





'''simple docstring'''






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]		,       __magic_name__  :   Dict		,       __magic_name__  :   Dict      )				->			Optional[int]:
 '''simple docstring'''






 snake_case__     :					List[Any]    =  AlbertConfig.from_json_file(__magic_name__      )
 print(f"Building PyTorch model from configuration: {config}"      )
 snake_case__     :					Optional[int]    =  AlbertForPreTraining(__magic_name__      )

 # Load weights from tf checkpoint
 load_tf_weights_in_albert(__magic_name__		,       __magic_name__		,       __magic_name__      )

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"      )
 torch.save(model.state_dict()		,       __magic_name__      )


if __name__ == "__main__":
    A_		:  List[Any]		 =						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--albert_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained ALBERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    A_		:  Optional[int]		 =						parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)











'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_pt_objects import *  # noqa F403
else:
 from .scheduling_consistency_models import CMStochasticIterativeScheduler
 from .scheduling_ddim import DDIMScheduler
 from .scheduling_ddim_inverse import DDIMInverseScheduler
 from .scheduling_ddim_parallel import DDIMParallelScheduler
 from .scheduling_ddpm import DDPMScheduler
 from .scheduling_ddpm_parallel import DDPMParallelScheduler
 from .scheduling_deis_multistep import DEISMultistepScheduler
 from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
 from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
 from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import EulerDiscreteScheduler
 from .scheduling_heun_discrete import HeunDiscreteScheduler
 from .scheduling_ipndm import IPNDMScheduler
 from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
 from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
 from .scheduling_karras_ve import KarrasVeScheduler
 from .scheduling_pndm import PNDMScheduler
 from .scheduling_repaint import RePaintScheduler
 from .scheduling_sde_ve import ScoreSdeVeScheduler
 from .scheduling_sde_vp import ScoreSdeVpScheduler
 from .scheduling_unclip import UnCLIPScheduler
 from .scheduling_unipc_multistep import UniPCMultistepScheduler
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_flax_objects import *  # noqa F403
else:
 from .scheduling_ddim_flax import FlaxDDIMScheduler
 from .scheduling_ddpm_flax import FlaxDDPMScheduler
 from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
 from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
 from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
 from .scheduling_pndm_flax import FlaxPNDMScheduler
 from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
 from .scheduling_utils_flax import (
     FlaxKarrasDiffusionSchedulers,
     FlaxSchedulerMixin,
     FlaxSchedulerOutput,
     broadcast_to_shape_from_left,
 )


try:
 if not (is_torch_available() and is_scipy_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
 from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
 if not (is_torch_available() and is_torchsde_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
 from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler

