


'''simple docstring'''







from __future__ import annotations







def _UpperCamelCase	(					__A				,      __A				,      __A   )							->		int | float:


      '''simple docstring'''



      if len(__A   ) == 0:
            raise ValueError("find_min() arg is an empty sequence"   )
      if (
          left >= len(__A   )
          or left < -len(__A   )
          or right >= len(__A   )
          or right < -len(__A   )
      ):
            raise IndexError("list index out of range"   )
      if left == right:
            return nums[left]
      UpperCamelCase__  =    (left + right) >> 1  # the middle
      UpperCamelCase__  =    find_min(__A				,      __A				,      __A   )  # find min in range[left, mid]
      UpperCamelCase__  =    find_min(__A				,      mid + 1				,      __A   )  # find min in range[mid + 1, right]

      return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__  : Any				=	{
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Tuple				=	['CodeGenTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Dict				=	[
      'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'CodeGenForCausalLM',
      'CodeGenModel',
      'CodeGenPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  from .tokenization_codegen import CodeGenTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_codegen_fast import CodeGenTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_codegen import (
        CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
        CodeGenForCausalLM,
        CodeGenModel,
        CodeGenPreTrainedModel,
    )

else:
  import sys

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





