

import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'M-CLIP'


    def __init__(      self :       Optional[int],lowercase_ :       Union[str, Any]=1_0_2_4,lowercase_ :       str=7_6_8,**lowercase_ :       List[str]		)->    Optional[int]:





        '''simple docstring'''







        A__          =   transformerDimSize
        A__          =   imageDimSize
        super().__init__(**lowercase_		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							MCLIPConfig


    def __init__(      self :       List[str],lowercase_ :       Dict,*lowercase_ :       Optional[int],**lowercase_ :       str		)->    int:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       int,lowercase_ :       int,lowercase_ :       List[Any]		)->    Tuple:





        '''simple docstring'''







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




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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'open-llama'


    def __init__(      self :       Any,lowercase_ :       Optional[int]=1_0_0_0_0_0,lowercase_ :       Union[str, Any]=4_0_9_6,lowercase_ :       Dict=1_1_0_0_8,lowercase_ :       Dict=3_2,lowercase_ :       Optional[int]=3_2,lowercase_ :       Dict="silu",lowercase_ :       Union[str, Any]=2_0_4_8,lowercase_ :       Optional[int]=0.02,lowercase_ :       Dict=1E-6,lowercase_ :       Dict=True,lowercase_ :       List[Any]=0,lowercase_ :       Optional[int]=1,lowercase_ :       str=2,lowercase_ :       str=False,lowercase_ :       str=True,lowercase_ :       int=0.1,lowercase_ :       List[Any]=0.1,lowercase_ :       List[Any]=True,lowercase_ :       Union[str, Any]=True,lowercase_ :       Any=None,**lowercase_ :       List[Any],)->    Tuple:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   max_position_embeddings
        A__          =   hidden_size
        A__          =   intermediate_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   hidden_act
        A__          =   initializer_range
        A__          =   rms_norm_eps
        A__          =   use_cache
        A__          =   kwargs.pop(
            'use_memorry_efficient_attention',lowercase_		)
        A__          =   hidden_dropout_prob
        A__          =   attention_dropout_prob
        A__          =   use_stable_embedding
        A__          =   shared_input_output_embedding
        A__          =   rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,tie_word_embeddings=lowercase_,**lowercase_,)


    def 						snake_case__     (      self :       str		)->    str:





        '''simple docstring'''







        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling,lowercase_		) or len(self.rope_scaling		) != 2:
            raise ValueError(
                '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                F'got {self.rope_scaling}'		)
        A__          =   self.rope_scaling.get('type',lowercase_		)
        A__          =   self.rope_scaling.get('factor',lowercase_		)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
            raise ValueError(
                F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
        if rope_scaling_factor is None or not isinstance(lowercase_,lowercase_		) or rope_scaling_factor <= 1.0:
            raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)

