'''simple docstring'''





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


UpperCamelCase       :     Union[str, Any]			=    logging.get_logger(__name__)

UpperCamelCase       :     Dict			=    {
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    List[str]				=			"regnet"
    A     :    Dict				=			["x", "y"]





    def __init__(					self     :						str							,							UpperCAmelCase_     :						Optional[Any]=3							,							UpperCAmelCase_     :						Any=3_2							,							UpperCAmelCase_     :						int=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]							,							UpperCAmelCase_     :						Optional[int]=[2, 6, 1_2, 2]							,							UpperCAmelCase_     :						Tuple=6_4							,							UpperCAmelCase_     :						Tuple="y"							,							UpperCAmelCase_     :						Dict="relu"							,							**UpperCAmelCase_     :						List[Any]							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types)}""")
         a					:							Dict							= num_channels
         a					:							List[str]							= embedding_size
         a					:							Dict							= hidden_sizes
         a					:							str							= depths
         a					:							str							= groups_width
         a					:							Union[str, Any]							= layer_type
         a					:							Dict							= hidden_act
         # always downsample in the first stage
         a					:							Dict							= True


'''simple docstring'''





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase       :     Any			=    logging.get_logger(__name__)

UpperCamelCase       :     Tuple			=    {
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    Union[str, Any]				=			"longt5"
    A     :    List[str]				=			["past_key_values"]
    A     :    Dict				=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





    def __init__(					self     :						int							,							UpperCAmelCase_     :						Union[str, Any]=3_2_1_2_8							,							UpperCAmelCase_     :						List[Any]=5_1_2							,							UpperCAmelCase_     :						List[Any]=6_4							,							UpperCAmelCase_     :						List[str]=2_0_4_8							,							UpperCAmelCase_     :						List[str]=6							,							UpperCAmelCase_     :						Tuple=None							,							UpperCAmelCase_     :						Tuple=8							,							UpperCAmelCase_     :						str=1_2_7							,							UpperCAmelCase_     :						Dict=1_6							,							UpperCAmelCase_     :						Optional[int]=3_2							,							UpperCAmelCase_     :						Union[str, Any]=1_2_8							,							UpperCAmelCase_     :						str=0.1							,							UpperCAmelCase_     :						Optional[int]=1e-6							,							UpperCAmelCase_     :						Tuple=1.0							,							UpperCAmelCase_     :						str="relu"							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Dict="local"							,							UpperCAmelCase_     :						Optional[int]=True							,							UpperCAmelCase_     :						Dict=0							,							UpperCAmelCase_     :						Union[str, Any]=1							,							**UpperCAmelCase_     :						Any							,							):


         """simple docstring"""







         a					:							Optional[int]							= vocab_size
         a					:							Tuple							= d_model
         a					:							str							= d_kv
         a					:							str							= d_ff
         a					:							Union[str, Any]							= num_layers
         # default = symmetry
         a					:							Optional[Any]							= num_decoder_layers if num_decoder_layers is not None else self.num_layers
         a					:							List[Any]							= num_heads
         a					:							List[str]							= local_radius
         a					:							Optional[Any]							= global_block_size
         a					:							Optional[int]							= relative_attention_num_buckets
         a					:							List[Any]							= relative_attention_max_distance
         a					:							int							= dropout_rate
         a					:							List[str]							= layer_norm_epsilon
         a					:							Optional[Any]							= initializer_factor
         a					:							List[Any]							= feed_forward_proj
         a					:							Dict							= encoder_attention_type
         a					:							int							= use_cache

         a					:							Optional[Any]							= self.feed_forward_proj.split('-')
         a					:							List[Any]							= act_info[-1]
         a					:							str							= act_info[0] == 'gated'

         if len(UpperCAmelCase_) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                  '\'gated-gelu\' or \'relu\'')

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              a					:							Any							= 'gelu_new'

         super().__init__(
             pad_token_id=UpperCAmelCase_							,							eos_token_id=UpperCAmelCase_							,							is_encoder_decoder=UpperCAmelCase_							,							**UpperCAmelCase_							,							)






class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         a					:							List[Any]							= {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
              a					:							List[str]							= 'past_encoder_sequence + sequence'
              a					:							str							= {0: 'batch'}
              a					:							List[Any]							= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
              a					:							Optional[Any]							= {0: 'batch', 1: 'decoder_sequence'}
              a					:							str							= {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
              self.fill_with_past_key_values_(UpperCAmelCase_							,							direction='inputs')

         return common_inputs







    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         return 1_3

