


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				int:
  return ConvertCommand(
      args.model_type							,   args.tf_checkpoint							,   args.pytorch_dump_output							,   args.config							,   args.finetuning_task_name				)


lowerCamelCase__							:						Optional[int]							     =   """\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 		__magic_name__						(snake_case_				):





     '''simple docstring'''



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

     def __init__(					self:int     ,		_a:Optional[int]     ,		_a:Dict     ,		_a:Tuple     ,		_a:List[str]     ,		_a:List[Any]     ,		*_a:Optional[int]     ,		):
       snake_case__						= logging.get_logger('''transformers-cli/converting'''   )

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







     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):







       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(_a   )

         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(_a   )

         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(_a   )

         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(_a   )

         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(_a   )

         if "ckpt" in self._tf_checkpoint.lower():
           snake_case__						= self._tf_checkpoint
           snake_case__						= ''
         else:
           snake_case__						= self._tf_checkpoint
           snake_case__						= ''
         convert_transfo_xl_checkpoint_to_pytorch(
             _a     ,		self._config     ,		self._pytorch_dump_output     ,		_a   )
       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(_a   )

         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(_a   )

         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'''



from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


UpperCamelCase_    	=				logging.get_logger(__name__)


UpperCamelCase_    	=				TypeVar("DatasetType", Dataset, IterableDataset)




def   lowercase__(			__UpperCamelCase:					List[DatasetType]      ,__UpperCamelCase:					Optional[List[float]] = None      ,__UpperCamelCase:					Optional[int] = None      ,__UpperCamelCase:					Optional[DatasetInfo] = None      ,__UpperCamelCase:					Optional[NamedSplit] = None      ,__UpperCamelCase:					Literal["first_exhausted", "all_exhausted"] = "first_exhausted"      ,):

      """simple docstring"""






      from .arrow_dataset import Dataset
      from .iterable_dataset import IterableDataset

      if not datasets:
            raise ValueError('Unable to interleave an empty list of datasets.'    )
      for i, dataset in enumerate(__UpperCamelCase    ):
            if not isinstance(__UpperCamelCase      ,(Dataset, IterableDataset)    ):
                  if isinstance(__UpperCamelCase      ,(DatasetDict, IterableDatasetDict)    ):
                        if not dataset:
                              raise ValueError(
                                  f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                                  'is an empty dataset dictionary.'    )
                        raise ValueError(
                            f"Dataset at position {i} has at least one split: {list(__UpperCamelCase    )}\n"
                            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCamelCase    )    )}']"    )
                  raise ValueError(
                      f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCamelCase    ).__name__}."    )
            if i == 0:
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						(
                      (Dataset, IterableDataset) if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(__UpperCamelCase      ,__UpperCamelCase    ):
                  raise ValueError(
                      f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."    )
      if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
            raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."    )
      if dataset_type is Dataset:
            return _interleave_map_style_datasets(
                __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,stopping_strategy=__UpperCamelCase    )
      else:
            return _interleave_iterable_datasets(
                __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,stopping_strategy=__UpperCamelCase    )





def   lowercase__(			__UpperCamelCase:					List[DatasetType]      ,__UpperCamelCase:					Optional[DatasetInfo] = None      ,__UpperCamelCase:					Optional[NamedSplit] = None      ,__UpperCamelCase:					int = 0      ,):

      """simple docstring"""






      if not dsets:
            raise ValueError('Unable to concatenate an empty list of datasets.'    )
      for i, dataset in enumerate(__UpperCamelCase    ):
            if not isinstance(__UpperCamelCase      ,(Dataset, IterableDataset)    ):
                  if isinstance(__UpperCamelCase      ,(DatasetDict, IterableDatasetDict)    ):
                        if not dataset:
                              raise ValueError(
                                  f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                                  'is an empty dataset dictionary.'    )
                        raise ValueError(
                            f"Dataset at position {i} has at least one split: {list(__UpperCamelCase    )}\n"
                            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCamelCase    )    )}']"    )
                  raise ValueError(
                      f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCamelCase    ).__name__}."    )
            if i == 0:
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						(
                      (Dataset, IterableDataset) if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(__UpperCamelCase      ,__UpperCamelCase    ):
                  raise ValueError(
                      f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."    )
      if dataset_type is Dataset:
            return _concatenate_map_style_datasets(__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,axis=__UpperCamelCase    )
      else:
            return _concatenate_iterable_datasets(__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,axis=__UpperCamelCase    )


