
import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Any:
				# Initialise PyTorch model.
				# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
				# TapasConfig to False.

				# initialize configuration from json file
				UpperCamelCase_:				Optional[Any]						=    TapasConfig.from_json_file(__lowerCAmelCase    )
				# set absolute/relative position embeddings parameter
				UpperCamelCase_:				str						=    reset_position_index_per_cell

				# set remaining parameters of TapasConfig as well as the model based on the task
				if task == "SQA":
								UpperCamelCase_:				Optional[Any]						=    TapasForQuestionAnswering(config=__lowerCAmelCase    )
				elif task == "WTQ":
								# run_task_main.py hparams
								UpperCamelCase_:				Any						=    4
								UpperCamelCase_:				str						=    True
								# hparam_utils.py hparams
								UpperCamelCase_:				List[Any]						=    0.664694
								UpperCamelCase_:				Tuple						=    0.207951
								UpperCamelCase_:				int						=    0.121194
								UpperCamelCase_:				int						=    True
								UpperCamelCase_:				Optional[int]						=    True
								UpperCamelCase_:				List[Any]						=    False
								UpperCamelCase_:				Dict						=    0.0352513

								UpperCamelCase_:				Optional[int]						=    TapasForQuestionAnswering(config=__lowerCAmelCase    )
				elif task == "WIKISQL_SUPERVISED":
								# run_task_main.py hparams
								UpperCamelCase_:				Any						=    4
								UpperCamelCase_:				Any						=    False
								# hparam_utils.py hparams
								UpperCamelCase_:				Tuple						=    36.4519
								UpperCamelCase_:				Tuple						=    0.903421
								UpperCamelCase_:				Optional[Any]						=    222.088
								UpperCamelCase_:				str						=    True
								UpperCamelCase_:				Tuple						=    True
								UpperCamelCase_:				Tuple						=    True
								UpperCamelCase_:				int						=    0.763141

								UpperCamelCase_:				Optional[Any]						=    TapasForQuestionAnswering(config=__lowerCAmelCase    )
				elif task == "TABFACT":
								UpperCamelCase_:				int						=    TapasForSequenceClassification(config=__lowerCAmelCase    )
				elif task == "MLM":
								UpperCamelCase_:				Optional[int]						=    TapasForMaskedLM(config=__lowerCAmelCase    )
				elif task == "INTERMEDIATE_PRETRAINING":
								UpperCamelCase_:				Union[str, Any]						=    TapasModel(config=__lowerCAmelCase    )
				else:
								raise ValueError(F'''Task {task} not supported.'''    )

				print(F'''Building PyTorch model from configuration: {config}'''    )
				# Load weights from tf checkpoint
				load_tf_weights_in_tapas(__lowerCAmelCase  ,     __lowerCAmelCase  ,     __lowerCAmelCase    )

				# Save pytorch-model (weights and configuration)
				print(F'''Save PyTorch model to {pytorch_dump_path}'''    )
				model.save_pretrained(__lowerCAmelCase    )

				# Save tokenizer files
				print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
				UpperCamelCase_:				Optional[int]						=    TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""  ,     model_max_length=5_12    )
				tokenizer.save_pretrained(__lowerCAmelCase    )

				print("""Used relative position embeddings:"""  ,     model.config.reset_position_index_per_cell    )


if __name__ == "__main__":
							lowerCamelCase_    :			List[Any]    =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
							)
							parser.add_argument(
							    """--reset_position_index_per_cell""",
							    default=False,
							    action="""store_true""",
							    help="""Whether to use relative position embeddings or not. Defaults to True.""",
							)
							parser.add_argument(
							    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--tapas_config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained TAPAS 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."""
							)
							lowerCamelCase_    :			Any    =	parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.task,
							    args.reset_position_index_per_cell,
							    args.tf_checkpoint_path,
							    args.tapas_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

