





from argparse import ArgumentParser

from . import BaseTransformersCLICommand



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Optional[int]:


      '''simple docstring'''


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



class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    ArgumentParser       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parser.add_parser("""download"""       )
           download_parser.add_argument(
               """--cache-dir"""						,type=lowerCamelCase__						,default=lowerCamelCase__						,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=lowerCamelCase__						,help="""Name of the model to download"""       )
           download_parser.set_defaults(func=lowerCamelCase__       )






     def __init__(							self						:    Union[str, Any]						,lowerCamelCase__						:    str						,lowerCamelCase__						:    str						,lowerCamelCase__						:    bool						,lowerCamelCase__						:    bool       )     ->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						model
           SCREAMING_SNAKE_CASE		=						cache
           SCREAMING_SNAKE_CASE		=						force
           SCREAMING_SNAKE_CASE		=						trust_remote_code






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Union[str, Any]:




           '''simple docstring'''


           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       )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						   =       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       [
          """LlamaForCausalLM""",
          """LlamaModel""",
          """LlamaPreTrainedModel""",
          """LlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama_fast import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
      import sys

      SCREAMING_SNAKE_CASE_						   =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


