


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

__UpperCamelCase       :       Optional[int]						       =						logging.get_logger(__name__)

@add_end_docstrings(UpperCamelCase_)
class       __magic_name__	(     UpperCamelCase_):


     def __init__(      self							:     List[str]							,	*lowerCamelCase__							:     Dict							,	**lowerCamelCase__							:     List[Any]			)  ->      Optional[Any]:





      '''simple docstring'''






      super().__init__(*__a							,	**__a			)
      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      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     str=None			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   {}
      if top_k is not None:
       UpperCamelCase__					:    str          =   top_k
      return {}, {}, postprocess_params


     def __call__(      self							:     Any							,	lowerCamelCase__							:     Any							,	**lowerCamelCase__							:     str			)  ->      str:





      '''simple docstring'''






      return super().__call__(__a							,	**__a			)


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     Tuple			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   load_image(__a			)
      UpperCamelCase__					:    Optional[int]          =   self.image_processor(images=__a							,	return_tensors=self.framework			)
      return model_inputs


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     Tuple			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   self.model(**__a			)
      return model_outputs


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     List[str]=5			)  ->      str:





      '''simple docstring'''






      if top_k > self.model.config.num_labels:
       UpperCamelCase__					:    Optional[int]          =   self.model.config.num_labels

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

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






from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      "roberta-prelayernorm"


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     List[Any]=50265							,	lowerCamelCase__							:     Optional[Any]=768							,	lowerCamelCase__							:     str=12							,	lowerCamelCase__							:     Union[str, Any]=12							,	lowerCamelCase__							:     Dict=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     List[str]=512							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Tuple=0.02							,	lowerCamelCase__							:     List[Any]=1E-1_2							,	lowerCamelCase__							:     str=1							,	lowerCamelCase__							:     int=0							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Union[str, Any]="absolute"							,	lowerCamelCase__							:     Optional[Any]=True							,	lowerCamelCase__							:     Dict=None							,	**lowerCamelCase__							:     Any							,	)  ->      Optional[int]:





      '''simple docstring'''






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

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



class       __magic_name__	(     __lowerCAmelCase):


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





      '''simple docstring'''






      if self.task == "multiple-choice":
       UpperCamelCase__					:    Dict          =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       UpperCamelCase__					:    Any          =   {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]			)


