
'''simple docstring'''





from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available





def 							snake_case				(		UpperCAmelCase						)->      str:







      """simple docstring"""
      if is_torch_available():
            import torch

            if isinstance(UpperCAmelCase   ,	torch.Tensor						):
                  return True

      return isinstance(UpperCAmelCase   ,	np.ndarray						)


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 def 						lowercase_    (      self	:Optional[Any]      )					->      Tuple:







       '''simple docstring'''



       __A													=      fields(self      )

       # Safety and consistency checks
       if not len(_A      ):
             raise ValueError(F'{self.__class__.__name__} has no fields.'      )

       __A													=      getattr(self						,					class_fields[0].name      )
       __A													=      all(getattr(self						,					field.name      ) is None for field in class_fields[1:]      )

       if other_fields_are_none and isinstance(_A						,					_A      ):
             for key, value in first_field.items():
                   __A													=      value
       else:
             for field in class_fields:
                   __A													=      getattr(self						,					field.name      )
                   if v is not None:
                         __A													=      v






 def __delitem__(      self	:Optional[Any]						,					*_A	:Tuple						,					**_A	:Optional[Any]      )					->      str:







       '''simple docstring'''



       raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'      )






 def 						lowercase_    (      self	:Any						,					*_A	:Tuple						,					**_A	:Any      )					->      List[Any]:







       '''simple docstring'''



       raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'      )






 def 						lowercase_    (      self	:List[Any]						,					*_A	:Tuple						,					**_A	:List[Any]      )					->      int:







       '''simple docstring'''



       raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.'      )






 def 						lowercase_    (      self	:List[str]						,					*_A	:Union[str, Any]						,					**_A	:Union[str, Any]      )					->      Any:







       '''simple docstring'''



       raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.'      )






 def __getitem__(      self	:Optional[Any]						,					_A	:Union[str, Any]      )					->      Dict:







       '''simple docstring'''



       if isinstance(_A						,					_A      ):
             __A													=      dict(self.items()      )
             return inner_dict[k]
       else:
             return self.to_tuple()[k]






 def __setattr__(      self	:str						,					_A	:Any						,					_A	:str      )					->      Union[str, Any]:







       '''simple docstring'''



       if name in self.keys() and value is not None:
             # Don't call self.__setitem__ to avoid recursion errors
             super().__setitem__(_A						,					_A      )
       super().__setattr__(_A						,					_A      )






 def __setitem__(      self	:List[str]						,					_A	:Tuple						,					_A	:List[Any]      )					->      int:







       '''simple docstring'''



       super().__setitem__(_A						,					_A      )
       # Don't call self.__setattr__ to avoid recursion errors
       super().__setattr__(_A						,					_A      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      Tuple[Any]:







       '''simple docstring'''



       return tuple(self[k] for k in self.keys()      )







"""simple docstring"""







from typing import TYPE_CHECKING

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


_a	:	Optional[Any]=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

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

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Optional[Any]=					[
	    "TFGPTJForCausalLM",
	    "TFGPTJForQuestionAnswering",
	    "TFGPTJForSequenceClassification",
	    "TFGPTJModel",
	    "TFGPTJPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	List[str]=					[
	    "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

	_a	:	Any=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





