


"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Any     =	"""swin2sr"""

    UpperCAmelCase	:							Optional[int]     =	{
        """hidden_size""": """embed_dim""",
        """num_attention_heads""": """num_heads""",
        """num_hidden_layers""": """num_layers""",
    }






    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   Union[str, Any]=64    ,			__UpperCAmelCase    :   Any=1    ,			__UpperCAmelCase    :   List[Any]=3    ,			__UpperCAmelCase    :   str=180    ,			__UpperCAmelCase    :   Optional[int]=[6, 6, 6, 6, 6, 6]    ,			__UpperCAmelCase    :   Any=[6, 6, 6, 6, 6, 6]    ,			__UpperCAmelCase    :   Optional[Any]=8    ,			__UpperCAmelCase    :   Optional[int]=2.0    ,			__UpperCAmelCase    :   int=True    ,			__UpperCAmelCase    :   List[str]=0.0    ,			__UpperCAmelCase    :   Tuple=0.0    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   Union[str, Any]="gelu"    ,			__UpperCAmelCase    :   List[Any]=False    ,			__UpperCAmelCase    :   Any=0.02    ,			__UpperCAmelCase    :   Tuple=1e-5    ,			__UpperCAmelCase    :   Optional[Any]=2    ,			__UpperCAmelCase    :   Optional[Any]=1.0    ,			__UpperCAmelCase    :   Optional[int]="1conv"    ,			__UpperCAmelCase    :   Optional[int]="pixelshuffle"    ,			**__UpperCAmelCase    :   Optional[int]    ,			):
      super().__init__(**__UpperCAmelCase)

      a						:		int											=      image_size
      a						:		List[str]											=      patch_size
      a						:		str											=      num_channels
      a						:		List[str]											=      embed_dim
      a						:		Tuple											=      depths
      a						:		List[Any]											=      len(__UpperCAmelCase)
      a						:		Any											=      num_heads
      a						:		List[str]											=      window_size
      a						:		Tuple											=      mlp_ratio
      a						:		Optional[int]											=      qkv_bias
      a						:		List[Any]											=      hidden_dropout_prob
      a						:		List[str]											=      attention_probs_dropout_prob
      a						:		Union[str, Any]											=      drop_path_rate
      a						:		int											=      hidden_act
      a						:		int											=      use_absolute_embeddings
      a						:		Tuple											=      layer_norm_eps
      a						:		Tuple											=      initializer_range
      a						:		Any											=      upscale
      a						:		Tuple											=      img_range
      a						:		Any											=      resi_connection
      a						:		Tuple											=      upsampler










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Union[str, Any]     =	"""gpt_neox"""






    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Tuple=50432    ,			__UpperCAmelCase    :   str=6144    ,			__UpperCAmelCase    :   Any=44    ,			__UpperCAmelCase    :   Union[str, Any]=64    ,			__UpperCAmelCase    :   Dict=24576    ,			__UpperCAmelCase    :   Any="gelu"    ,			__UpperCAmelCase    :   List[Any]=0.25    ,			__UpperCAmelCase    :   Optional[Any]=10000    ,			__UpperCAmelCase    :   Tuple=0.0    ,			__UpperCAmelCase    :   Union[str, Any]=0.0    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[Any]=2048    ,			__UpperCAmelCase    :   Optional[Any]=0.02    ,			__UpperCAmelCase    :   Optional[Any]=1e-5    ,			__UpperCAmelCase    :   Dict=True    ,			__UpperCAmelCase    :   Union[str, Any]=0    ,			__UpperCAmelCase    :   Union[str, Any]=2    ,			__UpperCAmelCase    :   Any=False    ,			__UpperCAmelCase    :   List[Any]=True    ,			__UpperCAmelCase    :   int=None    ,			**__UpperCAmelCase    :   Tuple    ,			):
      super().__init__(bos_token_id=__UpperCAmelCase    ,			eos_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)
      a						:		List[Any]											=      vocab_size
      a						:		Optional[int]											=      max_position_embeddings
      a						:		List[Any]											=      hidden_size
      a						:		Union[str, Any]											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Optional[Any]											=      hidden_act
      a						:		Dict											=      rotary_pct
      a						:		Any											=      rotary_emb_base
      a						:		Dict											=      attention_dropout
      a						:		List[str]											=      hidden_dropout
      a						:		List[str]											=      classifier_dropout
      a						:		Any											=      initializer_range
      a						:		Union[str, Any]											=      layer_norm_eps
      a						:		int											=      use_cache
      a						:		int											=      tie_word_embeddings
      a						:		str											=      use_parallel_residual
      a						:		Union[str, Any]											=      rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            "The hidden size is not divisble by the number of attention heads! Make sure to update them!")






    def      __snake_case							( self    :   Any):
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,			__UpperCAmelCase) or len(self.rope_scaling) != 2:
        raise ValueError(
            "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
            f'''got {self.rope_scaling}''')
      a						:		str											=      self.rope_scaling.get("type"    ,			__UpperCAmelCase)
      a						:		List[str]											=      self.rope_scaling.get("factor"    ,			__UpperCAmelCase)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''')
      if rope_scaling_factor is None or not isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) or rope_scaling_factor <= 1.0:
        raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''')






