



import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    str											=						'''M-CLIP'''



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any=1_024     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=768     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				transformerDimSize
           __a						       =				imageDimSize
           super().__init__(**__SCREAMING_SNAKE_CASE)


class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						MCLIPConfig



       def __init__(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		*__SCREAMING_SNAKE_CASE							:     int     ,		**__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           super().__init__(__SCREAMING_SNAKE_CASE     ,		*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				XLMRobertaModel(__SCREAMING_SNAKE_CASE)
           __a						       =				torch.nn.Linear(
               in_features=config.transformerDimensions     ,		out_features=config.numDims)





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           __a						       =				self.transformer(input_ids=__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE)[0]
           __a						       =				(embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
           return self.LinearTransformation(__SCREAMING_SNAKE_CASE), embs










import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case						:Any      		=     logging.getLogger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						'''masked_bert'''



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     int=30_522     ,		__SCREAMING_SNAKE_CASE							:     str=768     ,		__SCREAMING_SNAKE_CASE							:     List[str]=12     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=3_072     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1E-12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0     ,		__SCREAMING_SNAKE_CASE							:     List[str]="topK"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="constant"     ,		__SCREAMING_SNAKE_CASE							:     int=0.0     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				hidden_act
           __a						       =				intermediate_size
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				initializer_range
           __a						       =				layer_norm_eps
           __a						       =				pruning_method
           __a						       =				mask_init
           __a						       =				mask_scale





