


from typing import TYPE_CHECKING

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


_snake_case					=					{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GPTJForCausalLM''',
       '''GPTJForQuestionAnswering''',
       '''GPTJForSequenceClassification''',
       '''GPTJModel''',
       '''GPTJPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''TFGPTJForCausalLM''',
       '''TFGPTJForQuestionAnswering''',
       '''TFGPTJForSequenceClassification''',
       '''TFGPTJModel''',
       '''TFGPTJPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''FlaxGPTJForCausalLM''',
       '''FlaxGPTJModel''',
       '''FlaxGPTJPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gptj import (
          GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTJForCausalLM,
          GPTJForQuestionAnswering,
          GPTJForSequenceClassification,
          GPTJModel,
          GPTJPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_gptj import (
          TFGPTJForCausalLM,
          TFGPTJForQuestionAnswering,
          TFGPTJForSequenceClassification,
          TFGPTJModel,
          TFGPTJPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
   import sys

   _snake_case					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  sequence.append(SCREAMING_SNAKE_CASE_     )

  lowerCamelCase			: str   =    0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(SCREAMING_SNAKE_CASE_     ):
    return None

  return index


if __name__ == "__main__":
   _snake_case					=					input('''Enter numbers separated by comma:\n''').strip()
   _snake_case					=					[int(item) for item in user_input.split(''',''')]

   _snake_case					=					input('''Enter a single number to be found in the list:\n''')
   _snake_case					=					int(target_input)
   _snake_case					=					sentinel_linear_search(sequence, target)
   if result is not None:
      print(f'''{target} found at positions: {result}''')
   else:
      print('''Not found''')



