




from typing import Mapping

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


_lowerCamelCase    				=		logging.get_logger(__name__)

_lowerCamelCase    				=		{
    '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     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : Tuple     	=						'longt5'
     lowerCAmelCase       : Optional[int]     	=						['past_key_values']
     lowerCAmelCase       : Optional[Any]     	=						{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
     def __init__(			self     :    Any      ,					__snake_case     :    Dict=3_21_28      ,					__snake_case     :    Dict=5_12      ,					__snake_case     :    Optional[Any]=64      ,					__snake_case     :    Dict=20_48      ,					__snake_case     :    int=6      ,					__snake_case     :    Dict=None      ,					__snake_case     :    List[Any]=8      ,					__snake_case     :    List[Any]=1_27      ,					__snake_case     :    List[str]=16      ,					__snake_case     :    Optional[int]=32      ,					__snake_case     :    Optional[Any]=1_28      ,					__snake_case     :    Optional[Any]=0.1      ,					__snake_case     :    Optional[int]=1E-6      ,					__snake_case     :    Optional[Any]=1.0      ,					__snake_case     :    Optional[int]="relu"      ,					__snake_case     :    List[Any]=True      ,					__snake_case     :    Union[str, Any]="local"      ,					__snake_case     :    int=True      ,					__snake_case     :    List[str]=0      ,					__snake_case     :    List[Any]=1      ,					**__snake_case     :    List[Any]      ,					):
         UpperCAmelCase_											=  vocab_size
         UpperCAmelCase_											=  d_model
         UpperCAmelCase_											=  d_kv
         UpperCAmelCase_											=  d_ff
         UpperCAmelCase_											=  num_layers
         # default = symmetry
         UpperCAmelCase_											=  num_decoder_layers if num_decoder_layers is not None else self.num_layers
         UpperCAmelCase_											=  num_heads
         UpperCAmelCase_											=  local_radius
         UpperCAmelCase_											=  global_block_size
         UpperCAmelCase_											=  relative_attention_num_buckets
         UpperCAmelCase_											=  relative_attention_max_distance
         UpperCAmelCase_											=  dropout_rate
         UpperCAmelCase_											=  layer_norm_epsilon
         UpperCAmelCase_											=  initializer_factor
         UpperCAmelCase_											=  feed_forward_proj
         UpperCAmelCase_											=  encoder_attention_type
         UpperCAmelCase_											=  use_cache

         UpperCAmelCase_											=  self.feed_forward_proj.split('''-'''       )
         UpperCAmelCase_											=  act_info[-1]
         UpperCAmelCase_											=  act_info[0] == '''gated'''

         if len(__snake_case       ) > 1 and act_info[0] != "gated" or len(__snake_case       ) > 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":
             UpperCAmelCase_											=  '''gelu_new'''

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



class     a  (     _A    ):

     '''simple docstring'''





     @property
     def 						lowerCamelCase_     (			self     :    List[str]       ):
         UpperCAmelCase_											=  {
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
             UpperCAmelCase_											=  '''past_encoder_sequence + sequence'''
             UpperCAmelCase_											=  {0: '''batch'''}
             UpperCAmelCase_											=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
             UpperCAmelCase_											=  {0: '''batch''', 1: '''decoder_sequence'''}
             UpperCAmelCase_											=  {0: '''batch''', 1: '''decoder_sequence'''}

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

         return common_inputs




     @property
     def 						lowerCamelCase_     (			self     :    str       ):
         return 13











import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_lowerCamelCase    				=		logging.get_logger(__name__)

_lowerCamelCase    				=		{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : Any     	=						'vit-hybrid'
     def __init__(			self     :    str      ,					__snake_case     :    str=None      ,					__snake_case     :    List[Any]=7_68      ,					__snake_case     :    List[Any]=12      ,					__snake_case     :    Dict=12      ,					__snake_case     :    Optional[int]=30_72      ,					__snake_case     :    Optional[int]="gelu"      ,					__snake_case     :    Union[str, Any]=0.0      ,					__snake_case     :    Any=0.0      ,					__snake_case     :    List[Any]=0.02      ,					__snake_case     :    Union[str, Any]=1E-12      ,					__snake_case     :    Optional[int]=2_24      ,					__snake_case     :    Optional[int]=1      ,					__snake_case     :    Tuple=3      ,					__snake_case     :    Union[str, Any]=[1, 10_24, 24, 24]      ,					__snake_case     :    List[Any]=True      ,					**__snake_case     :    Tuple      ,					):
         super().__init__(**__snake_case       )

         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''       )
             UpperCAmelCase_											=  {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }

         if isinstance(__snake_case      ,					__snake_case       ):
             if "model_type" in backbone_config:
                 UpperCAmelCase_											=  CONFIG_MAPPING[backbone_config['''model_type''']]
             else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''       )
                 UpperCAmelCase_											=  BitConfig
             UpperCAmelCase_											=  backbone_config_class(**__snake_case       )

         UpperCAmelCase_											=  backbone_featmap_shape
         UpperCAmelCase_											=  backbone_config
         UpperCAmelCase_											=  hidden_size
         UpperCAmelCase_											=  num_hidden_layers
         UpperCAmelCase_											=  num_attention_heads
         UpperCAmelCase_											=  intermediate_size
         UpperCAmelCase_											=  hidden_act
         UpperCAmelCase_											=  hidden_dropout_prob
         UpperCAmelCase_											=  attention_probs_dropout_prob
         UpperCAmelCase_											=  initializer_range
         UpperCAmelCase_											=  layer_norm_eps
         UpperCAmelCase_											=  image_size
         UpperCAmelCase_											=  patch_size
         UpperCAmelCase_											=  num_channels
         UpperCAmelCase_											=  qkv_bias




     def 						lowerCamelCase_     (			self     :    Any       ):
         UpperCAmelCase_											=  copy.deepcopy(self.__dict__       )
         UpperCAmelCase_											=  self.backbone_config.to_dict()
         UpperCAmelCase_											=  self.__class__.model_type
         return output





