




'''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 		a_( lowerCamelCase      )				->List[str]:






       '''simple docstring'''




       if is_torch_available():
              import torch

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

       return isinstance(lowerCamelCase   ,				np.ndarray      )





class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      fields(self )

        # Safety and consistency checks
        if not len(A ):
               raise ValueError(f"""{self.__class__.__name__} has no fields.""" )

        lowercase  :		Dict						=      getattr(self   ,		class_fields[0].name )
        lowercase  :		Tuple						=      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():
                      lowercase  :		int						=      value
        else:
               for field in class_fields:
                      lowercase  :		List[str]						=      getattr(self   ,		field.name )
                      if v is not None:
                             lowercase  :		Optional[int]						=      v



 def __delitem__(  self   ,		*A   ,		**A ):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		*A   ,		**A ):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		*A   ,		**A ):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		*A   ,		**A ):






        '''simple docstring'''




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



 def __getitem__(  self   ,		A ):






        '''simple docstring'''




        if isinstance(A   ,		A ):
               lowercase  :		str						=      dict(self.items() )
               return inner_dict[k]
        else:
               return self.to_tuple()[k]



 def __setattr__(  self   ,		A   ,		A ):






        '''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   ,		A   ,		A ):






        '''simple docstring'''




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





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase	:      Optional[int]				=       {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      List[str]				=       [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

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




