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__  (					__A   , __A   , __A   , __A   , __A	):







    '''simple docstring'''
    # 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
    _lowerCamelCase     :		Optional[int] 						=						TapasConfig.from_json_file(__A	)
    # set absolute/relative position embeddings parameter
    _lowerCamelCase     :		List[str] 						=						reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        _lowerCamelCase     :		Optional[int] 						=						TapasForQuestionAnswering(config=__A	)
    elif task == "WTQ":
        # run_task_main.py hparams
        _lowerCamelCase     :		int 						=						4
        _lowerCamelCase     :		Union[str, Any] 						=						True
        # hparam_utils.py hparams
        _lowerCamelCase     :		Any 						=						0.664_694
        _lowerCamelCase     :		int 						=						0.207_951
        _lowerCamelCase     :		Union[str, Any] 						=						0.121_194
        _lowerCamelCase     :		int 						=						True
        _lowerCamelCase     :		List[Any] 						=						True
        _lowerCamelCase     :		List[str] 						=						False
        _lowerCamelCase     :		Tuple 						=						0.0_352_513

        _lowerCamelCase     :		Tuple 						=						TapasForQuestionAnswering(config=__A	)
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        _lowerCamelCase     :		Dict 						=						4
        _lowerCamelCase     :		List[str] 						=						False
        # hparam_utils.py hparams
        _lowerCamelCase     :		str 						=						36.4_519
        _lowerCamelCase     :		Any 						=						0.903_421
        _lowerCamelCase     :		int 						=						222.088
        _lowerCamelCase     :		Optional[Any] 						=						True
        _lowerCamelCase     :		Optional[int] 						=						True
        _lowerCamelCase     :		Optional[int] 						=						True
        _lowerCamelCase     :		Any 						=						0.763_141

        _lowerCamelCase     :		List[str] 						=						TapasForQuestionAnswering(config=__A	)
    elif task == "TABFACT":
        _lowerCamelCase     :		List[str] 						=						TapasForSequenceClassification(config=__A	)
    elif task == "MLM":
        _lowerCamelCase     :		int 						=						TapasForMaskedLM(config=__A	)
    elif task == "INTERMEDIATE_PRETRAINING":
        _lowerCamelCase     :		Union[str, Any] 						=						TapasModel(config=__A	)
    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(__A   , __A   , __A	)

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

    # Save tokenizer files
    print(F"""Save tokenizer files to {pytorch_dump_path}"""	)
    _lowerCamelCase     :		List[Any] 						=						TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""   , model_max_length=512	)
    tokenizer.save_pretrained(__A	)

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


if __name__ == "__main__":
    lowerCAmelCase			:							Dict     =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			:							List[str]     =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,
    )





import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowerCAmelCase			:							Tuple     =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def 						A__  (					__A   , __A   , __A   , __A   , __A   , __A   , __A   , __A=False   , ):







    '''simple docstring'''
    output_path.parent.mkdir(parents=__A   , exist_ok=__A	)
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , use_external_data_format=__A   , enable_onnx_checker=__A   , opset_version=__A   , )
    else:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , opset_version=__A   , )







@torch.no_grad()
def 						A__  (					__A   , __A   , __A   , __A = False	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        _lowerCamelCase     :		str 						=						"""cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
    else:
        _lowerCamelCase     :		List[str] 						=						"""cpu"""
    _lowerCamelCase     :		Dict 						=						Path(__A	)

    # VAE DECODER
    _lowerCamelCase     :		Optional[Any] 						=						AutoencoderKL.from_pretrained(model_path + """/vae"""	)
    _lowerCamelCase     :		List[str] 						=						vae_decoder.config.latent_channels
    # forward only through the decoder part
    _lowerCamelCase     :		Tuple 						=						vae_decoder.decode
    onnx_export(
        __A   , model_args=(
            torch.randn(1   , __A   , 25   , 25	).to(device=__A   , dtype=__A	),
            False,
        )   , output_path=output_path / """vae_decoder""" / """model.onnx"""   , ordered_input_names=["""latent_sample""", """return_dict"""]   , output_names=["""sample"""]   , dynamic_axes={
            """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
        }   , opset=__A   , )
    del vae_decoder


if __name__ == "__main__":
    lowerCAmelCase			:							Optional[int]     =argparse.ArgumentParser()

    parser.add_argument(
        "--model_path",
        type=str,
        required=True,
        help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
    )

    parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
    parser.add_argument(
        "--opset",
        default=14,
        type=int,
        help="The version of the ONNX operator set to use.",
    )
    parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

    lowerCAmelCase			:							Optional[Any]     =parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print("SD: Done: ONNX")




