code: 
  
  # Lint as: python3
  
  import hashlib
  from typing import Union
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> bytes:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if isinstance(_UpperCAmelCase,      _UpperCAmelCase				):
          # Data already in bytes, returns as it as
          return hash_data
      elif isinstance(_UpperCAmelCase,      _UpperCAmelCase				):
          # We keep the data as it as for it ot be later encoded to UTF-8
          # However replace `\\` with `/` for Windows compatibility
          lowerCAmelCase				:			Optional[Any]    			=      hash_data.replace('\\',      '/'				)
      elif isinstance(_UpperCAmelCase,      _UpperCAmelCase				):
          lowerCAmelCase				:			Optional[Any]    			=      str(_UpperCAmelCase				)
      else:
          # If data is not of the required type, raise error
          raise InvalidKeyError(_UpperCAmelCase				)
  
      return hash_data.encode('utf-8'				)
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def __init__(	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[Any]    ):
          lowerCAmelCase				:			Optional[int]    			=      '\nFAILURE TO GENERATE DATASET: Invalid key type detected'
          lowerCAmelCase				:			Optional[int]    			=      f"\nFound Key {hash_data} of type {type(UpperCAmelCase_    )}"
          lowerCAmelCase				:			Dict    			=      '\nKeys should be either str, int or bytes type'
          super().__init__(f"{self.prefix}{self.err_msg}{self.suffix}"    )
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def __init__(	self			:    Optional[int]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    Union[str, Any]=""    ):
          lowerCAmelCase				:			int    			=      key
          lowerCAmelCase				:			List[Any]    			=      duplicate_key_indices
          lowerCAmelCase				:			Optional[Any]    			=      fix_msg
          lowerCAmelCase				:			List[str]    			=      'Found multiple examples generated with the same key'
          if len(UpperCAmelCase_    ) <= 20:
              lowerCAmelCase				:			List[str]    			=      f"\nThe examples at index {', '.join(UpperCAmelCase_    )} have the key {key}"
          else:
              lowerCAmelCase				:			int    			=      f"\nThe examples at index {', '.join(duplicate_key_indices[:20]    )}... ({len(UpperCAmelCase_    ) - 20} more) have the key {key}"
          lowerCAmelCase				:			str    			=      '\n' + fix_msg if fix_msg else ''
          super().__init__(f"{self.prefix}{self.err_msg}{self.suffix}"    )
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    Optional[Any]    ,				UpperCAmelCase_			:    str    ):
          lowerCAmelCase				:			Union[str, Any]    			=      hashlib.mda(_as_bytes(UpperCAmelCase_    )    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Union[str, int, bytes]    ):
          lowerCAmelCase				:			Any    			=      self._split_mda.copy()
          lowerCAmelCase				:			str    			=      _as_bytes(UpperCAmelCase_    )
          mda.update(UpperCAmelCase_    )
          # Convert to integer with hexadecimal conversion
          return int(mda.hexdigest()    ,				16    )
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __A   :     List[Any]      =			{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Tuple      =			[
       '''UMT5EncoderModel''',
       '''UMT5ForConditionalGeneration''',
       '''UMT5ForQuestionAnswering''',
       '''UMT5Model''',
       '''UMT5PreTrainedModel''',
   ]
  
  if TYPE_CHECKING:
   from .configuration_umta import UMTaConfig, UMTaOnnxConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_umta import (
        UMTaEncoderModel,
        UMTaForConditionalGeneration,
        UMTaForQuestionAnswering,
        UMTaModel,
        UMTaPreTrainedModel,
    )
  else:
   import sys
  
   __A   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  