


'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


lowercase				=logging.get_logger(__name__)

lowercase				={
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 ="data2vec-text"






       def __init__(     self    ,				snake_case=3_0_5_2_2    ,				snake_case=7_6_8    ,				snake_case=1_2    ,				snake_case=1_2    ,				snake_case=3_0_7_2    ,				snake_case="gelu"    ,				snake_case=0.1    ,				snake_case=0.1    ,				snake_case=5_1_2    ,				snake_case=2    ,				snake_case=0.02    ,				snake_case=1E-1_2    ,				snake_case=1    ,				snake_case=0    ,				snake_case=2    ,				snake_case="absolute"    ,				snake_case=True    ,				snake_case=None    ,				**snake_case    ,				)		->    Tuple:




          '''simple docstring'''
          super().__init__(pad_token_id=snake_case    ,				bos_token_id=snake_case    ,				eos_token_id=snake_case    ,				**snake_case)

          _UpperCAmelCase  :  int				       =vocab_size
          _UpperCAmelCase  :  Optional[Any]				       =hidden_size
          _UpperCAmelCase  :  List[str]				       =num_hidden_layers
          _UpperCAmelCase  :  Optional[int]				       =num_attention_heads
          _UpperCAmelCase  :  Optional[Any]				       =hidden_act
          _UpperCAmelCase  :  int				       =intermediate_size
          _UpperCAmelCase  :  Optional[Any]				       =hidden_dropout_prob
          _UpperCAmelCase  :  int				       =attention_probs_dropout_prob
          _UpperCAmelCase  :  Optional[int]				       =max_position_embeddings
          _UpperCAmelCase  :  Any				       =type_vocab_size
          _UpperCAmelCase  :  Tuple				       =initializer_range
          _UpperCAmelCase  :  Union[str, Any]				       =layer_norm_eps
          _UpperCAmelCase  :  List[str]				       =position_embedding_type
          _UpperCAmelCase  :  List[Any]				       =use_cache
          _UpperCAmelCase  :  List[str]				       =classifier_dropout






class    __magic_name__				(						lowerCAmelCase							):






       @property
       def      lowerCAmelCase						(     self)		->    Mapping[str, Mapping[int, str]]:




          '''simple docstring'''
          if self.task == "multiple-choice":
             _UpperCAmelCase  :  int				       ={0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
             _UpperCAmelCase  :  List[str]				       ={0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])





'''simple docstring'''






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

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

lowercase				=logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase							)
class    __magic_name__				(						lowerCAmelCase							):






       def __init__(     self    ,				*snake_case    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          super().__init__(*snake_case    ,				**snake_case)
          requires_backends(self    ,				'vision')
          self.check_model_type(
              TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == 'tf'
              else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)






       def      lowerCAmelCase						(     self    ,				snake_case=None)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       ={}
          if top_k is not None:
             _UpperCAmelCase  :  Optional[int]				       =top_k
          return {}, {}, postprocess_params






       def __call__(     self    ,				snake_case    ,				**snake_case)		->    List[str]:




          '''simple docstring'''
          return super().__call__(snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =load_image(snake_case)
          _UpperCAmelCase  :  Tuple				       =self.image_processor(images=snake_case    ,				return_tensors=self.framework)
          return model_inputs






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =self.model(**snake_case)
          return model_outputs






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case=5)		->    Any:




          '''simple docstring'''
          if top_k > self.model.config.num_labels:
             _UpperCAmelCase  :  Optional[Any]				       =self.model.config.num_labels

          if self.framework == "pt":
             _UpperCAmelCase  :  List[Any]				       =model_outputs.logits.softmax(-1)[0]
             _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[Any]				       =probs.topk(snake_case)
          elif self.framework == "tf":
             _UpperCAmelCase  :  int				       =stable_softmax(model_outputs.logits    ,				axis=-1)[0]
             _UpperCAmelCase  :  List[Any]				       =tf.math.top_k(snake_case    ,				k=snake_case)
             _UpperCAmelCase       ,			_UpperCAmelCase  :  List[str]				       =topk.values.numpy(), topk.indices.numpy()
          else:
             raise ValueError(f"Unsupported framework: {self.framework}")

          _UpperCAmelCase  :  int				       =scores.tolist()
          _UpperCAmelCase  :  Tuple				       =ids.tolist()
          return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case    ,				snake_case)]

