





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase : List[str]											=			{"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase : List[Any]											=			[
       "OPT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "OPTForCausalLM",
       "OPTModel",
       "OPTPreTrainedModel",
       "OPTForSequenceClassification",
       "OPTForQuestionAnswering",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase : str											=			["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase : Optional[Any]											=			[
       "FlaxOPTForCausalLM",
       "FlaxOPTModel",
       "FlaxOPTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_opt import (
          OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OPTForCausalLM,
          OPTForQuestionAnswering,
          OPTForSequenceClassification,
          OPTModel,
          OPTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
   import sys

   __lowerCamelCase : Optional[Any]											=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











'''simple docstring'''

from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return DownloadCommand(args.model		,							args.cache_dir		,							args.force		,							args.trust_remote_code							)


class       UpperCAmelCase (						_lowercase  ):




		@staticmethod
		def 				UpperCAmelCase__   (A__      :	ArgumentParser				)					-> List[Any]:
						lowercase         = parser.add_parser("download"				)
						download_parser.add_argument(
						    "--cache-dir"							,			type=A__							,			default=A__							,			help="Path to location to store the models"				)
						download_parser.add_argument(
						    "--force"							,			action="store_true"							,			help="Force the model to be download even if already in cache-dir"				)
						download_parser.add_argument(
						    "--trust-remote-code"							,			action="store_true"							,			help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"							,			)
						download_parser.add_argument("model"							,			type=A__							,			help="Name of the model to download"				)
						download_parser.set_defaults(func=A__				)




		def __init__(self      :	Tuple							,			A__      :	str							,			A__      :	str							,			A__      :	bool							,			A__      :	bool				)					-> str:
						lowercase         = model
						lowercase         = cache
						lowercase         = force
						lowercase         = trust_remote_code





		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)
						AutoTokenizer.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)




