



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

SCREAMING_SNAKE_CASE_:List[Any]					=						logging.get_logger(__name__)






@add_end_docstrings(_a   )
class SCREAMING_SNAKE_CASE__					(		_a   ):




    '''simple docstring'''






    def __init__(	self,		*lowerCamelCase__,		**lowerCamelCase__		):
           super().__init__(*__lowerCAmelCase,		**__lowerCAmelCase		)
           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,		lowerCamelCase__=None		):
           A			:	Dict										=       {}
           if top_k is not None:
                  A			:	Optional[Any]										=       top_k
           return {}, {}, postprocess_params






    def __call__(	self,		lowerCamelCase__,		**lowerCamelCase__		):
           return super().__call__(__lowerCAmelCase,		**__lowerCAmelCase		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
           A			:	Union[str, Any]										=       load_image(__lowerCAmelCase		)
           A			:	Optional[int]										=       self.image_processor(images=__lowerCAmelCase,		return_tensors=self.framework		)
           return model_inputs






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
           A			:	Any										=       self.model(**__lowerCAmelCase		)
           return model_outputs






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=5		):
           if top_k > self.model.config.num_labels:
                  A			:	List[Any]										=       self.model.config.num_labels

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

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







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				"roberta"






    def __init__(	self,		lowerCamelCase__=5_0265,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

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






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Optional[Any]										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	int										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


