




"""simple docstring"""



import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''git_vision_model'''




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Optional[Any]=768     ,			snake_case_						:	Dict=3_072     ,			snake_case_						:	List[str]=12     ,			snake_case_						:	List[Any]=12     ,			snake_case_						:	Union[str, Any]=3     ,			snake_case_						:	Union[str, Any]=224     ,			snake_case_						:	Union[str, Any]=16     ,			snake_case_						:	Any="quick_gelu"     ,			snake_case_						:	Optional[Any]=1e-5     ,			snake_case_						:	List[str]=0.0     ,			snake_case_						:	List[Any]=0.02     ,			**snake_case_						:	int     ,			)				->							Union[str, Any]:




        '''simple docstring'''

        super().__init__(**snake_case_				)

        A__	=    hidden_size
        A__	=    intermediate_size
        A__	=    num_hidden_layers
        A__	=    num_attention_heads
        A__	=    num_channels
        A__	=    patch_size
        A__	=    image_size
        A__	=    initializer_range
        A__	=    attention_dropout
        A__	=    layer_norm_eps
        A__	=    hidden_act




    @classmethod
    def 							__magic_name__							(   cls						:	Dict     ,			snake_case_						:	Union[str, os.PathLike]     ,			**snake_case_						:	Dict				)				->							"PretrainedConfig":




        '''simple docstring'''

        cls._set_token_in_kwargs(snake_case_				)

        A__,				A__	=    cls.get_config_dict(snake_case_     ,			**snake_case_				)

        # get the vision config dict if we are loading from GITConfig
        if config_dict.get("model_type"				) == "git":
            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(snake_case_     ,			**snake_case_				)






class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''git'''




    def __init__(   self						:	Any     ,			snake_case_						:	List[str]=None     ,			snake_case_						:	Optional[int]=30_522     ,			snake_case_						:	Optional[int]=768     ,			snake_case_						:	Optional[Any]=6     ,			snake_case_						:	Optional[Any]=12     ,			snake_case_						:	Union[str, Any]=3_072     ,			snake_case_						:	Dict="gelu"     ,			snake_case_						:	int=0.1     ,			snake_case_						:	Any=0.1     ,			snake_case_						:	Dict=1_024     ,			snake_case_						:	Optional[Any]=0.02     ,			snake_case_						:	Dict=1e-12     ,			snake_case_						:	Optional[Any]=0     ,			snake_case_						:	List[Any]="absolute"     ,			snake_case_						:	int=True     ,			snake_case_						:	int=False     ,			snake_case_						:	int=101     ,			snake_case_						:	Union[str, Any]=102     ,			snake_case_						:	List[Any]=None     ,			**snake_case_						:	str     ,			)				->							Union[str, Any]:




        '''simple docstring'''

        super().__init__(bos_token_id=snake_case_     ,			eos_token_id=snake_case_     ,			pad_token_id=snake_case_     ,			**snake_case_				)

        if vision_config is None:
            A__	=    {}
            logger.info("vision_config is None. initializing the GitVisionConfig with default values."				)

        A__	=    GitVisionConfig(**snake_case_				)
        A__	=    vocab_size
        A__	=    hidden_size
        A__	=    num_hidden_layers
        A__	=    num_attention_heads
        A__	=    hidden_act
        A__	=    intermediate_size
        A__	=    hidden_dropout_prob
        A__	=    attention_probs_dropout_prob
        A__	=    max_position_embeddings
        A__	=    initializer_range
        A__	=    layer_norm_eps
        A__	=    position_embedding_type
        A__	=    use_cache
        A__	=    tie_word_embeddings
        A__	=    num_image_with_embedding

        A__	=    bos_token_id
        A__	=    eos_token_id




    def 							__magic_name__							(   self						:	Tuple				)				->							List[str]:




        '''simple docstring'''

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











"""simple docstring"""



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''ViTImageProcessor'''
    lowercase__						 =				('''CLIPTokenizer''', '''CLIPTokenizerFast''')




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Dict=None     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."     ,			snake_case_     ,			)
      A__	=    kwargs.pop("feature_extractor"				)

     A__	=    image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(snake_case_     ,			snake_case_				)




    def __call__(   self						:	int     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Dict=None     ,			snake_case_						:	int=None     ,			**snake_case_						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     if text is None and visual_prompt is None and images is None:
      raise ValueError("You have to specify either text, visual prompt or images."				)

     if text is not None and visual_prompt is not None:
      raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None and images is not None:
      A__	=    {
          "pixel_values": image_features.pixel_values,
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     elif text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     elif visual_prompt is not None:
      A__	=    {
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							str:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[Any]     ,			*snake_case_						:	Any     ,			**snake_case_						:	List[Any]				)				->							List[str]:




     '''simple docstring'''

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




    @property
    def 							__magic_name__							(   self						:	int				)				->							Optional[int]:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,			snake_case_     ,			)
     return self.image_processor_class




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,			snake_case_     ,			)
     return self.image_processor





