



"""simple docstring"""


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Namespace				):






				'''simple docstring'''



				return ConvertCommand(
				    args.model_type					,     args.tf_checkpoint					,     args.pytorch_dump_output					,     args.config					,     args.finetuning_task_name				)


__A   :							Tuple										=   "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			@staticmethod
			def  lowercase__						(		__UpperCamelCase     :    ArgumentParser   )->List[str]:
							_UpperCAmelCase					=    parser.add_parser(
							    '''convert'''				,   help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''				,   )
							train_parser.add_argument('''--model_type'''				,   type=__UpperCamelCase				,   required=__UpperCamelCase				,   help='''Model\'s type.'''   )
							train_parser.add_argument(
							    '''--tf_checkpoint'''				,   type=__UpperCamelCase				,   required=__UpperCamelCase				,   help='''TensorFlow checkpoint path or folder.'''   )
							train_parser.add_argument(
							    '''--pytorch_dump_output'''				,   type=__UpperCamelCase				,   required=__UpperCamelCase				,   help='''Path to the PyTorch saved model output.'''   )
							train_parser.add_argument('''--config'''				,   type=__UpperCamelCase				,   default=''''''				,   help='''Configuration file path or folder.'''   )
							train_parser.add_argument(
							    '''--finetuning_task_name'''				,   type=__UpperCamelCase				,   default=__UpperCamelCase				,   help='''Optional fine-tuning task name if the TF model was a finetuned model.'''				,   )
							train_parser.set_defaults(func=__UpperCamelCase   )



			def __init__(		self     :    Any				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str				,   *__UpperCamelCase     :    str				,   )->Any:
							_UpperCAmelCase					=    logging.get_logger('''transformers-cli/converting'''   )

							self._logger.info(F'Loading model {model_type}'   )
							_UpperCAmelCase					=    model_type
							_UpperCAmelCase					=    tf_checkpoint
							_UpperCAmelCase					=    pytorch_dump_output
							_UpperCAmelCase					=    config
							_UpperCAmelCase					=    finetuning_task_name



			def  lowercase__						(		self     :    List[Any]   )->int:
							if self._model_type == "albert":
											try:
															from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(__UpperCamelCase   )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,   self._config				,   self._pytorch_dump_output   )
							elif self._model_type == "bert":
											try:
															from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(__UpperCamelCase   )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,   self._config				,   self._pytorch_dump_output   )
							elif self._model_type == "funnel":
											try:
															from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(__UpperCamelCase   )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,   self._config				,   self._pytorch_dump_output   )
							elif self._model_type == "t5":
											try:
															from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
											except ImportError:
															raise ImportError(__UpperCamelCase   )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,   self._config				,   self._pytorch_dump_output   )
							elif self._model_type == "gpt":
											from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
											    convert_openai_checkpoint_to_pytorch,
											)

											convert_openai_checkpoint_to_pytorch(self._tf_checkpoint				,   self._config				,   self._pytorch_dump_output   )
							elif self._model_type == "transfo_xl":
											try:
															from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
															    convert_transfo_xl_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(__UpperCamelCase   )

											if "ckpt" in self._tf_checkpoint.lower():
															_UpperCAmelCase					=    self._tf_checkpoint
															_UpperCAmelCase					=    ''''''
											else:
															_UpperCAmelCase					=    self._tf_checkpoint
															_UpperCAmelCase					=    ''''''
											convert_transfo_xl_checkpoint_to_pytorch(
											    __UpperCamelCase				,   self._config				,   self._pytorch_dump_output				,   __UpperCamelCase   )
							elif self._model_type == "gpt2":
											try:
															from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
															    convert_gpta_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(__UpperCamelCase   )

											convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint				,   self._config				,   self._pytorch_dump_output   )
							elif self._model_type == "xlnet":
											try:
															from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
															    convert_xlnet_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(__UpperCamelCase   )

											convert_xlnet_checkpoint_to_pytorch(
											    self._tf_checkpoint				,   self._config				,   self._pytorch_dump_output				,   self._finetuning_task_name   )
							elif self._model_type == "xlm":
											from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
											    convert_xlm_checkpoint_to_pytorch,
											)

											convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint				,   self._pytorch_dump_output   )
							elif self._model_type == "lxmert":
											from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
											    convert_lxmert_checkpoint_to_pytorch,
											)

											convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint				,   self._pytorch_dump_output   )
							elif self._model_type == "rembert":
											from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
											    convert_rembert_tf_checkpoint_to_pytorch,
											)

											convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint				,   self._config				,   self._pytorch_dump_output   )
							else:
											raise ValueError(
											    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''   )








"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple   )->int:
							_UpperCAmelCase					=    inspect.getfile(accelerate.test_utils   )
							_UpperCAmelCase					=    os.path.sep.join(
							    mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']   )

							from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

							_UpperCAmelCase					=    test_metrics



			@require_cpu
			def  lowercase__						(		self     :    Any   )->int:
							debug_launcher(self.test_metrics.main				,   num_processes=1   )



			@require_cpu
			def  lowercase__						(		self     :    List[str]   )->List[str]:
							debug_launcher(self.test_metrics.main   )



			@require_single_gpu
			def  lowercase__						(		self     :    List[Any]   )->Dict:
							self.test_metrics.main()



			@require_multi_gpu
			def  lowercase__						(		self     :    str   )->int:
							print(F'Found {torch.cuda.device_count()} devices.'   )
							_UpperCAmelCase					=    ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
							with patch_environment(omp_num_threads=1   ):
											execute_subprocess_async(__UpperCamelCase				,   env=os.environ.copy()   )



