




"""simple docstring"""


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,
)


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

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

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

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


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


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_						=				[
         '''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

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








"""simple docstring"""


from __future__ import annotations


def 					UpperCAmelCase__       (snake_case__							:					list[int]		,   snake_case__							:					int    ):
  """simple docstring"""

  if len(snake_case__    ) < k or k < 0:
    raise ValueError("""Invalid Input"""    )
  _snake_case							:		Optional[int]								=  sum(array[:k]    )
  for i in range(len(snake_case__    ) - k    ):
    _snake_case							:		Optional[Any]								=  current_sum - array[i] + array[i + k]
    _snake_case							:		List[str]								=  max(snake_case__		,   snake_case__    )
  return max_sum


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

     testmod()
     A_						=				[randint(-10_00, 10_00) for i in range(1_00)]
     A_						=				randint(0, 1_10)
     print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


