING_SNAKE_CASE :			Dict												=       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:
  __SCREAMING_SNAKE_CASE :			List[str]												=       scaler.state_dict()
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       os.path.join(lowercase_ ,      lowercase_		)
  torch.save(lowercase_ ,      lowercase_		)
  logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''		)
 # Random number generator states
 __SCREAMING_SNAKE_CASE :			List[str]												=       {}
 __SCREAMING_SNAKE_CASE :			Any												=       F'''{RNG_STATE_NAME}_{process_index}.pkl'''
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       random.getstate()
 __SCREAMING_SNAKE_CASE :			Any												=       np.random.get_state()
 __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.get_rng_state()
 if is_xpu_available():
  __SCREAMING_SNAKE_CASE :			Dict												=       torch.xpu.get_rng_state_all()
 else:
  __SCREAMING_SNAKE_CASE :			List[str]												=       torch.cuda.get_rng_state_all()
 if is_tpu_available():
  __SCREAMING_SNAKE_CASE :			int												=       xm.get_rng_state()
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       os.path.join(lowercase_ ,      lowercase_		)
 torch.save(lowercase_ ,      lowercase_		)
 logger.info(F'''Random states saved in {output_states_file}'''		)
 return output_dir




def 		lowerCAmelCase_	(  lowercase_   :							Tuple ,      lowercase_   :							Union[str, Any] ,      lowercase_   :							Tuple ,      lowercase_   :							int ,      lowercase_   :							Tuple ,      lowercase_   :							List[Any]=None ,      lowercase_   :							List[Any]=None ,      **lowercase_   :							List[str] ,      ):

 '''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:
  __SCREAMING_SNAKE_CASE :			List[Any]												=       '''cpu'''
 elif map_location == "on_device":
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       PartialState().device
 # Model states
 for i, model in enumerate(lowercase_		):
  __SCREAMING_SNAKE_CASE :			List[Any]												=       F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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_		):
  __SCREAMING_SNAKE_CASE :			Dict												=       F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       os.path.join(lowercase_ ,      lowercase_		)
  __SCREAMING_SNAKE_CASE :			str												=       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_		):
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
  __SCREAMING_SNAKE_CASE :			int												=       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:
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       os.path.join(lowercase_ ,      lowercase_		)
  scaler.load_state_dict(torch.load(lowercase_		)		)
  logger.info('''GradScaler state loaded successfully'''		)

 # Random states
 try:
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Tuple ,      lowercase_   :							int = 0		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[Any]												=       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 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							List[Any] ,      lowercase_   :							int = 0		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[Any]												=       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"""





from typing import TYPE_CHECKING

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


_lowerCamelCase       =	{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	['''MobileNetV1FeatureExtractor''']
    _lowerCamelCase       =	['''MobileNetV1ImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	[
        '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MobileNetV1ForImageClassification''',
        '''MobileNetV1Model''',
        '''MobileNetV1PreTrainedModel''',
        '''load_tf_weights_in_mobilenet_v1''',
    ]


if TYPE_CHECKING:
    from .configuration_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileNetVaConfig,
        MobileNetVaOnnxConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
        from .image_processing_mobilenet_va import MobileNetVaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilenet_va import (
            MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )


else:
    import sys

    _lowerCamelCase       =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

