



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_      :      Any    =     logging.get_logger(__name__)

a_      :      Optional[int]    =     {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Any      						=					'''beit'''

 def __init__(	self		,				SCREAMING_SNAKE_CASE=8_1_9_2		,				SCREAMING_SNAKE_CASE=7_6_8		,				SCREAMING_SNAKE_CASE=1_2		,				SCREAMING_SNAKE_CASE=1_2		,				SCREAMING_SNAKE_CASE=3_0_7_2		,				SCREAMING_SNAKE_CASE="gelu"		,				SCREAMING_SNAKE_CASE=0.0		,				SCREAMING_SNAKE_CASE=0.0		,				SCREAMING_SNAKE_CASE=0.02		,				SCREAMING_SNAKE_CASE=1e-1_2		,				SCREAMING_SNAKE_CASE=2_2_4		,				SCREAMING_SNAKE_CASE=1_6		,				SCREAMING_SNAKE_CASE=3		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=[3, 5, 7, 1_1]		,				SCREAMING_SNAKE_CASE=[1, 2, 3, 6]		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=0.4		,				SCREAMING_SNAKE_CASE=2_5_6		,				SCREAMING_SNAKE_CASE=1		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=2_5_5		,				**SCREAMING_SNAKE_CASE		,				) ->			int:
  super().__init__(**SCREAMING_SNAKE_CASE      )

  a__												= vocab_size
  a__												= hidden_size
  a__												= num_hidden_layers
  a__												= num_attention_heads
  a__												= intermediate_size
  a__												= hidden_act
  a__												= hidden_dropout_prob
  a__												= attention_probs_dropout_prob
  a__												= initializer_range
  a__												= layer_norm_eps

  a__												= image_size
  a__												= patch_size
  a__												= num_channels
  a__												= use_mask_token
  a__												= use_absolute_position_embeddings
  a__												= use_relative_position_bias
  a__												= use_shared_relative_position_bias
  a__												= layer_scale_init_value
  a__												= drop_path_rate
  a__												= use_mean_pooling
  # decode head attributes (semantic segmentation)
  a__												= out_indices
  a__												= pool_scales
  # auxiliary head attributes (semantic segmentation)
  a__												= use_auxiliary_head
  a__												= auxiliary_loss_weight
  a__												= auxiliary_channels
  a__												= auxiliary_num_convs
  a__												= auxiliary_concat_input
  a__												= semantic_loss_ignore_index





class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Tuple      						=					version.parse('''1.11'''				)

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Mapping[str, Mapping[int, str]]:
  return OrderedDict(
      [
          ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
      ]      )



 @property
 def 						_UpperCAmelCase       (	self      ) ->			float:
  return 1e-4










from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

a_      :      str    =     logging.get_logger(__name__)
@add_end_docstrings(_lowercase				)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  super().__init__(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  self.check_model_type(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__							,			a__												= {}, {}
  if padding is not None:
   a__												= padding
  if truncation is not None:
   a__												= truncation
  if top_k is not None:
   a__												= top_k
  return preprocess_params, {}, postprocess_params

 def __call__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if isinstance(SCREAMING_SNAKE_CASE		,				(Image.Image, str)      ) and isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   a__												= {'''image''': image, '''question''': question}
  else:
   a__												= image
  a__												= super().__call__(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  return results

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=False      ) ->			Dict:
  a__												= load_image(inputs['''image''']      )
  a__												= self.tokenizer(
      inputs['''question''']		,				return_tensors=self.framework		,				padding=SCREAMING_SNAKE_CASE		,				truncation=SCREAMING_SNAKE_CASE      )
  a__												= self.image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors=self.framework      )
  model_inputs.update(SCREAMING_SNAKE_CASE      )
  return model_inputs

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= self.model(**SCREAMING_SNAKE_CASE      )
  return model_outputs



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=5      ) ->			Optional[Any]:
  if top_k > self.model.config.num_labels:
   a__												= self.model.config.num_labels

  if self.framework == "pt":
   a__												= model_outputs.logits.sigmoid()[0]
   a__							,			a__												= probs.topk(SCREAMING_SNAKE_CASE      )
  else:
   raise ValueError(f"Unsupported framework: {self.framework}"      )

  a__												= scores.tolist()
  a__												= ids.tolist()
  return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )]





