


"""simple docstring"""







import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 					lowercase      (    A_	)->				Tuple:
		'''simple docstring'''







		return EnvironmentCommand()


def 					lowercase      (    A_	)->				Optional[Any]:
		'''simple docstring'''







		return EnvironmentCommand(args.accelerate_config_file	)







class 						_A  (  a__  ):







				"""simple docstring"""




				@staticmethod
				def      __snake_case							( __UpperCAmelCase    :   str):
						a						:		str											=      parser.add_parser("env")
						download_parser.set_defaults(func=_lowerCamelCase)
						download_parser.add_argument(
						    "--accelerate-config_file"    ,			default=_lowerCamelCase    ,			help="The accelerate config file to use for the default values in the launching script."    ,			)
						download_parser.set_defaults(func=_lowerCamelCase)






				def __init__( self    :   Dict    ,			__UpperCAmelCase    :   str    ,			*__UpperCAmelCase    :   Any):
						a						:		List[Any]											=      accelerate_config_file






				def      __snake_case							( self    :   List[Any]):
						a						:		List[Any]											=      '''not installed'''
						if is_safetensors_available():
								import safetensors

								a						:		Any											=      safetensors.__version__
						elif importlib.util.find_spec("safetensors") is not None:
								import safetensors

								a						:		int											=      f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

						a						:		Optional[int]											=      '''not installed'''
						a						:		Any											=      '''not found'''
						if is_accelerate_available():
								import accelerate
								from accelerate.commands.config import default_config_file, load_config_from_file

								a						:		List[Any]											=      accelerate.__version__
								# Get the default from the config file.
								if self._accelerate_config_file is not None or os.path.isfile(_lowerCamelCase):
										a						:		Union[str, Any]											=      load_config_from_file(self._accelerate_config_file).to_dict()

								a						:		Tuple											=      (
								    '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()])
								    if isinstance(_lowerCamelCase    ,			_lowerCamelCase)
								    else f'''\t{accelerate_config}'''
								)

						a						:		List[str]											=      '''not installed'''
						a						:		List[Any]											=      '''NA'''
						if is_torch_available():
								import torch

								a						:		List[str]											=      torch.__version__
								a						:		Optional[Any]											=      torch.cuda.is_available()

						a						:		str											=      '''not installed'''
						a						:		Any											=      '''NA'''
						if is_tf_available():
								import tensorflow as tf

								a						:		int											=      tf.__version__
								try:
										# deprecated in v2.1
										a						:		Dict											=      tf.test.is_gpu_available()
								except AttributeError:
										# returns list of devices, convert to bool
										a						:		Any											=      bool(tf.config.list_physical_devices("GPU"))

						a						:		Any											=      '''not installed'''
						a						:		Union[str, Any]											=      '''not installed'''
						a						:		int											=      '''not installed'''
						a						:		Union[str, Any]											=      '''NA'''
						if is_flax_available():
								import flax
								import jax
								import jaxlib

								a						:		Optional[int]											=      flax.__version__
								a						:		Tuple											=      jax.__version__
								a						:		Optional[int]											=      jaxlib.__version__
								a						:		List[Any]											=      jax.lib.xla_bridge.get_backend().platform

						a						:		Any											=      {
						    '''`transformers` version''': version,
						    '''Platform''': platform.platform(),
						    '''Python version''': platform.python_version(),
						    '''Huggingface_hub version''': huggingface_hub.__version__,
						    '''Safetensors version''': f'''{safetensors_version}''',
						    '''Accelerate version''': f'''{accelerate_version}''',
						    '''Accelerate config''': f'''{accelerate_config_str}''',
						    '''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
						    '''Tensorflow version (GPU?)''': f'''{tf_version} ({tf_cuda_available})''',
						    '''Flax version (CPU?/GPU?/TPU?)''': f'''{flax_version} ({jax_backend})''',
						    '''Jax version''': f'''{jax_version}''',
						    '''JaxLib version''': f'''{jaxlib_version}''',
						    '''Using GPU in script?''': '''<fill in>''',
						    '''Using distributed or parallel set-up in script?''': '''<fill in>''',
						}

						print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
						print(self.format_dict(_lowerCamelCase))

						return info






				@staticmethod
				def      __snake_case							( __UpperCAmelCase    :   List[Any]):
						return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]) + "\n"










"""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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a						:		Dict											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WTQ":
    # run_task_main.py hparams
    a						:		Any											=      4
    a						:		Dict											=      True
    # hparam_utils.py hparams
    a						:		str											=      0.6_6_4_6_9_4
    a						:		Optional[int]											=      0.2_0_7_9_5_1
    a						:		Optional[Any]											=      0.1_2_1_1_9_4
    a						:		Union[str, Any]											=      True
    a						:		int											=      True
    a						:		Tuple											=      False
    a						:		Dict											=      0.0_3_5_2_5_1_3

    a						:		List[str]											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a						:		Union[str, Any]											=      4
    a						:		List[Any]											=      False
    # hparam_utils.py hparams
    a						:		Dict											=      3_6.4_5_1_9
    a						:		List[str]											=      0.9_0_3_4_2_1
    a						:		Optional[Any]											=      2_2_2.0_8_8
    a						:		Dict											=      True
    a						:		Union[str, Any]											=      True
    a						:		List[str]											=      True
    a						:		List[str]											=      0.7_6_3_1_4_1

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[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}'''	)
  a						:		Optional[int]											=      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__":
 __lowercase    =				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."""
 )
 __lowercase    =				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,
 )






