


"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase_  :  Optional[int]     		=    {
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

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

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_  :  Dict     		=    [
           """OpenLlamaForCausalLM""",
           """OpenLlamaModel""",
           """OpenLlamaPreTrainedModel""",
           """OpenLlamaForSequenceClassification""",
       ]


if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

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

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

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_open_llama import (
                  OpenLlamaForCausalLM,
                  OpenLlamaForSequenceClassification,
                  OpenLlamaModel,
                  OpenLlamaPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase_  :  List[str]     		=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



"""simple docstring"""







from .stack import Stack
def _A		(		lowercase						):







    """simple docstring"""
    a											=Stack()
    a											={'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(lowercase						)
        elif bracket in (")", "]", "}"):
            if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                return False
    return stack.is_empty()


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       lowerCamelCase_  :  List[str]     		=    ["""((()))""", """((())""", """(()))"""]
       print("""Balanced parentheses demonstration:\n""")
       for example in examples:
              lowerCamelCase_  :  str     		=    """""" if balanced_parentheses(example) else """not """
              print(F'{example} is {not_str}balanced')