




from typing import TYPE_CHECKING

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


lowerCAmelCase_						=			{'configuration_xglm': ['XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XGLMConfig']}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			['XGLMTokenizer']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			['XGLMTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			[
     'XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
     'XGLMForCausalLM',
     'XGLMModel',
     'XGLMPreTrainedModel',
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			[
     'FlaxXGLMForCausalLM',
     'FlaxXGLMModel',
     'FlaxXGLMPreTrainedModel',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			[
     'TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFXGLMForCausalLM',
     'TFXGLMModel',
     'TFXGLMPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xglm import XGLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xglm_fast import XGLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
      TFXGLMPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_						=			_LazyModule(__name__, globals()['__file__'], _import_structure)





def  snake_case(    __magic_name__			,		__magic_name__   )			->							bool:





     '''simple docstring'''


     lowercase :       List[Any]       =			len(__magic_name__   )
     lowercase :       str       =			[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

     # for each arr value, a sum of zero(0) can be formed by not taking any element
     # hence True/1
     for i in range(arr_len + 1   ):
          lowercase :       List[str]       =			True

     # sum is not zero and set is empty then false
     for i in range(1			,		required_sum + 1   ):
          lowercase :       str       =			False

     for i in range(1			,		arr_len + 1   ):
          for j in range(1			,		required_sum + 1   ):
               if arr[i - 1] > j:
                    lowercase :       Optional[Any]       =			subset[i - 1][j]
               if arr[i - 1] <= j:
                    lowercase :       Dict       =			subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

     return subset[arr_len][required_sum]


if __name__ == "__main__":
 import doctest

 doctest.testmod()