from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def A__  (			lowercase:     Namespace					)				->		Optional[Any]:
       return ConvertCommand(
           args.model_type,		args.tf_checkpoint,		args.pytorch_dump_output,		args.config,		args.finetuning_task_name					)


_lowercase							:      Optional[int]      							='''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					ArgumentParser     )    ->				Dict:
         A						:     Any         =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=SCREAMING_SNAKE_CASE__      ,	required=SCREAMING_SNAKE_CASE__      ,	help='Model\'s type.'     )
         train_parser.add_argument(
             '--tf_checkpoint'      ,	type=SCREAMING_SNAKE_CASE__      ,	required=SCREAMING_SNAKE_CASE__      ,	help='TensorFlow checkpoint path or folder.'     )
         train_parser.add_argument(
             '--pytorch_dump_output'      ,	type=SCREAMING_SNAKE_CASE__      ,	required=SCREAMING_SNAKE_CASE__      ,	help='Path to the PyTorch saved model output.'     )
         train_parser.add_argument('--config'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=''      ,	help='Configuration file path or folder.'     )
         train_parser.add_argument(
             '--finetuning_task_name'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=SCREAMING_SNAKE_CASE__      ,	help='Optional fine-tuning task name if the TF model was a finetuned model.'      ,	)
         train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__     )







  def __init__(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					str      ,	*SCREAMING_SNAKE_CASE__    :					Tuple      ,	)    ->				Tuple:
         A						:     Optional[Any]         =logging.get_logger('transformers-cli/converting'     )

         self._logger.info(f'Loading model {model_type}'     )
         A						:     List[Any]         =model_type
         A						:     Any         =tf_checkpoint
         A						:     Optional[int]         =pytorch_dump_output
         A						:     Dict         =config
         A						:     str         =finetuning_task_name







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Tuple:
         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(SCREAMING_SNAKE_CASE__     )

                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(SCREAMING_SNAKE_CASE__     )

                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(SCREAMING_SNAKE_CASE__     )

                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(SCREAMING_SNAKE_CASE__     )

                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(SCREAMING_SNAKE_CASE__     )

                if "ckpt" in self._tf_checkpoint.lower():
                       A						:     Optional[int]         =self._tf_checkpoint
                       A						:     Optional[Any]         =''
                else:
                       A						:     str         =self._tf_checkpoint
                       A						:     Dict         =''
                convert_transfo_xl_checkpoint_to_pytorch(
                    SCREAMING_SNAKE_CASE__      ,	self._config      ,	self._pytorch_dump_output      ,	SCREAMING_SNAKE_CASE__     )
         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(SCREAMING_SNAKE_CASE__     )

                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(SCREAMING_SNAKE_CASE__     )

                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]'     )







_lowercase							:      Dict      							='''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
    from .utils import rich






