





"""simple docstring"""


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 SCREAMING_SNAKE_CASE_  (			snake_case  :   Tuple		,    snake_case  :   Optional[Any]		,    snake_case  :   str		,    snake_case  :   Optional[Any]		,    snake_case  :   Union[str, Any]						)->						str:
       _lowerCamelCase             =   TapasConfig.from_json_file(__A						)
       # set absolute/relative position embeddings parameter
       _lowerCamelCase             =   reset_position_index_per_cell

       # set remaining parameters of TapasConfig as well as the model based on the task
       if task == "SQA":
              _lowerCamelCase             =   TapasForQuestionAnswering(config=__A						)
       elif task == "WTQ":
              # run_task_main.py hparams
              _lowerCamelCase             =   4
              _lowerCamelCase             =   True
              # hparam_utils.py hparams
              _lowerCamelCase             =   0.6_6_4_6_9_4
              _lowerCamelCase             =   0.2_0_7_9_5_1
              _lowerCamelCase             =   0.1_2_1_1_9_4
              _lowerCamelCase             =   True
              _lowerCamelCase             =   True
              _lowerCamelCase             =   False
              _lowerCamelCase             =   0.0_3_5_2_5_1_3

              _lowerCamelCase             =   TapasForQuestionAnswering(config=__A						)
       elif task == "WIKISQL_SUPERVISED":
              # run_task_main.py hparams
              _lowerCamelCase             =   4
              _lowerCamelCase             =   False
              # hparam_utils.py hparams
              _lowerCamelCase             =   3_6.4_5_1_9
              _lowerCamelCase             =   0.9_0_3_4_2_1
              _lowerCamelCase             =   2_2_2.0_8_8
              _lowerCamelCase             =   True
              _lowerCamelCase             =   True
              _lowerCamelCase             =   True
              _lowerCamelCase             =   0.7_6_3_1_4_1

              _lowerCamelCase             =   TapasForQuestionAnswering(config=__A						)
       elif task == "TABFACT":
              _lowerCamelCase             =   TapasForSequenceClassification(config=__A						)
       elif task == "MLM":
              _lowerCamelCase             =   TapasForMaskedLM(config=__A						)
       elif task == "INTERMEDIATE_PRETRAINING":
              _lowerCamelCase             =   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             =   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__":
      A_	: Optional[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."""
      )
      A_	: int      				=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,
      )









# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCamelCase__		 =     '''Create a default config file for Accelerate with only a few flags set.'''







def 						lowerCamelCase__      ( __A    :List[Any]="no"	,__A    :str = default_json_config_file	,__A    :bool = False		):


       """simple docstring"""




       __snake_case       	=							Path(__A		)
       path.parent.mkdir(parents=__A	,exist_ok=__A		)
       if path.exists():
              print(
                  F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'		)
              return False
       __snake_case       	=							mixed_precision.lower()
       if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
              raise ValueError(
                  F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'		)
       __snake_case       	=							{
           """compute_environment""": """LOCAL_MACHINE""",
           """mixed_precision""": mixed_precision,
       }
       if torch.cuda.is_available():
              __snake_case       	=							torch.cuda.device_count()
              __snake_case       	=							num_gpus
              __snake_case       	=							False
              if num_gpus > 1:
                     __snake_case       	=							"""MULTI_GPU"""
              else:
                     __snake_case       	=							"""NO"""
       elif is_xpu_available() and use_xpu:
              __snake_case       	=							torch.xpu.device_count()
              __snake_case       	=							num_xpus
              __snake_case       	=							False
              if num_xpus > 1:
                     __snake_case       	=							"""MULTI_XPU"""
              else:
                     __snake_case       	=							"""NO"""
       elif is_npu_available():
              __snake_case       	=							torch.npu.device_count()
              __snake_case       	=							num_npus
              __snake_case       	=							False
              if num_npus > 1:
                     __snake_case       	=							"""MULTI_NPU"""
              else:
                     __snake_case       	=							"""NO"""
       else:
              __snake_case       	=							0
              __snake_case       	=							True
              __snake_case       	=							1
              __snake_case       	=							"""NO"""
       __snake_case       	=							ClusterConfig(**__A		)
       config.to_json_file(__A		)
       return path







def 						lowerCamelCase__      ( __A    :Dict	,__A    :List[Any]		):


       """simple docstring"""




       __snake_case       	=							parser.add_parser("""default"""	,parents=__A	,help=__A	,formatter_class=__A		)
       parser.add_argument(
           """--config_file"""	,default=__A	,help=(
               """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
               """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
               """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
               """with 'huggingface'."""
           )	,dest="""save_location"""	,)

       parser.add_argument(
           """--mixed_precision"""	,choices=["""no""", """fp16""", """bf16"""]	,type=__A	,help="""Whether or not to use mixed precision training. """
           """Choose between FP16 and BF16 (bfloat16) training. """
           """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""	,default="""no"""	,)
       parser.set_defaults(func=__A		)
       return parser







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							write_basic_config(args.mixed_precision	,args.save_location		)
       if config_file:
              print(F'accelerate configuration saved at {config_file}'		)

