




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "M-CLIP"



 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[str]=1_024							,							_lowerCAmelCase    :						str=768							,							**_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     transformerDimSize
       SCREAMING_SNAKE_CASE_      		=     imageDimSize
       super().__init__(**_lowerCAmelCase		)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       MCLIPConfig



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]							,							*_lowerCAmelCase    :						str							,							**_lowerCAmelCase    :						str		):
       super().__init__(_lowerCAmelCase							,							*_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaModel(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Linear(
           in_features=config.transformerDimensions							,							out_features=config.numDims		)




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.transformer(input_ids=_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_      		=     (embs * attention_mask.unsqueeze(2		)).sum(dim=1		) / attention_mask.sum(dim=1		)[:, None]
       return self.LinearTransformation(_lowerCAmelCase		), embs





'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class      SCREAMING_SNAKE_CASE    (						tf.keras.layers.Layer				):






      def __init__( self   :  List[Any]						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  List[str]						,						lowercase__   :  int = None						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           super().__init__()
           a_     :							Optional[Any]     						=  pad_token_id
           a_     :							str     						=  max_length
           a_     :							List[str]     						=  vocab
           a_     :							Union[str, Any]     						=  merges
           a_     :							Union[str, Any]     						=  BytePairTokenizer(lowercase__						,						lowercase__						,						sequence_length=lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  List[Any]						,						lowercase__   :  GPTaTokenizer						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  [""" """.join(lowercase__       ) for m in tokenizer.bpe_ranks.keys()]
           a_     :							Any     						=  tokenizer.get_vocab()
           return cls(lowercase__						,						lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Union[str, os.PathLike]						,						*lowercase__   :  str						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  GPTaTokenizer.from_pretrained(lowercase__						,						*lowercase__						,						**lowercase__       )
           return cls.from_tokenizer(lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return cls(**lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tf_tokenizer(lowercase__       )
           a_     :							int     						=  tf.ones_like(lowercase__       )

           if self.pad_token_id is not None:
                # pad the tokens up to max length
                a_     :							int     						=  max_length if max_length is not None else self.max_length

                if max_length is not None:
                     a_						,     a_     :							int     						=  pad_model_inputs(
                         lowercase__						,						max_seq_length=lowercase__						,						pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}
