





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					str						=			["""image_processor""", """tokenizer"""]
      _lowercase				:					List[str]						=			"""BridgeTowerImageProcessor"""
      _lowercase				:					Dict						=			("""RobertaTokenizer""", """RobertaTokenizerFast""")







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Optional[int]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      lowerCAmelCase__							)







      def __call__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = 0		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__		,      )   ->						BatchEncoding:



        '''simple docstring'''




        a__	:					int										=self.tokenizer(
            text=lowerCAmelCase__		,      add_special_tokens=lowerCAmelCase__		,      padding=lowerCAmelCase__		,      truncation=lowerCAmelCase__		,      max_length=lowerCAmelCase__		,      stride=lowerCAmelCase__		,      pad_to_multiple_of=lowerCAmelCase__		,      return_token_type_ids=lowerCAmelCase__		,      return_attention_mask=lowerCAmelCase__		,      return_overflowing_tokens=lowerCAmelCase__		,      return_special_tokens_mask=lowerCAmelCase__		,      return_offsets_mapping=lowerCAmelCase__		,      return_length=lowerCAmelCase__		,      verbose=lowerCAmelCase__		,      return_tensors=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        # add pixel_values + pixel_mask
        a__	:					str										=self.image_processor(
            lowerCAmelCase__		,      return_tensors=lowerCAmelCase__		,      do_normalize=lowerCAmelCase__		,      do_center_crop=lowerCAmelCase__		,      **lowerCAmelCase__							)
        encoding.update(lowerCAmelCase__							)

        return encoding







      def    _lowercase  (    self		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        return self.tokenizer.batch_decode(*lowerCAmelCase__		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        return self.tokenizer.decode(*lowerCAmelCase__		,      **lowerCAmelCase__							)







      @property
      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					str										=self.tokenizer.model_input_names
        a__	:					Union[str, Any]										=self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)













import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			"""M-CLIP"""







      def __init__(    self		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=7_6_8		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					int										=transformerDimSize
        a__	:					Dict										=imageDimSize
        super().__init__(**lowerCAmelCase__							)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			MCLIPConfig







      def __init__(    self		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Tuple										=XLMRobertaModel(lowerCAmelCase__							)
        a__	:					List[str]										=torch.nn.Linear(
            in_features=config.transformerDimensions		,      out_features=config.numDims							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




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






