



'''simple docstring'''


import copy
import os
from typing import Union

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


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      'blip_text_model'




 def __init__(   self							,  lowerCAmelCase_=3_0_5_2_4							,  lowerCAmelCase_=7_6_8							,  lowerCAmelCase_=7_6_8							,  lowerCAmelCase_=3_0_7_2							,  lowerCAmelCase_=7_6_8							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=8							,  lowerCAmelCase_=5_1_2							,  lowerCAmelCase_="gelu"							,  lowerCAmelCase_=1e-12							,  lowerCAmelCase_=0.0							,  lowerCAmelCase_=0.0							,  lowerCAmelCase_=0.0_2							,  lowerCAmelCase_=3_0_5_2_2							,  lowerCAmelCase_=2							,  lowerCAmelCase_=0							,  lowerCAmelCase_=1_0_2							,  lowerCAmelCase_=True							,  lowerCAmelCase_=True							,  **lowerCAmelCase_							,  ) ->      List[str]:



      """simple docstring"""







      super().__init__(
          pad_token_id=UpperCAmelCase__							,  bos_token_id=UpperCAmelCase__							,  eos_token_id=UpperCAmelCase__							,  sep_token_id=UpperCAmelCase__							,  **UpperCAmelCase__							,  )

      a_       		=vocab_size
      a_       		=hidden_size
      a_       		=encoder_hidden_size
      a_       		=intermediate_size
      a_       		=projection_dim
      a_       		=hidden_dropout_prob
      a_       		=num_hidden_layers
      a_       		=num_attention_heads
      a_       		=max_position_embeddings
      a_       		=layer_norm_eps
      a_       		=hidden_act
      a_       		=initializer_range
      a_       		=attention_probs_dropout_prob
      a_       		=is_decoder
      a_       		=use_cache




 @classmethod
 def       lowercase_  (   cls							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      int:



      """simple docstring"""







      cls._set_token_in_kwargs(UpperCAmelCase__)

      a_       		=cls.get_config_dict(UpperCAmelCase__							,  **UpperCAmelCase__)

      # get the text config dict if we are loading from BlipConfig
      if config_dict.get("model_type") == "blip":
           a_       		=config_dict['''text_config''']

      if "model_type" in config_dict and hasattr(cls							,  "model_type") and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
               f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

      return cls.from_dict(UpperCAmelCase__							,  **UpperCAmelCase__)







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Tuple  =      'blip_vision_model'




 def __init__(   self							,  lowerCAmelCase_=7_6_8							,  lowerCAmelCase_=3_0_7_2							,  lowerCAmelCase_=5_1_2							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=3_8_4							,  lowerCAmelCase_=1_6							,  lowerCAmelCase_="gelu"							,  lowerCAmelCase_=1e-5							,  lowerCAmelCase_=0.0							,  lowerCAmelCase_=1e-10							,  **lowerCAmelCase_							,  ) ->      Optional[Any]:



      """simple docstring"""







      super().__init__(**UpperCAmelCase__)

      a_       		=hidden_size
      a_       		=intermediate_size
      a_       		=projection_dim
      a_       		=num_hidden_layers
      a_       		=num_attention_heads
      a_       		=patch_size
      a_       		=image_size
      a_       		=initializer_range
      a_       		=attention_dropout
      a_       		=layer_norm_eps
      a_       		=hidden_act




 @classmethod
 def       lowercase_  (   cls							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







      cls._set_token_in_kwargs(UpperCAmelCase__)

      a_       		=cls.get_config_dict(UpperCAmelCase__							,  **UpperCAmelCase__)

      # get the vision config dict if we are loading from BlipConfig
      if config_dict.get("model_type") == "blip":
           a_       		=config_dict['''vision_config''']

      if "model_type" in config_dict and hasattr(cls							,  "model_type") and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
               f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

      return cls.from_dict(UpperCAmelCase__							,  **UpperCAmelCase__)







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: List[str]  =      'blip'
 __magic_name__					: Tuple  =      True




 def __init__(   self							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=5_1_2							,  lowerCAmelCase_=2.6_5_9_2							,  lowerCAmelCase_=2_5_6							,  **lowerCAmelCase_							,  ) ->      Optional[Any]:



      """simple docstring"""







      super().__init__(**UpperCAmelCase__)

      if text_config is None:
           a_       		={}
           logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values.")

      if vision_config is None:
           a_       		={}
           logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.")

      a_       		=BlipTextConfig(**UpperCAmelCase__)
      a_       		=BlipVisionConfig(**UpperCAmelCase__)

      a_       		=self.vision_config.hidden_size

      a_       		=projection_dim
      a_       		=logit_scale_init_value
      a_       		=1.0
      a_       		=0.0_2
      a_       		=image_text_hidden_size




 @classmethod
 def       lowercase_  (   cls							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      int:



      """simple docstring"""







      return cls(text_config=text_config.to_dict()							,  vision_config=vision_config.to_dict()							,  **UpperCAmelCase__)




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      a_       		=copy.deepcopy(self.__dict__)
      a_       		=self.text_config.to_dict()
      a_       		=self.vision_config.to_dict()
      a_       		=self.__class__.model_type
      return output











'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=5       ):


     '''simple docstring'''







     assert masked_input.count("<mask>"       ) == 1
     a_       		=torch.tensor(tokenizer.encode(lowercase__       ,	add_special_tokens=lowercase__       )       ).unsqueeze(0       )  # Batch size 1
     a_       		=model(lowercase__       )[0]  # The last hidden-state is the first element of the output tuple
     a_       		=(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     a_       		=logits[0, masked_index, :]
     a_       		=logits.softmax(dim=0       )
     a_		,						a_       		=prob.topk(k=lowercase__       ,	dim=0       )
     a_       		=" ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(lowercase__       )       )]       )
     a_       		=tokenizer.mask_token
     a_       		=[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
          a_       		=predicted_token_bpe.replace("\u2581"       ,	" "       )
          if " {0}".format(lowercase__       ) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(" {0}".format(lowercase__       )       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(lowercase__       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
     return topk_filled_outputs


lowercase							       =			CamembertTokenizer.from_pretrained('''camembert-base''')
lowercase							       =			CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

lowercase							       =			'''Le camembert est <mask> :)'''

print(fill_mask(masked_input, model, tokenizer, topk=3))


