


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 	UpperCAmelCase_					(					tf.keras.layers.Layer    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A = None						,				__A = None ):






     """simple docstring"""





     super().__init__()
     lowerCamelCase :					Any    =				pad_token_id
     lowerCamelCase :					Optional[Any]    =				max_length
     lowerCamelCase :					str    =				vocab
     lowerCamelCase :					int    =				merges
     lowerCamelCase :					str    =				BytePairTokenizer(__A						,				__A						,				sequence_length=__A )






   @classmethod
   def _snake_case    (		cls						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				[" ".join(__A ) for m in tokenizer.bpe_ranks.keys()]
     lowerCamelCase :					Optional[Any]    =				tokenizer.get_vocab()
     return cls(__A						,				__A						,				*__A						,				**__A )






   @classmethod
   def _snake_case    (		cls						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					int    =				GPTaTokenizer.from_pretrained(__A						,				*__A						,				**__A )
     return cls.from_tokenizer(__A						,				*__A						,				**__A )






   @classmethod
   def _snake_case    (		cls						,				__A ):






     """simple docstring"""





     return cls(**__A )






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A						,				__A = None ):






     """simple docstring"""





     lowerCamelCase :					int    =				self.tf_tokenizer(__A )
     lowerCamelCase :					Any    =				tf.ones_like(__A )

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

       if max_length is not None:
         lowerCamelCase    ,	lowerCamelCase :					Optional[Any]    =				pad_model_inputs(
             __A						,				max_seq_length=__A						,				pad_value=self.pad_token_id )

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







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





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



