




'''simple docstring'''







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									=logging.get_logger(__name__)

A									={
    '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     _a						(			__a				):
       __a       :  List[Any]            =      """beit"""
       def __init__(			self		:			Dict     ,       lowercase		:			Optional[int]=8_192     ,       lowercase		:			Union[str, Any]=768     ,       lowercase		:			Any=12     ,       lowercase		:			int=12     ,       lowercase		:			str=3_072     ,       lowercase		:			Optional[int]="gelu"     ,       lowercase		:			int=0.0     ,       lowercase		:			int=0.0     ,       lowercase		:			Optional[int]=0.02     ,       lowercase		:			Optional[Any]=1E-12     ,       lowercase		:			Union[str, Any]=224     ,       lowercase		:			List[Any]=16     ,       lowercase		:			Optional[int]=3     ,       lowercase		:			Any=False     ,       lowercase		:			Dict=False     ,       lowercase		:			Any=False     ,       lowercase		:			Any=False     ,       lowercase		:			str=0.1     ,       lowercase		:			List[Any]=0.1     ,       lowercase		:			str=True     ,       lowercase		:			Union[str, Any]=[3, 5, 7, 11]     ,       lowercase		:			Optional[int]=[1, 2, 3, 6]     ,       lowercase		:			Union[str, Any]=True     ,       lowercase		:			Tuple=0.4     ,       lowercase		:			List[Any]=256     ,       lowercase		:			Optional[Any]=1     ,       lowercase		:			List[Any]=False     ,       lowercase		:			str=255     ,       **lowercase		:			Union[str, Any]     ,       ):



           '''simple docstring'''

           super().__init__(**lowercase       )

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

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






class     _a						(			__a				):
       __a       :  Any            =      version.parse("""1.11"""				)
       @property
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           return OrderedDict(
               [
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ]       )







       @property
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           return 1E-4










'''simple docstring'''







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									=logging.get_logger(__name__)


@add_end_docstrings(__a				)
class     _a						(			__a				):
       def __init__(			self		:			Optional[int]     ,       *lowercase		:			Any     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           super().__init__(*lowercase     ,       **lowercase       )
           self.check_model_type(lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			str=None     ,       lowercase		:			List[str]=None     ,       lowercase		:			List[str]=None     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	{}, {}
           if padding is not None:
               UpperCAmelCase     =	padding
           if truncation is not None:
               UpperCAmelCase     =	truncation
           if top_k is not None:
               UpperCAmelCase     =	top_k
           return preprocess_params, {}, postprocess_params
       def __call__(			self		:			Optional[Any]     ,       lowercase		:			Union["Image.Image", str]     ,       lowercase		:			str = None     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           if isinstance(lowercase     ,       (Image.Image, str)       ) and isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	{'''image''': image, '''question''': question}
           else:
               UpperCAmelCase     =	image
           UpperCAmelCase     =	super().__call__(lowercase     ,       **lowercase       )
           return results
       def 					A   (			self		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Any=False     ,       lowercase		:			Any=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	load_image(inputs['''image''']       )
           UpperCAmelCase     =	self.tokenizer(
               inputs['''question''']     ,       return_tensors=self.framework     ,       padding=lowercase     ,       truncation=lowercase       )
           UpperCAmelCase     =	self.image_processor(images=lowercase     ,       return_tensors=self.framework       )
           model_inputs.update(lowercase       )
           return model_inputs
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model(**lowercase       )
           return model_outputs







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]=5       ):



           '''simple docstring'''

           if top_k > self.model.config.num_labels:
               UpperCAmelCase     =	self.model.config.num_labels

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

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




