
"""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__   :						List[Any] 							=     get_logger(__name__)



def 				a__			(					lowerCAmelCase						:							str							,     lowerCAmelCase						:							List[dict]							,     lowerCAmelCase						:							list							,     lowerCAmelCase						:							list							,     lowerCAmelCase						:							int							,     lowerCAmelCase						:							GradScaler = None							,     ):






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



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

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

     # Scheduler states
     for i, scheduler in enumerate(lowerCAmelCase		):
          UpperCAmelCase__				:			Optional[int]							=    F"{SCHEDULER_NAME}.bin" if i == 0 else F"{SCHEDULER_NAME}_{i}.bin"
          UpperCAmelCase__				:			Any							=    os.path.join(lowerCAmelCase							,     lowerCAmelCase		)
          scheduler.load_state_dict(torch.load(lowerCAmelCase		)		)
     logger.info("All scheduler states loaded successfully"		)

     # GradScaler state
     if scaler is not None:
          UpperCAmelCase__				:			List[str]							=    os.path.join(lowerCAmelCase							,     lowerCAmelCase		)
          scaler.load_state_dict(torch.load(lowerCAmelCase		)		)
          logger.info("GradScaler state loaded successfully"		)

     # Random states
     try:
          UpperCAmelCase__				:			int							=    torch.load(os.path.join(lowerCAmelCase							,     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__			(					lowerCAmelCase						:							int							,     lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							int = 0		):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    Path(lowerCAmelCase		) / F"custom_checkpoint_{index}.pkl"
     logger.info(F"Saving the state of {get_pretty_name(lowerCAmelCase		)} to {save_location}"		)
     torch.save(obj.state_dict()							,     lowerCAmelCase		)



def 				a__			(					lowerCAmelCase						:							str							,     lowerCAmelCase						:							List[Any]							,     lowerCAmelCase						:							int = 0		):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    F"{path}/custom_checkpoint_{index}.pkl"
     logger.info(F"Loading the state of {get_pretty_name(lowerCAmelCase		)} from {load_location}"		)
     obj.load_state_dict(torch.load(lowerCAmelCase							,     map_location="cpu"		)		)





"""simple docstring"""




from typing import TYPE_CHECKING

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


A__   :						Optional[Any] 							=     {"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						List[Any] 							=     ["""ViltFeatureExtractor"""]
    A__   :						Any 							=     ["""ViltImageProcessor"""]
    A__   :						int 							=     ["""ViltProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						str 							=     [
        """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViltForImageAndTextRetrieval""",
        """ViltForImagesAndTextClassification""",
        """ViltForTokenClassification""",
        """ViltForMaskedLM""",
        """ViltForQuestionAnswering""",
        """ViltLayer""",
        """ViltModel""",
        """ViltPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_vilt import ViltFeatureExtractor
        from .image_processing_vilt import ViltImageProcessor
        from .processing_vilt import ViltProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vilt import (
            VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViltForImageAndTextRetrieval,
            ViltForImagesAndTextClassification,
            ViltForMaskedLM,
            ViltForQuestionAnswering,
            ViltForTokenClassification,
            ViltLayer,
            ViltModel,
            ViltPreTrainedModel,
        )


else:
    import sys

    A__   :						Any 							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure)



