


from collections import OrderedDict
from typing import Mapping

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


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

a					:				int       =      {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """plbart"""
       _UpperCamelCase     		=   ["""past_key_values"""]
       _UpperCamelCase     		=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

       def __init__(      self						,    a__=5_00_05						,    a__=10_24						,    a__=6						,    a__=30_72						,    a__=12						,    a__=6						,    a__=30_72						,    a__=12						,    a__=0.0						,    a__=0.0						,    a__=True						,    a__=True						,    a__="gelu"						,    a__=7_68						,    a__=0.1						,    a__=0.1						,    a__=0.0						,    a__=0.02						,    a__=0.0						,    a__=True						,    a__=1						,    a__=0						,    a__=2						,    a__=2						,    **a__						,    ):






        '''simple docstring'''






        snake_case :			int							=   vocab_size
        snake_case :			List[str]							=   max_position_embeddings
        snake_case :			str							=   d_model
        snake_case :			Union[str, Any]							=   encoder_ffn_dim
        snake_case :			Tuple							=   encoder_layers
        snake_case :			List[str]							=   encoder_attention_heads
        snake_case :			Tuple							=   decoder_ffn_dim
        snake_case :			Tuple							=   decoder_layers
        snake_case :			Dict							=   decoder_attention_heads
        snake_case :			Tuple							=   dropout
        snake_case :			Tuple							=   attention_dropout
        snake_case :			int							=   activation_dropout
        snake_case :			List[Any]							=   activation_function
        snake_case :			Union[str, Any]							=   init_std
        snake_case :			Any							=   encoder_layerdrop
        snake_case :			Optional[int]							=   decoder_layerdrop
        snake_case :			List[Any]							=   classifier_dropout
        snake_case :			str							=   use_cache
        snake_case :			List[str]							=   encoder_layers
        snake_case :			Optional[int]							=   scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=a__						,    bos_token_id=a__						,    eos_token_id=a__						,    is_encoder_decoder=a__						,    forced_eos_token_id=a__						,    **a__						,    )







class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
            ]       )



       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        if self.use_past:
         return OrderedDict(
             [
                 ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                 ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
                 ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
             ]       )
        else:
         return OrderedDict(
             [
                 ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
                 ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
             ]       )










import copy

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


a					:				Optional[Any]       =      logging.get_logger(__name__)

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hybrid-clip"""
       _UpperCamelCase     		=   True

       def __init__(      self						,    a__=5_12						,    **a__       ):






        '''simple docstring'''






        super().__init__(**a__       )

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

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

        snake_case :			int							=   kwargs.pop('''text_config'''       )
        snake_case :			Optional[int]							=   kwargs.pop('''vision_config'''       )

        snake_case :			Optional[Any]							=   text_config.pop('''model_type'''       )
        snake_case :			str							=   vision_config.pop('''model_type'''       )

        from transformers import AutoConfig

        snake_case :			List[Any]							=   AutoConfig.for_model(a__						,    **a__       )

        if vision_model_type == "clip":
         snake_case :			Dict							=   AutoConfig.for_model(a__						,    **a__       ).vision_config
        elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         snake_case :			str							=   CLIPVisionConfig(**a__       )
        else:
         snake_case :			Any							=   AutoConfig.for_model(a__						,    **a__       )

        snake_case :			Optional[Any]							=   projection_dim
        snake_case :			int							=   1.0

       @classmethod
       def 					UpperCAmelCase					(      cls						,    a__						,    a__						,    **a__       ):






        '''simple docstring'''






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



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   copy.deepcopy(self.__dict__       )
        snake_case :			Dict							=   self.text_config.to_dict()
        snake_case :			int							=   self.vision_config.to_dict()
        snake_case :			List[Any]							=   self.__class__.model_type
        return output






