

from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image






class 	UpperCAmelCase__		(	_A	):






  """simple docstring"""


  a									= "dandelin/vilt-b32-finetuned-vqa"
  a									= (
      "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
      "image containing the information, as well as a `question` which should be the question in English. It "
      "returns a text that is the answer to the question."
  )
  a									= "image_qa"
  a									= AutoProcessor
  a									= AutoModelForVisualQuestionAnswering

  a									= ["image", "text"]
  a									= ["text"]






  def __init__(							self						:		Union[str, Any]     ,       *__lowerCamelCase						:		Any     ,       **__lowerCamelCase						:		Union[str, Any] )		->							List[Any]:
       requires_backends(self     ,       ['''vision'''] )
       super().__init__(*__SCREAMING_SNAKE_CASE     ,       **__SCREAMING_SNAKE_CASE )






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		"Image"     ,       __lowerCamelCase						:		str )		->							Any:
       return self.pre_processor(__SCREAMING_SNAKE_CASE     ,       __SCREAMING_SNAKE_CASE     ,       return_tensors='''pt''' )






  def 				lowercase_		(							self						:		str     ,       __lowerCamelCase						:		Any )		->							Dict:
       with torch.no_grad():
            return self.model(**__SCREAMING_SNAKE_CASE ).logits







  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		Union[str, Any] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   outputs.argmax(-1 ).item()
       return self.model.config.idalabel[idx]





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


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      {
    '''google/vivit-b-16x2-kinetics400''': (
        '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "vivit"






  def __init__(							self						:		str     ,       __lowerCamelCase						:		List[Any]=224     ,       __lowerCamelCase						:		Optional[int]=32     ,       __lowerCamelCase						:		Tuple=[2, 16, 16]     ,       __lowerCamelCase						:		Union[str, Any]=3     ,       __lowerCamelCase						:		Optional[Any]=768     ,       __lowerCamelCase						:		Any=12     ,       __lowerCamelCase						:		Optional[Any]=12     ,       __lowerCamelCase						:		List[str]=3072     ,       __lowerCamelCase						:		Any="gelu_fast"     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		str=0.02     ,       __lowerCamelCase						:		Any=1e-06     ,       __lowerCamelCase						:		Dict=True     ,       **__lowerCamelCase						:		Any     ,       )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps

       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   num_frames
       SCREAMING_SNAKE_CASE__   =   tubelet_size
       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   qkv_bias

       super().__init__(**__lowerCamelCase )


