
"""simple docstring"""




from typing import TYPE_CHECKING

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


_A			:       str   		=       {
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       List[Any]   		=       [
       'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'GPTBigCodeForSequenceClassification',
       'GPTBigCodeForTokenClassification',
       'GPTBigCodeForCausalLM',
       'GPTBigCodeModel',
       'GPTBigCodePreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpt_bigcode import (
          GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTBigCodeForCausalLM,
          GPTBigCodeForSequenceClassification,
          GPTBigCodeForTokenClassification,
          GPTBigCodeModel,
          GPTBigCodePreTrainedModel,
      )


else:
   import sys

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






'''simple docstring'''



import os
import sys

__lowercase	:						List[Any]       =	os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__lowercase	:						int       =	[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[str]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoConfig.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoTokenizer.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   str						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoTokenizer.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModel.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					return AutoModel.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForCausalLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[Any]						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					return AutoModelForCausalLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForMaskedLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoModelForMaskedLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForSequenceClassification.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoModelForSequenceClassification.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Any						,      **_SCREAMING_SNAKE_CASE      :   List[str]   ):
					return AutoModelForQuestionAnswering.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )



