





import copy
import os
from typing import Union

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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'bridgetower_vision_model'

 def __init__(  self,		A_=768,		A_=12,		A_=3,		A_=16,		A_=288,		A_=1,		A_=1E-05,		A_=False,		A_=True,		A_=False,		**A_,		)	->       Optional[int]:
    super().__init__(**A_		)
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=initializer_factor
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=stop_gradient
    UpperCAmelCase__						=share_layernorm
    UpperCAmelCase__						=remove_last_layer






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       "PretrainedConfig":
    UpperCAmelCase__			,							UpperCAmelCase__						=cls.get_config_dict(A_,		**A_		)

    if config_dict.get("model_type"		) == "bridgetower":
       UpperCAmelCase__						=config_dict["text_config"]

    if "model_type" in config_dict and hasattr(cls,		"model_type"		) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
           f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

    return cls.from_dict(A_,		**A_		)

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'bridgetower_text_model'

 def __init__(  self,		A_=5_0265,		A_=768,		A_=12,		A_=12,		A_=1,		A_=3072,		A_="gelu",		A_=0.1,		A_=0.1,		A_=514,		A_=1,		A_=1E-05,		A_=1,		A_=0,		A_=2,		A_="absolute",		A_=True,		**A_,		)	->       List[Any]:
    super().__init__(**A_		)

    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=initializer_factor
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=max_position_embeddings
    UpperCAmelCase__						=type_vocab_size
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=position_embedding_type
    UpperCAmelCase__						=use_cache
    UpperCAmelCase__						=pad_token_id
    UpperCAmelCase__						=bos_token_id
    UpperCAmelCase__						=eos_token_id






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       "PretrainedConfig":
    UpperCAmelCase__			,							UpperCAmelCase__						=cls.get_config_dict(A_,		**A_		)

    if config_dict.get("model_type"		) == "bridgetower":
       UpperCAmelCase__						=config_dict["text_config"]

    if "model_type" in config_dict and hasattr(cls,		"model_type"		) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
           f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

    return cls.from_dict(A_,		**A_		)

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'bridgetower'

 def __init__(  self,		A_=True,		A_="gelu",		A_=768,		A_=1,		A_=1E-05,		A_=False,		A_="add",		A_=12,		A_=6,		A_=False,		A_=False,		A_=None,		A_=None,		**A_,		)	->       Any:
    # TODO: remove this once the Hub files are updated.
    UpperCAmelCase__						=kwargs.pop("text_config_dict",		A_		)
    UpperCAmelCase__						=kwargs.pop("vision_config_dict",		A_		)

    super().__init__(**A_		)
    UpperCAmelCase__						=share_cross_modal_transformer_layers
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=initializer_factor
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=share_link_tower_layers
    UpperCAmelCase__						=link_tower_type
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=tie_word_embeddings
    UpperCAmelCase__						=init_layernorm_from_vision_encoder

    if text_config is None:
       UpperCAmelCase__						={}
       logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."		)

    if vision_config is None:
       UpperCAmelCase__						={}
       logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."		)

    UpperCAmelCase__						=BridgeTowerTextConfig(**A_		)
    UpperCAmelCase__						=BridgeTowerVisionConfig(**A_		)

 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		A_,		**A_		)	->       Dict:
    return cls(text_config=text_config.to_dict(),		vision_config=vision_config.to_dict(),		**A_		)






 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=copy.deepcopy(self.__dict__		)
    UpperCAmelCase__						=self.text_config.to_dict()
    UpperCAmelCase__						=self.vision_config.to_dict()
    UpperCAmelCase__						=self.__class__.model_type
    return output










from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'roformer'

 def __init__(  self,		A_=5_0000,		A_=None,		A_=768,		A_=12,		A_=12,		A_=3072,		A_="gelu",		A_=0.1,		A_=0.1,		A_=1536,		A_=2,		A_=0.02,		A_=1E-12,		A_=0,		A_=False,		A_=True,		**A_,		)	->       Dict:
    super().__init__(pad_token_id=A_,		**A_		)

    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=hidden_size if embedding_size is None else embedding_size
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=max_position_embeddings
    UpperCAmelCase__						=type_vocab_size
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=rotary_value
    UpperCAmelCase__						=use_cache

class     snake_case_		(   a							):






 '''simple docstring'''





 @property
 def  __UpperCAmelCase						(  self		)	->       Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
       UpperCAmelCase__						={0: "batch", 1: "choice", 2: "sequence"}
    else:
       UpperCAmelCase__						={0: "batch", 1: "sequence"}
    UpperCAmelCase__						={0: "batch", 1: "sequence"}
    return OrderedDict(
        [
            ("input_ids", dynamic_axis),
            ("attention_mask", dynamic_axis),
            ("token_type_ids", dynamic_axis),
        ]		)



