'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


_lowerCamelCase :				str														= logging.get_logger(__name__)




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""vision-text-dual-encoder"""
   __UpperCAmelCase           =						True



   def __init__(    self					,      lowerCAmelCase__=512					,      lowerCAmelCase__=2.6592					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





        super().__init__(**lowerCAmelCase__     )

        if "vision_config" not in kwargs:
             raise ValueError("""`vision_config` can not be `None`."""     )

        if "text_config" not in kwargs:
             raise ValueError("""`text_config` can not be `None`."""     )

        _UpperCamelCase  :Optional[int]       =kwargs.pop("""vision_config"""     )
        _UpperCamelCase  :List[Any]       =kwargs.pop("""text_config"""     )

        _UpperCamelCase  :Union[str, Any]       =vision_config.pop("""model_type"""     )
        _UpperCamelCase  :Tuple       =text_config.pop("""model_type"""     )

        if vision_model_type == "clip":
             _UpperCamelCase  :str       =AutoConfig.for_model(lowerCAmelCase__					,      **lowerCAmelCase__     ).vision_config
        elif vision_model_type == "clip_vision_model":
             _UpperCamelCase  :Union[str, Any]       =CLIPVisionConfig(**lowerCAmelCase__     )
        else:
             _UpperCamelCase  :List[str]       =AutoConfig.for_model(lowerCAmelCase__					,      **lowerCAmelCase__     )

        _UpperCamelCase  :int       =AutoConfig.for_model(lowerCAmelCase__					,      **lowerCAmelCase__     )

        _UpperCamelCase  :Any       =projection_dim
        _UpperCamelCase  :Tuple       =logit_scale_init_value



   @classmethod
   def  _UpperCamelCase		(    cls					,      lowerCAmelCase__					,      lowerCAmelCase__					,      **lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        return cls(vision_config=vision_config.to_dict()					,      text_config=text_config.to_dict()					,      **lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =copy.deepcopy(self.__dict__     )
        _UpperCamelCase  :List[Any]       =self.vision_config.to_dict()
        _UpperCamelCase  :Dict       =self.text_config.to_dict()
        _UpperCamelCase  :Any       =self.__class__.model_type
        return output

from typing import Mapping

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


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

lowerCamelCase_				:							Optional[Any]       =    {
    "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  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""longt5"""
     lowercase__				  =			["""past_key_values"""]
     lowercase__				  =			{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=32_128							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=64							,       SCREAMING_SNAKE_CASE_=2_048							,       SCREAMING_SNAKE_CASE_=6							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=8							,       SCREAMING_SNAKE_CASE_=127							,       SCREAMING_SNAKE_CASE_=16							,       SCREAMING_SNAKE_CASE_=32							,       SCREAMING_SNAKE_CASE_=128							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=1E-6							,       SCREAMING_SNAKE_CASE_=1.0							,       SCREAMING_SNAKE_CASE_="relu"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_="local"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=1							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   vocab_size
            __snake_case						       =   d_model
            __snake_case						       =   d_kv
            __snake_case						       =   d_ff
            __snake_case						       =   num_layers
            # default = symmetry
            __snake_case						       =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
            __snake_case						       =   num_heads
            __snake_case						       =   local_radius
            __snake_case						       =   global_block_size
            __snake_case						       =   relative_attention_num_buckets
            __snake_case						       =   relative_attention_max_distance
            __snake_case						       =   dropout_rate
            __snake_case						       =   layer_norm_epsilon
            __snake_case						       =   initializer_factor
            __snake_case						       =   feed_forward_proj
            __snake_case						       =   encoder_attention_type
            __snake_case						       =   use_cache

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

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

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







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
            }
            if self.use_past:
                   __snake_case						       =   'past_encoder_sequence + sequence'
                   __snake_case						       =   {0: 'batch'}
                   __snake_case						       =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}

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

            return common_inputs

     @property
     def 						__lowerCamelCase				(  self    ):
            return 13
